Feature Index

A guide to JMP® software features

With JMP installed on your PC or Mac, you can run an example of an analysis by clicking on the Example Script (JSL) link. This link will download a script, which can be run in JMP software to demonstrate the feature. Navigate this index by the letter links below or use the search box.

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JMP Man
  • = Capability only available in JMP Pro.
  • NEW! = Denotes new capability in JMP 11.
Term Definition Example of how to access in JMP

3D biplot: Gabriel

A multivariate plot in principal components space, which shows both points and rays showing variables directions.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Biplot

3D scatterplot

A three-dimensional spinnable view of your data.

Example Script (JSL)

Graph > Scatterplot 3D

A

Term Definition Example of how to access in JMP

ABCD design

Screening design for mixtures.

Example Script (JSL)

DOE > Mixture Design > Choose Mixture Design Type > ABCD Design

accelerated failure-time models

Fits a regression model to the parameters of a life distribution, such as Weibull.

Example Script (JSL)

Analyze > Quality and Process > Fit Life by X > Fit All Distributions

accelerated life test (ALT) design

Used to design high stress tests; used when the time required to test the product until it fails is prohibitive.

DOE > Accelerated Life Test Design

adaptive elastic net

  NEW!

A generalized regression estimation technique that applies an adaptive L1 penalty and an L2 penalty in estimating parameters.

Example Script (JSL)

Analyze > Fit Model > Personality > Generalized Regression > Estimation Method

adaptive lasso

  NEW!

A generalized regression estimation technique that applies an adaptive L1 penalty in estimating parameters. The L1 penalty weights are determined to guarantee the oracle property (Zou, 2006).

Example Script (JSL)

Analyze > Fit Model > Personality > Generalized Regression > Estimation Method > Lasso

added-variable plot (leverage plot)

A plot such that the distance from a point to the sloped line is the residual, and the distance to the horizontal line is what the residual would be under the hypothesis.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Leverage Plot

adjusted means

The predicted value at each level of the indicated term, with other terms being set to neutral values. The Fit Model platform produces these automatically for nominal terms.

Example Script (JSL)

General

adjusted R2

A measure of degree of fit that has been adjusted to reflect the number of parameters in the model. Unlike the unadjusted R2, the adjusted R2 does not always increase as more terms are added to the model.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Summary of Fit > RSquare Adj

AIC, AICc, Akaike's 'A' Information Criterion

A measure of the goodness of fit of an estimated statistical model that can be used to compare two or more models. The model with the lowest AIC value is the best. The AICc is a modification of the AIC adjusted for small samples.

General

alias matrix

Shows the aliasing between the model terms and the terms that you specify in the Alias Terms panel. It enables you to see the confounding patterns.

Example Script (JSL)

DOE > Custom Design > Make Design > Design Evaluation > Alias Matrix

alias optimal design

This design minimizes the sum of squares of the entries in the alias matrix subject to constraining the D-efficiency of the design to be above some lower bound.

Example Script (JSL)

DOE > Custom Design > Optimality Criterion > Make Alias Optimal Design

all possible models

Runs all possible models using combinations of the regression parameters specified.

Example Script (JSL)

Analyze > Fit Model > Personality:Stepwise > All Possible Models

ALT design

Used to design high stress tests; used when the time required to test the product until it fails is prohibitive.

Example Script (JSL)

DOE > Accelerated Life Test Design

analysis of covariance - ANCOVA - same slopes

When main effects model needs adjusting for a covariate. Use Fit Model, specifying the main effect, the covariate.

Example Script (JSL)

Analyze > FIt Model > Add Effects

analysis of covariance - ANCOVA - separate slopes

The slope on a covariate is different in different groups. Use Fit Model, specifying the main effect, the covariate, and a crossed effect for main effect by covariate.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square

analysis of mean ranges

NEW!

A chart which shows how ranges vary across groups.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Range Chart

analysis of means

Compares group means to the overall mean. This method assumes that your data are approximately normally distributed.

Analyze > Fit Y by X > Oneway > Analysis of Means > ANOM

analysis of means - ANOM

Compares group means to the overall mean. This method assumes that your data are approximately normally distributed.

Analyze > Fit Y by X > Oneway > Analysis of Means Method > ANOM

analysis of means - ANOM for proportions

Compares response proportions for the X levels to the overall response proportion. Only appears if the response has exactly two levels.

Analyze > Fit Y by X > Contingency > Analysis of Means for Proportions

analysis of means - ANOM for transformed ranks (ANOM-TR)

This is the nonparametric version of the ANOM analysis. Use this method if your data are clearly non-normal and cannot be transformed to normality. Compares each group mean transformed rank to the overall mean transformed rank.

Analyze > Fit Y by X > Oneway > Analysis of Means Method > ANOM with Transformed Rank

analysis of means - ANOM for variances (ANOMV)

Tests for variance heterogeneity by comparing group standard deviations to the root mean square error.

Analyze > Fit Y by X > Oneway > Analysis of Means Method > ANOM for Variances

analysis of means - ANOM for variances with Levene (ADM)

This is the nonparametric version of the ANOM for Variances analysis. Use this method if you suspect your data are non-normal and cannot be transformed to normality. Compares the group means of the absolute deviation from the median (ADM) to the overall mean ADM.

Analyze > Fit Y by X > Oneway > Analysis of Means Method > ANOM for Variances with Levene

analysis of variance - ANOVA general (two or more factors)

For almost any linear model, use Fit Model and fill in the dialog.

Analyze > Fit Model > Personality:Standard Least Square > Analysis of Variance

analysis of variance - ANOVA one-way

Fitting means across a grouping variable, and testing if they are significantly different.

Analyze > Fit Y by X > Oneway > t Test

anticipated response

NEW!

Response values at the specified design settings calculated using the Anticipated Coefficients specified in the Power Analysis report. Found in the DOE Design report.

Example Script (JSL)

DOE > Custom Design > Make Design

AR (auto regressive) model

Group of linear prediction formulas that attempt to predict an output of a system based on the previous outputs.

Analyze > Modeling > Time Series > ARIMA Model Group > Auto Regressive

area plot

Shows a response summarized by categories.

Graph > Graph Builder > Area

area under the curve (AUC)

AUC is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.

Analyze > Fit Y by X > Logistic > ROC Curve

ARIMA

Fitting ARIMA (autoregressive integrated moving average) models for times series analysis.

Analyze > Modeling > Time Series > ARIMA

ARL

The average run length at a given quality level is the average number of samples (subgroups) taken before an action signal is given.

Analyze > Quality and Process > Control Chart > CUSUM > Show ARL (Also available through Control Chart Builder)

Arrhenius transformation

A transformation of temperature used in accelerated life testing. This transformation is supported for single-column continuous effects.

Example Script (JSL)

Analyze > Reliability and Survival > Fit Life by X > Relationship:Arrhenius Transformation

association

A relationship between two variables. Association can be tested for or visualized in many platforms in JMP.

General

Attribute Gauge R&R

Analyzing the agreement between raters of an attribute, such as accept/reject.

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Chart Type:Attribute > Gauge Studies > Gauge RR

AUC

AUC is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.

Analyze > Fit Y by X > Logistic > ROC Curve

augment design

To modify an existing design data table.

DOE > Augment Design

autocorrelation

The correlation between a value and the value next to it in a time series.

Example Script (JSL)

Analyze > Time Series > Autocorrelation

autoregression

See ARIMA. Note, autoregression models with autocorrelated and heteroscedastic errors are available in the SAS/ETS® add-in using SAS PROC Autoreg.

Analyze > Modeling > Time Series

average chart

XBar Control Chart. Shows the process mean and its variability.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Average Chart

average run length

The average run length at a given quality level is the average number of samples (subgroups) taken before an action signal is given.

Analyze > Quality and Process > Control Chart > CUSUM > Show ARL (Also available through Control Chart Builder)

B

Term Definition Example of how to access in JMP

backwards selection

Describes how an effect leaves a model; backward removes the regressor that affects the fit the least, given that term is not significant at the specified level.

Example Script (JSL)

Analyze > Fit Model > Personality:Stepwise > Stepwise Regressional Control > Direction:Backwards

bar graph (bar chart)

Bar heights across groups represent statistics.

Graph > Chart > Options: Bar Chart

bar plot

Shows a response summarized by categories.

Graph > Graph Builder > Bar

Bartlett's test

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they aren't.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Unequal Variances

Bayes Plot (Box-Meyer)

For screening designs, this helps pick out the active effects, using a Bayesian approach that views the distribution of inactive effects as being contaminated by a distribution of active effects with k-times larger variance.

Example Script (JSL)

Analyze > Fit Model > Effect Screening > Bayes Plot

Bayesian alias-optimal

This is a modification of the Alias Optimal criterion that seeks to minimize the aliasing between model effects and alias effects. At the same time, this design has the ability to detect and estimate some higher-order terms.

Example Script (JSL)

DOE > Custom Design > Optimality Criterion > Make Alias Optimal Design

Bayesian D-optimal

This type of design allows the precise estimation of all of the Necessary terms while providing omnibus detectability and some estimability of the If Possible terms.

Example Script (JSL)

DOE > Custom Design > Optimality Criterion > Make D-Optimal Design

Bayesian designs

Modifies the given optimality criterion so that the design has the ability to detect and estimate some higher order terms.

Example Script (JSL)

DOE > Custom Design > Make Design

Bayesian I-optimal

This type of design minimizes the average prediction variance over the design region and at the same time has the ability to detect and estimate some higher-order terms.

Example Script (JSL)

DOE > Custom Design > Optimality Criterion > Make I-Optimal Design

Bayesian Information Criterion (BIC)

This is a measure (that is in part, based on the likelihood function) of model fit that is helpful when comparing different models.

Example Script (JSL)

Analyze > Modeling > Time Series > Stopping Rule:Minimum BIC

Bayesian split plot

This type of design contains hard-to-change factors which only change between one whole plot and the next. In addition, this design has the ability to detect and estimate some higher order terms.

Example Script (JSL)

DOE > Custom Design > Design Generation > Number of Whole Plots

BCI

  NEW!

A measure of the impact of a component to system reliability over time. A large BCI indicates that the system is sensitive to the component.

Analyze > Reliability and Survival > Reliability Block Diagran > Show BCI

Bernoulli (trials)

Random numbers from a binomial distribution with parameters that you enter as function arguments.

Cols > Formula > Random Functions

best subsets regression

Runs all possible models using combinations of the regression parameters specified.

Example Script (JSL)

Analyze > Fit Model > Personality:Stepwise > All Possible Models

bias comparison

Analysis of Means test to determine whether the group differences are real differences or whether they are due to measurement error.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Bias Comparison

bias report

Shows a graph and summary table for each X variable. The average bias, or differences between the observed values and the standard values, is given for each level of the X variable.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Gauge Studies > Bias Report

BIC

This is a measure (that is in part, based on the likelihood function) of model fit that is helpful when comparing different models.

Analyze > Fit Model > Personality:Stepwise > Stepwise Regression Control > Stopping Rule: Minimum BIC

biplot (principal components)

Biplots are a type of exploratory graph used in statistics, a generalization of the simple two-variable scatterplot. A biplot allows information on both samples and variables of a data matrix to be displayed graphically. Samples are displayed as points while variables are displayed either as vectors, linear axes or nonlinear trajectories.

Example Script (JSL)

Analyze > Multivariate Methods > principal Components > Biplot

Birnbaum's Component Importance (BCI)

  NEW!

A measure of the impact of a component to system reliability over time. A large BCI indicates that the system is sensitive to the component.

Example Script (JSL)

Analyze > Reliability and Survival > Reliability Block Diagran > Show BCI

Bland-Altman plot

A plot for matched pairs analysis which shows the relationship between the differences versus the means of the paired observations.

Example Script (JSL)

Analyze > Matched Pairs > Plot Dif by Mean

BLUP (best linear unbiased prediction)

Output for random effects in mixed model analysis.

Analyze > Fit Model Personality: Standard Least Squares | Method: REML (Recommended) > Random Effect Predictions: BLUP

Bonferroni adjustment for multiple comparisons

Access in Fit Y by X (set alpha level). Apply the Bonferroni adjustment for multiple comparisons by dividing alpha by the number of comparisons being made.

General

boosted neural network

 

The process of building a large additive neural network by fitting a sequence of smaller models.

Example Script (JSL)

Analyze > Modeling > Neural > Boosting

boosting (boosted tree)

 

The process of building a large, additive decision tree by fitting a sequence of smaller trees.

Example Script (JSL)

Analyze > Modeling > Partition > Method:Boosted Tree

boosting (neural network)

 

The process of building a large additive neural network by fitting a sequence of smaller models.

Analyze > Modeling > Neural > Boosting

bootstrap forest (random forest technique)

 

Creates many trees and computes the final predicted value by averaging the predicted values.

Example Script (JSL)

Analyze > Modeling > Partition > Method:Bootstrap Forest

bootstrapping

 

A resampling method that allows for estimation of the standard error of a statistic. Available in most analytical platforms in JMP Pro.

Example Script (JSL)

Right click any statistic in JMP Pro report and click bootstrap.

Bowker's test of symmetry

A test of the symmetry of k-by-k tables that assumes corresponding non-diagonal elements are equal; for 2x2 tables, Bowker's Test is equivalent to McNemar's Test.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Agreement Statistics

box plot (Graph Builder)

Shows a compact view of a variable's distribution, with quartiles and outliers.

Graph > Graph Builder > Box Plot

box plot (One level)

A graph to look at a distribution, which draws a box around the middle half of the data, and lines extending until the outlying data. The Distribution platform does this across one or more variables.

Analyze > Distribution > Outlier Box Plot

box plot - groups

Side-by-side box plots comparing the distribution across groups. The Oneway platform does this across groups.

Analyze > Fit Y by X > Oneway > Display Option > Box Plots

box plot - multiple groupings

Side-by-side box plots across several grouping variables.

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Multiple Groupings > Show Box Plots

box-and-whisker plot

A box plot enclosing the inner quartiles of points with lines to the farthest point within 1.5 interquartile ranges from the quartiles.

Analyze > Distribution > Outlier Box Plot

Box-Behnken design

A response surface experimental design with points midway between vertices.

DOE > Response Surface Design > Choose a Design > Box-Behnken

Box-Cox power transformation

A power transformation, usually on the response, proportional to ylambda-1.

Analyze > Fit Model > Personality:Standard Least Square > Factor Profiling > Bo Co Y Transformation

Box-Jenkins Methods

Fitting ARIMA (autoregessive integrated moving average) models for time series analysis.

Example Script (JSL)

Analyze > Modeling > Time Series > ARIMA

Box-Meyer Bayes Plot

For screening designs, this helps pick out the active effects, using a Bayesian approach that views the distribution of inactive effects as being contaminated by a distribution of active effects with k-times larger variance.

Example Script (JSL)

Analyze > Fit Model > Effect Screening > Bayes Plot

Box-Wilson designs

An experimental design for response surface analysis involving three sets of points: Vertex points, Center points, and Axial points.

DOE > Custom Design

Brown-Forsythe test/test variances equal across groups/Oneway(Test Equal Variances)

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they are not.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Unequal Variances

bubble chart (bubble plot)

A scatterplot that draws its points as circles (bubbles). The bubbles can be sized or colored depending on other columns. A bubble plot can also be animated through time, resulting in a fifth displayed variable.

Example Script (JSL)

Graph > Bubble Plot

C

Term Definition Example of how to access in JMP

C chart

A plot showing the numbers of nonconformities in subgroup samples.

Analyze > Quality and Process > Control Chart > C

C(p)

Mallow’s C(p) is a measure to use when comparing models. Usually C(p) is plotted against p, the number of regressors.

Analyze > Fit Model > Personality: Stepwise

calibration

To obtain a prediction and (fiducial) confidence interval for an X, given the Y value and other X values.

Analyze > Fit Model > Personality:Nominal Logistic > Inverse Partition

canonical correlation (Discriminant Analysis)

Canonical Correlation in discriminant analysis is the correlation between the classification categories and the canonical scores calculated on the discriminant factors.

Example Script (JSL)

Analyze > Multivariate > Discriminant, hotspot > canonical options > show canonical details

canonical correlation (MANOVA)

Canonical correlations between the multiple response canonical scores and the model factors can be calculated.

Example Script (JSL)

Analyze > Fit Model > Personality:MANOVA For a fitted model (based on the MANOVA correlation structure you want to use), choose the hotspot for the whole model or model effect of interest and choose test details.

canonical plot (discriminant analysis)

Shows the points and multivariate means in the two dimensions that best separate the groups in linear discriminant analysis. Each row in the data is a point. The multivariate means are labeled circles. The directions of the variables in the canonical space are shown by labeled rays emanating from the grand mean.

Example Script (JSL)

Analyze > Multivariate Models > Discriminant > Canonical Plot

capability analysis

Computes capability analyses for each column and creates a goal plot with one point for each column. Capability analysis uses the standardized mean and standard deviation as the X and Y coordinates.

Example Script (JSL)

Analyze > Quality and Process > Capability

capability box plots

Shows or hides together in one graph, one box plot for each column, where the box plots are each standardized to their respective specification limits.

Example Script (JSL)

Analyze > Quality and Process > Capability > Capability Box Plots

caption box (Graph Builder)

NEW!

Shows a summary statistic value for the data.

Example Script (JSL)

Graph > Graph Builder > Caption Box

categorical analysis

Analysis for data with discrete (categorical) levels. The strength of the Categorical platform is that it can handle responses in a wide variety of formats without needing to reshape the data.

Example Script (JSL)

Analyze > Modeling > Categorical

cause and effect diagrams

A hierarchical diagram to lay out root causes. Also called Ishikawa or fishbone diagrams.

Analyze > Quality and Process > Diagram

CDF plot

Plot of the empirical cumulative distribution function. Available in the Distribution platform as well as in Fit Y by X (Oneway), when comparing distributions across groups.

Analyze > Distribution > CDF Plot

center points

Additional runs to be placed at the center of each continuous factor’s range.

Example Script (JSL)

DOE > Custom Design > Design Generation > Number of Center Points

central composite design

An experimental design for response surface analysis involving three sets of points: Vertex points, Center points, and Axial points.

DOE > Response Surface Design > Choose a Design > Central Composite Design

chi-square (chi squared) – test for association

Used to determine whether there is a significant association between the two categorical variables from a single population.

Analyze > Fit Y by X: Contingency

chi-square (chi squared) – test for independence

Used to determine whether there is a significant association between the two categorical variables from a single population.

Analyze > Fit Y by X: Contingency

ChiSquare - for a general categorical response model

The likelihood ratio ChiSquare compares the fit of a model with how good the fit would be without certain effects in the model.

Analyze > Fit Model > Personality:Nominal Logistic > Whole Model Test > ChiSquare

ChiSquare - for two-way table of frequencies

To test for independence (or marginal homogeneity) for two categorical variables using a table of counts. There are two versions: the likelihood ratio G2, and the Pearson X2.

Analyze > Fit Y by X > Contingency > Tests > ChiSquare

choice analysis

Fits choice models for market research. Conjoint Experiments.

Example Script (JSL)

Analyze > Modeling > Choice

choice design

Creates experiments with factors that are product attributes. The purpose of a choice experiment is to define a product that people want to buy.

DOE > Choice Design

classification trees

A decision tree for a categorical response variable.

Example Script (JSL)

Analyze > Modeling > Partition > Method

cluster analysis

Clustering clumps together points that are close to each other (points that have similar values). JMP provides two types of clustering, hierarchical and k-means.

Analyze > Multivariate Methods > Cluter > Hierarchical

Cochran Armitage trend test

Tests for trends in binomial proportions across levels of a single variable.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Cochran Armitage Trend Test

Cochran-Mantel-Haenszel test

Computes ChiSquare statistics for stratifications of a two-way contingency table by a third variable.

Analyze > Fit Y by X > Contingency > Cochran Mantel Haenszel

coefficient of variation

The standard deviation estimate divided by the mean, expressed as a percentage.

Example Script (JSL)

Analyze > Distribution > Customize Summary Statistics > CV

coefficient of variation (Cv)

Measure of dispersion, which is the standard deviation divided by the mean multiplied by 100.

General

collinearity

Collinearity occurs when one or more model effects in a regression model have strong correlations. This results in inflated estimates of the model parameter estimates, making it more difficult to show statistical significance for these parameter estimates. Collinearity can be seen in many ways, including: a) Collinearity shows as a shrunken X scale on the factors’ leverage plots. b) Variance Inflation Factor: See statistics index entry for Variance Inflation Factors.

Example Script (JSL)

Analyze > Fit Model&;Personality:Standard Least Square > Leverage Plots

color by variable levels (Graph Builder)

Drop variables here to color the graph. If you are using a map, the map shapes are colored. If you are using a contour plot, colored contours appear. If your graph contains points, they are colored.

Graph > Graph Builder > Color

compare data tables

Compare two data tables. Compare data, tables’ metadata, as well as columns’ metadata.

Example Script (JSL)

Tables > Compare Data Tables

comparison circles

A graphical method of comparing means, lining up circles vertically with center at means and diameter as the confidence interval, the angle of intersection indicates significance. Used in conjunction with a means comparison option.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Display Options > Comparison Circles

concatenate data tables

Concatenate to append tables end to end.

Example Script (JSL)

Tables > Concatenate

conditional logistic regression

Regression models for matched or grouped data with a binary response variable. A case-control study could be analyzed using conditional logistic regression using the Choice platform.

Example Script (JSL)

Analyze > Modeling > Choice

confidence intervals

JMP can produce confidence intervals around estimates in most reports.

General

confounding

Confounding is when terms in a model are linearly related. The DOE and Fit Model facilities both deal with this as needed.

General

confusion matrix

A matrix that tabulates the predictive ability of a model for a categorical response. Available for logistic regression in Fit Model, Partition, Neural and in JMP Pro, Model Comparison.

Example Script (JSL)

General

constellation plot

NEW!

Plot which shows cluster joins as points and observations as endpoints.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster > Options = Hierarchical > OK > Constellation Plot

constraints for DOE factors

Defines the allowable region for factors in a designed experiment.

Example Script (JSL)

DOE > Custom Design > Continue > Define Factor Constraints

contour plot

A two-dimensional plot that displays level curves of a function or a bivariate density.

Example Script (JSL)

Graph > Contour Plot

contour plot (Graph Builder)

A two-dimensional plot that displays level curves of a function or a bivariate density.

Example Script (JSL)

Graph > Graph Builder > Contour

contour profiler

A two-dimensional plot that shows level curves of one or more functions, that may also depend on other factors.

Example Script (JSL)

Graph > Contour Profiler

contrasts

Contrasts are tests that means or least squares means are different. The linear function across the means should sum to zero, and the positive and negative elements should sum to +1 and -1 respectively. In JMP, the Fit Model platform provides features for estimating and testing contrasts.

Analyze > Fit Model > Personality:Standard Least Squares > LSMeans Contrast

control chart with stages (phase)

Phases generate for each level of the specified phase variable a new σ, set of limits, zones, and resulting tests.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > Choose option > Phase (Also available through Control Chart Builder)

control charts (C)

A plot showing the numbers of nonconformities in subgroup samples.

Analyze > Quality and Process > Control Chart > C

control charts (CUSUM)

A plot showing cumulative sums of the deviations of the subgroup means from a target value.

Analyze > Quality and Process > Control Chart > CUSUM

control charts (EWMA)

A plot showing an exponentially weighted moving average chart.

Analyze > Quality and Process > Control Chart > EWMA

control charts (G)

NEW!

A control chart for rare events that plots a count of units or occurrences between rare events.

Example Script (JSL)

Analyze > Quality and Process > Control Chart Builder > Rare Event Charts > g-chart

control charts (I-MR)

An individual measurement chart and moving range chart.

Analyze > Quality and Process > Control Chart > IR > Individual Measurement/Moving Range (Also available through Control Chart Builder)

control charts (I-MR-R)

Uses both between-subgroup and within-subgroup variations to generate an individuals, moving range, and R chart.

Analyze > Quality and Process > Control Chart > XBar > R (Also available through Control Chart Builder)

control charts (I-MR-S)

Uses both between-subgroup and within-subgroup variations to generate an individuals, moving range, and S chart.

Analyze > Quality and Process > Control Chart > XBar > S (Also available through Control Chart Builder)

control charts (individual measurement)

An individual measurement chart and moving range chart.

Analyze > Quality and Process > Control Chart > IR > Individual Measurement (Also available through Control Chart Builder)

control charts (IR)

An individual measurement chart and moving range chart.

Analyze > Quality and Process > Control Chart > IR (Also available through Control Chart Builder)

control charts (Levey Jennings)

Chart that shows a process mean with control limits based on a long-term σ. The control limits are placed at 3*σ distance from the center line.

Analyze > Quality and Process > Control Chart > Levey Jennings (Also available through Control Chart Builder)

control charts (MR)

Control chart that displays the moving ranges of two or more successive measurements.

Analyze > Quality and Process > Control Chart > Right Click:Limits > Moving Range

control charts (multivariate)

Creates a chart to view summaries for monitoring problems where several related variables are of interest.

Analyze > Quality and Process > Multivariate Control Chart

control charts (NP)

A plot showing the numbers of nonconforming items in subgroup samples.

Analyze > Quality and Process > Control Chart > NP

control charts (P)

A plot showing the proportions of nonconforming items in subgroup samples.

Analyze > Quality and Process > Control Chart > P

control charts (presummarized)

A plot showing presummarized sample means.

Analyze > Quality and Process > Control Chart > Presummarize (Also available through Control Chart Builder)

control charts (rare events)

NEW!

Control charts used to determine whether rare events are occurring more frequently. G-charts plot the number of events between rare events, and T-charts plot the time between rare events.

Example Script (JSL)

Analyze > Quality and Process > Control Chart Builder > Rare Event Charts

control charts (run)

A plot showing a run of the samples.

Analyze > Quality and Process > Control Chart > Run Chart (Also available through Control Chart Builder)

control charts (S)

Control chart that displays the subgroup standard deviations.

Analyze > Quality and Process > Control Chart > XBar > S (Also available through Control Chart Builder)

control charts (T)

NEW!

A control chart for rare events to determine whether rare events are occurring more frequently than expected by graphing time between events.

Example Script (JSL)

Analyze > Quality and Process > Control Chart Builder > Rare Event Charts > t-chart

control charts (U)

A plot showing the numbers of nonconformities per inspection unit in subgroup samples.

Analyze > Quality and Process > Control Chart > U

control charts (UWMA)

A plot showing a uniformly weighted moving average chart.

Analyze > Quality and Process > Control Chart > UWMA

control charts (XBar/R)

A plot showing a sequence of samples to detect an out-of-control situation in statistical process control.

Analyze > Quality and Process > Control Chart > XBar > XBar/R (Also available through Control Chart Builder)

control charts (XBar/S)

A plot showing a sequence of samples to detect an out-of-control situation in statistical process control.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > XBar > XBar/S (Also available through Control Chart Builder)

Cook’s D

A regression diagnostic used to identify outliers and influential observations.

Analyze > Fit Model > Personality:Standard Least Squares > Save Columns > Cook’s D Influence

corrected sum of squares

Determines the total amount of variation that occurs with the individual observations of Y about the mean estimate of Y.

Example Script (JSL)

General

correlation - for many variables

Correlation measures how linearly related two variables are, ranging from -1 to 1. For many variables, the matrix of correlations is the gateway to many multivariate techniques.

Analyze > Multivariate Merthods > Multivariate

correlation - two variables

Correlation measures how linearly related two variables are, ranging from -1 to 1.

Analyze > Fit Y by X > Bivariate

correspondence analysis

A plot using 2-way frequency counts, showing relationship between levels of nominal/ordinal variables.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Correspondence Analysis

Cotter designs/DOE(Screening(Cotter))

A vary-one-factor-at-a-time design in which sums of even and odd effects are estimable. The DOE screening facility can construct these if they have not been disabled by preference setting.

DOE > Screening Design > Deselect Suppress Cotter Design

counts; see frequency counts

Distribution or Contingency.

Analyze > Distribution

covariance

Measure of how two random variables change together.

Example Script (JSL)

Analyze > Multivariate Method > Multivariate > Covariance Matrix

Cox Proportional Hazards Model

A semi-parametric model to fit survival times.

Analyze > Reliability and Survival > Fit Proportional Hazard

CP

Estimates what the process is capable of producing if the process mean were to be centered between the specification limits.

Analyze > Distribution > Capability Analysis > Long Term Sigma > CP

CPK

Estimates what the process is capable of producing, considering that the process mean might not be centered between the specification limits.

Analyze > Distribution > Capability Analysis > Long Term Sigma > CPK

CPL

Estimates process capability for specifications that consist of a lower limit only.

Analyze > Distribution > Capability Analysis > Long Term Sigma > CPL

CPM

Estimates process capability around a target.

Analyze > Distribution > Capability Analysis > CPM

CPU

Estimates process capability for specifications that consist of an upper limit only.

Analyze > Distribution > Capability Analysis > CPU

Cramer-von Mises

See Reliability Growth documentation.

Reliability and Survival > Reliability Growth

Cronbach’s a

A measure of item reliability on how consistently a set of variables measures overall response.

Analyze > Mulivariate Methods > Multivariate > Item Reliability > Cronbach’s a

cross correlation

The linear relationship between two time series variables.

Example Script (JSL)

Analyze > Modeling > Time Series > Cross Correlation

cross validation

 

The process of using parts of a data set to estimate model parameters, as well as to assess the predictive ability of the fitted model. Many modeling platforms in JMP Pro support a validation column role.

Example Script (JSL)

General

cross validation

Many modeling platforms in JMP support k-fold or leave-one-out cross validation. See k-fold cross validation.

General

cross-tabulation

Frequency counts arranged in a table. JMP does this for 2 variables only.

Tables > Tabulate > Add Columns by Categories

Crow AMSAA model

Model that allows for tracking reliability of a process during development testing to determine whether changes to the process are improving process reliability over time.

Example Script (JSL)

Analyze > Reliability and Survival > Reliability Growth > Fit Model > Crow AMSAA

cube plot

A cube with predicted values shown on the vertices.

Analyze > Fit Model > Personality:Standard Least Square > Factor Profiling > Cube Plots

cumulative density function plot

Plot of the empirical cumulative distribution function.

Analyze > Distribution > CDF Plot

cumulative gains chart

  NEW!

Shows overlaid cumulative gains curves, which measure the effectiveness of a predictive model, for each level of the response.

Example Script (JSL)

Analyze > Modeling > Model Comparison > Cum Gains Curve

curvature

JMP can deal with curvature in models in many platforms. See documentation for Custom Designer, Fit Model, and Fit Y by X.

General

custom tests

Tests for customized model hypotheses.

Analyze > Fit Model > Personality:Standard Least Square > Estimates > Custom Test

CUSUM chart

A plot showing cumulative sums of the deviations of the subgroup means from a target value.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > CUSUM

CV

The standard deviation estimate divided by the mean, expressed as a percentage.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > CV

D

Term Definition Example of how to access in JMP

D-Optimal design

An experimental design that minimizes the determinant of the variance matrix of the regression parameters. The DOE platform does this under Custom Design.

DOE > Custom Design > Optimality Criterion > Make D-Optimal Design

Daniel plot (half-normal plot)

Plots the absolute values of the estimates against the normal quantiles for the absolute value normal distribution.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Effect Screening > Normal Plot > Half Normal Plot

data mining

General term to refer to extracting patterns from large sets of data.

General

databases (connection to)

You can import data from a database if you have an ODBC (Open Database Connectivity) driver for the database.

File > Database > Open Table

decision Trees

Recursively partition the data to predict a response. Classification and regression trees.

Analyze > Modeling > Partition > Method:Decision Tree

definitive screening design

NEW!

Three-level designs where: main effects are independent of two-factor interactions; quadratic effects are estimable; second-order effects are only partially aliased.

Example Script (JSL)

DOE > Definitive Screening Design

degradation analysis

Analyzes product degradation (or deterioration) over time and anticipates product quality in the future.

Example Script (JSL)

Analyze > Reliability and Survival > Degradation

demonstration test plans

Let you design a test to compare the reliability of a new product to a standard.

DOE > Sample Size and Power > Reliability Demonstration

dendrogram

A tree diagram used to illustrate the arrangement of clusters produced by hierarchical clustering.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster > Options:Hierarchical

density ellipse (Graph Builder)

Shows a bivariate normal density ellipse.

Graph > Graph Builder > Ellipse

density ellipses

Draws an ellipse that contains a specified mass of points; the number of points is determined by a specified probability.

Example Script (JSL)

Analyze > Fit Y by X > Bivariate > Density Ellipses

design of experiments

The issues for planning experimental runs efficiently so that the responses can be fit to answer the questions of interest.

DOE

desirability profiling (Optimization)

A technique of setting up desirability functions, and searching for factor values that optimize a composite desirability of a number of responses.

Many modeling platforms such as Neural, Fit Model and Partition can add desirability functions to their profilers for optimization.

diagram

Cause and effect diagrams. Also called Ishikawa or fishbone diagrams. A hierarchical diagram to lay out root causes.

Analyze > Quality and Process > Diagram

Dickey-Fuller tests

Diagnostic tests for stationarity performed in the Time Series platform.

Example Script (JSL)

Analyze > Modeling > Time Series > ADF

disallowed combinations

Disallows any combination of levels of categorical factors in Design of Experiments.

Example Script (JSL)

DOE > Custom Design > Disallowed Combinations

discrete choice analysis

Fits choice models for market research. Conjoint Experiments.

Example Script (JSL)

Analyse > Modeling > Choice

discrete choice design

Creates experiments with factors that are product attributes. The purpose of a choice experiment is to define a product that people want to buy.

DOE > Choice Design

discriminant analysis

Classifying points to groups according to which group means that the column values are closest to.

Analyze > Multivariate Methods > Discriminant

discrimination ratio

Compares the total variance of the measurement with the variance of the measurement error.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Gauge Studies > Dicrimination Ratio

distance matrix

Matrix containing the distances between the observations.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster > Options:Hierarchical > Save Distance Matrix

distribution

Use the Distribution platform to describe the shape, centering and spread of variables with graphical displays and summary statistics. Explore and fit the underlying distribution using Distribution or Life Distribution.

Analyze > Distribution or Analyze > Reliability and Survival > Life Distribution

Duane plot

NEW!

Plot in reliability growth analysis that fits a line to the points (log10(X), log10(Y)), where Y is the estimated cumulative mean time between failures (MTBF) and X is the time to event variable.

Example Script (JSL)

Analyze > Reliability and Survival > Reliability Growth > OK

Dunn All Pairs for Joint Ranks

Performs a comparison of each pair, similar to the Steel-Dwass All Pairs option. The Dunn method is different in that it computes ranks on all of the data, not just the pair being compared. The Dunn method calculates p-values using the Bonferroni method.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Dunn All Pairs for Joint Ranks

Dunn with Control for Joint Ranks

Compares each level to a control level, similar to the Steel With Control option. The Dunn method is different in that it computes ranks on all of the data, not just the pair being compared. The Dunn method calculates p-values using the Bonferroni method.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Dunn with Control for Joint Ranks

Dunnett’s test for treatments vs. control

Test that group means are different from mean of a control in a one-way ANOVA. The test controls the significance level for multiple comparisons. See ‘multiple comparisons’.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Compare Means > With Control, Dunnett’s

Durbin-Watson test

In regression, a test that the residuals are autocorrelated. In JMP, an exact significant level is available.

Analyze > Fit Model > Personality:Standard Least Squares > Row Diagnostics > Durbin Watson Test

E

Term Definition Example of how to access in JMP

effective resolution

Determines the proper number of digits to record for a measurement.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Effective Resolution

effectiveness report

The ratio of the number of correct decisions to the total number of opportunities for a decision.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Chart Type:Attribute > Effectiveness Report

elastic net

  NEW!

A generalized regression estimation method that applies both an L1 and an L2 penalty in estimating parameters.

Example Script (JSL)

Analyze > Fit Model > Personality > Generalized Regression

ellipses - bivariate density

The contours of the bivariate normal distribution are ellipses.

Example Script (JSL)

Analyze > Fit Y by X > Bivariate > Density Ellipse

EMP Gauge R&R

Gauge R&R based on the EMP method (Evaluating the Measurement Process) by Don Wheeler.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > EMP Gauge RR Results

EMP study

Evaluating the Measurement Process.

Analyze > Quality and Process > Measurement Systems Analysis > EMP Results

empirical CDF plot

Plot of the empirical cumulative distribution function.

Analyze > Distribution > CDF Plot

empirical cumulative distribution function plot

Plot of the empirical cumulative distribution function.

Analyze > Distribution > CDF Plot

entropy R2

Measure of fit that compares the log-likelihoods from the fitted model and the constant probability model.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistics > Whole Model Test > Entropy R2

equal variance test

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they are not.

Analyze > Fit Y by X > Oneway > Unequal Variances

error

Pure error, within error, lack of fit error, residual error, mean square error.

General

error variance heterogeneity

Tests for variance heterogeneity by comparing group standard deviations to the root mean square error.

Analyze > Fit Y by X > Oneway > Unequal Variances

escape rate

The probability that a non-conforming part will be produced and not detected.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Chart Type:Attribute > Conformance Report > Calculate Escape Rate

estimation efficiency

NEW!

A report that gives the fractional increase in confidence interval length and relative standard error of parameters for each model effect in a designed experiment.

Example Script (JSL)

DOE General Design Evaluation

evaluate design

Evaluates existing designs (from JMP and other software products).

DOE > Evaluate Design

exact agreement statistic

 

Performs an exact test for testing agreement between variables. This is an exact test for the Kappa statistic. This is available only when the two variables have the same levels.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Exact Test > Exact Agreement Statistic

exact Cochran Armitage trend test

 

Performs the exact version of the Cochran Armitage Trend Test. This test is available only when one of the variables has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Exact Test > Exact Cochran Armitage Trend Test

exact Kolmogorov-Smirnov test

 

Performs the exact version of the Kolmogorov-Smirnov test. This option is available only when the X factor has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Exact Test > Kolmogorov Smirnov Exact Test

exact van der Waerden test

 

Performs the exact version of the van der Waerden test. This option is available only when the X factor has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Exact Test > Van Der Waerden Exact Test

exact Wilcoxon rank sum test

 

Performs exact versions of the Wilcoxon test. This option is available only when the X factor has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Nonparametric&Exact Test > Wilcoxon Exact Test

exact Wilcoxon test

 

Performs exact versions of the Wilcoxon test. This option is available only when the X factor has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Exact Test > Wilcoxon Exact Test

Excel (integration)

Transfer data from an Excel worksheet into a JMP table or launch basic JMP analysis platforms. Visualize and explore Excel models in JMP using the Prediction Profiler (Windows Only).

General

experimental design

The issues for planning experimental runs efficiently so that the responses can be fit to answer the questions of interest.

DOE

exponential distribution

A family of continuous probability distributions describing the time between Poisson events.

General

exponential plot - Survival

A plot of -log(Survival) by time for an estimated survival curve. If the survival distribution is exponential, the curve tends to be a straight line.

Analyze > Reliability and Survival > Survival > Exponential Plot

exponential smoothing

Fitting a moving average process for forecasting a time series.

Analyze > Modeling > Time Series > Smoothing Model > Exponential Smoothing

Exponentially weighted moving average (EWMA) chart

A plot showing an exponentially weighted moving average chart.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > EWMA

extreme vertices design

For mixture experiments, this design is based on taking corners of the constrained factor space.

DOE > Mixture Design > Choose Mixture Design Type > Extreme Vertices

F

Term Definition Example of how to access in JMP

F test

A test, most commonly used in ANOVA and regression, in which the test statistic follows the F distribution under the null hypothesis. The test statistic, the F ratio, is the ratio of two scaled sums of squares.

General

factor analysis

Finding directions among a large set of variables that seem to simplify the structure of the variables into a small number of ‘factors’. Usually the factor solution is rotated to be more interpretable. JMP implements three types of factor analysis: principal component analysis, non-iterated principal factor analysis with SMC, and maximum likelihood factor analysis.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Factor Analysis

factor loading plot

NEW!

A scatterplot matrix where each cell is a plot of the loadings for each variable on a pair of factors.

Example Script (JSL)

Analyze > Consumer Research > Factor Analysis > OK > Rotation Matrix

factorial designs

An experimental design in which all possible combinations of factor levels occur.

DOE > Full Factorial Design

factorial models

A factorial model is one in which all possible interactions are specified. In the JMP Model dialog, there is a menu item to make factorial effect sets.

Analyze > Fit Model > Personality:Nominal Logistic

failure time

Failure times can be modeled in the Survival and Fit Model platforms.

Analyze > Reliability and Survival > Survival > Plot Failure instead of Survival

false discovery rate (FDR)

NEW!

The expected proportion of Type I errors, or false discoveries, when conducting multiple hypothesis tests. Used to control error rate in multiple testing.

Example Script (JSL)

Analyze > Modeling > Response Screening > Select X and Y > OK

fast flexible filling design

NEW!

A design whose points are quasi-uniformly distributed throughout the design space. Useful when the design region is not rectangular.

Example Script (JSL)

DOE > Space Filling Design > Set factor level values to 0 and 1 > Continue > Specificy a sample size > Linear Constraint > Fast Flexible Filling > Make Table

Fast Ward

Modification of Ward’s method that is more efficient for large numbers of rows.

Analyze > Multivariate Methods > Cluster > Options:Hierarchical, Fast Ward

FDR

NEW!

The expected proportion of Type I errors, or false discoveries, when conducting multiple hypothesis tests. Used to control error rate in multiple testing.

Analyze > Modeling > Response Screening > Select X and Y > OK

final communality estimate

NEW!

In factor analysis, the sum of the squared loadings for a variable. Estimates the proportion of the variance of that variable that is explained by the common factors.

Example Script (JSL)

Analyze > Consumer Research > Factor Analysis > OK > Final Community Estimates

fishbone diagrams

A hierarchical diagram to lay out root causes. Also called Ishikawa or cause and effect diagrams.

Analyze > Quality and Process > Diagram

Fisher’s Exact Test (2x2)

For 2-by-2 tables only, the Contingency platform performs a Fisher’s Exact Test as well as a traditional ChiSquare.

Analyze > Fit Y by X > Contingency

Fisher’s Exact Test (mxn)

 

JMP Pro can perform a Fisher’s exact test on an m x n contingency table.

Analyze > Fit Y by X > Contingency > Exact Test > Fisher’s Exact Test

fitting lines or polynomial

Using the Fit Line command, you can add straight line fits to your scatterplot using least squares regression. Using the Fit Polynomial command, you can fit polynomial curves of a certain degree using least squares regression. Available in Fit Y by X as well as Graph Builder.

General

forecasting

Predicting future values based on fitting a time series model to data.

Analyze > Modeling > Time Series > Forecast Periods

formula (Graph Builder)

Shows a function defined by a column formula.

Graph > Graph Builder > Formula

forward selection

Describes how an effect enters a model; forward brings in the regressor that most improves the fit, given that term is significant at the level specified.

Example Script (JSL)

Analyze > Fit Model > Personality:Stepwise > Direction:Forward

forward selection (forward stepwise regression)

In stepwise regression, forward brings in the regressor that most improves the fit, given that term is significant at the level specified by Prob to Enter.

Analyze > Fit Model: Stepwise > Direction: Forward

fraction of design space (FDS) plot

A way to see how much of the model prediction variance lies above (or below) a given value.

DOE > Custom Design > Design Evaluation > Fraction of Design Space Plot

fractional factorial design (2 and 3 level)

An experimental design that is a factorial in n-k of the n factors, with the remaining factors set to interactions of the first set. Used for screening designs.

DOE > Custom Design > Design Evaluation > Fraction of Design Space Plot

freq (Graph Builder)

Drop a variable here to use it as a frequency or weight for graph elements that use statistics, such as mean or counts.

Graph > Graph Builder > Freq

frequency counts - 2-way cross-tabulation

Contingency Platform.

Analyze > Fit Y by X > Contingency > Contingency Table

frequency counts - general

Summary Command.

Analyze > Distribution > Freq

frequency counts - one-way classification

Distribution Platform.

Analyze > Distribution > Freq

frequency table, frequency distribution

The Distribution platform shows the univariate frequency distribution for one or more categorical variables, showing the number of times each value occurs. Use Fit Y by X for the joint frequency distribution for two categorical variables.

Analyze > Distribution, Analyze > Fit Y by X: Contingency

Friedman’s test for non-parametric repeated measures ANOVA

Not supported directly in JMP but can be determined by calculating the ranks within each block and then doing a two way ANOVA on ranks using Fit Y by X (treating one of the effects as a blocking variable).

Analyze > Fit Y by X

full factorial design

Design that contains all combinations of the levels of the factors.

DOE > Full Factorial Design

G

Term Definition Example of how to access in JMP

G chart

NEW!

A control chart for rare events that plots a count of units or occurrences between rare events.

Analyze > Quality and Process > Control Chart Builder > Control Chart Types

G2

NEW!

For a node in a partition analysis of a categorical response, G² is 2 times the entropy. Candidate G² is the change in entropy if the next split occurred on that candidate.

Analyze > Modeling > Partition

Gabriel biplot

A multivariate plot in principal components space, showing variable directions for both points and rays.

Example Script (JSL)

Graph > Scatterplot 3D > Biplot Rays

Gamma (measure of association)

A nonparametric measure of ordinal association that uses the counts of concordant and discordant pairs. A pair is concordant if an observation with a larger value of X also has a larger value of Y. A pair is discordant if an observation with a larger value of X has a smaller value of Y. Only appropriate when both variables are ordinal.

Analyze > Fit Y by X > Contingency > Measures of Association > Gamma

GASP models

Models a surface by interpolating across a set of data points with respect to a distance-covariance specification, with each coordinate distance component parameterized by a different value.

Example Script (JSL)

Analyze > Modeling > Gaussian Process

gauge chart

Chart that is used to study how a measurement process varies across gauges.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart

Gauge R&R

For analyzing measurement systems, the variation is characterized as due to measurements (repeatability) or operators (reproducibility). Use the Variability Chart platform.

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Gauge Studies > Gauge RR

Gaussian process

Models a surface by interpolating across a set of data points with respect to a distance-covariance specification, with each coordinate distance component parameterized by a different value.

Example Script (JSL)

Analyze > Modeling > Gaussian Process

Gaussian process I-optimal design

Design that minimizes the integrated mean squared error of the Gaussian process model over the experimental region.

Example Script (JSL)

DOE > Space Filling Design > Space Filling Design Methods > Gaussian Process IMSE Optimal

Gaussian process IMSE design

Design that minimizes the integrated mean squared error of the Gaussian process model over the experimental region.

Example Script (JSL)

DOE > Space Filling Design > Space Filling Design Methods > Gaussian Process IMSE Optimal

Generalized Linear Models or GLIM

An advanced technique for modeling non-normally distributed responses. JMP’s Fit Model platform supports Normal, Poisson, and binomial distributions in its Generalized Linear Model Personality.

Analyzed > Fit Model > Personality:Generalized Linear Model

generalized R2

Generalization of the R2 measure of fit that simplifies to the regular R2 measure for continuous normal responses. It is similar to the entropy R2 measure, but instead of using the log-likelihood, it uses the 2/n root of the likelihood.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistic > Whole Model Test > Generalized RSquare

generalized regression

  NEW!

A collection of techniques that fit models using shrinkage techniques. The method accommodates a variety of response distributions. Two methods, the lasso and elastic net, perform variable reduction as part of the fitting procedure. Useful in fitting correlated and high-dimensional data.

Example Script (JSL)

Analyze > Fit Model > Generalized Regression Models

geometric mean

Indicates the central tendency or typical value of a set of numbers. The numbers are multiplied and then the nth root of the resulting product is taken.

Example Script (JSL)

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Geometric Mean

goal plot

A goal plot shows the mean and standard deviation of each variable on the X and Y axes, as measured with respect to its specification limits.

Analyze > Quality and Process > Capability > Goal Plot

goodness of fit

Tests the hypothesis that the data comes from a particular distribution.

Analyze > Distribution > Summary Statistics > Show All Modes > Fitted Generalized Logarithm > Goodness of Fit

Greenhouse-Geisser

An adjustment to the degrees of freedom to adjust for compound symmetry violations in fitting a multivariate model with univariate calculations.

Analyze > Fit Model > Personality:MANOVA > Univariate Tests Also > Choose Response:Repeated Measures > Within Subjects > Univar G-G

group X (Graph Builder)

Subsets or partitions the data based on the variable or variables that you select. Displays the variable horizontally. Once a variable is placed here, no variable can be placed in Wrap.

Graph > Graph Builder > Group X

group Y (Graph Builder)

Subsets or partitions the data based on the variable or variables that you select. Displays the variable vertically.

Graph > Graph Builder > Group Y

groups (grouping variables)

Subset or partition the data based on a grouping variable.

General

H

Term Definition Example of how to access in JMP

half-normal plot

Plots the absolute values of the estimates against the normal quantiles for the absolute value normal distribution.

Analyze > Fit Model > Personality:Standard Least Square > Effect Screening > Normal Plot > Half Normal Plot

hazard profiler

  NEW!

Plots the hazard rate (or instantaneous failure rate) over time.

Example Script (JSL)

Analyze > Reliability and Survival Methods > Reliability Block Diagrams > Show Hazard Profiler

heatmap matrix (Graph Builder)

Shows counts using color for X and Y categories.

Graph > Graph Builder > Heatmap

heteroschedasticity by groups

Testing that the variances are different in different groups.

Analyze > Fit Y by X > Oneway > Fit Line > Plot Residuals?

hierarchical clustering

A clustering technique that starts out with each point being its own cluster, then at each step combining the clusters that are closest to each other.

Analyze > Multivariate Methods > Cluster > Options:Hierarchical

histogram

A bar graph in which the height of the bars represent frequency counts. Histograms help visualize the density of a distribution.

Analyze > Distribution > Histogram Options > Histogram

histogram (Graph Builder)

Shows a variable’s distribution using binning. If you specify the same variable for X and Y, then the Y role is ignored and a single histogram appears.

Example Script (JSL)

Graph > Graph Builder > Histogram

historical mean

Mean gathered from a previous process that can be used in computing Gauge R&R summaries.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Chart Type:Variability > Gauge Studies > Gauge RR > Historical Mean

Hoeffding’s D

A nonparametric measure of association.

Analyze > Multivariate Methods > Multivariate > Nonparametric Correlations > Hoeffding’s D

homogeneity of variances, across groups

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they are not.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Unequal Variances

Hotelling’s T2

A measure of multivariate distance that takes into account the variances and covariances. Equal to the squared Mahalanobis distance.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Outlier Analysis > T2

Hotelling-Lawley Trace

Four multivariate tests are supported in the MANOVA personality of the Fit Model platform. Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’s Maximum Root Criterion.

Analyze > Fit Model > Personality:MANOVA > Choose Response:Identity > Whole Model > Hotelling-Lawley

Hsu’s MCB test

Test that a level has the highest or lowest mean in a one-way ANOVA. See ‘multiple comparisons’.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Compare Means > With Best, Hsu MCB

Huynh-Feldt

An adjustment to the degrees of freedom to adjust for compound symmetry violations in fitting a multivariate model with univariate calculations.

Analyze > Fit Model > Personality:MANOVA > Univariate Tests Also > Choose Response:Repeated Measures > Within Subjects > Univar H-F

hypergeometric

The hypergeometric distribution models the total number of successes in a fixed sample drawn without replacement from a finite population.

Cols > Formula > Hypergeometric

hypothesis testing

Hypothesis tests are used to answer the question: Assuming the null hypothesis is true, what is the probability of observing a test statistic that is at least as extreme as the value that was observed?

General

hypothesis testing for one sample means

A test that the mean is some hypothesized value.

Analyze > Distribution > Test Mean

hypothesis testing for one sample two level proportions

Test if proportions are different than hypothesized values. In the two-sided case, this test is a chi-square test; in either of the one-sided cases, this test is an exact one-sided binomial test.

Analyze > Distribution > Test Mean

I

Term Definition Example of how to access in JMP

I-MR chart

An individual measurement chart and moving range chart.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > IR > Individual Measurement/Moving Range (Also available through Control Chart Builder)

I-MR-R chart

Uses both between-subgroup and within-subgroup variations to generate an individuals, moving range, and R chart.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > XBar > R (Also available through Control Chart Builder)

I-MR-S chart

Uses both between-subgroup and within-subgroup variations to generate an individuals, moving range, and S chart.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > XBar > S

I-optimal design

Design that minimizes the average variance of prediction over the region of the data.

Example Script (JSL)

DOE > Custom Design > Optimality Criterion > Make I-Optimal Design

ICC (intraclass correlation)

Indicates the proportion of the total variation that you can attribute to the part.

Analyze > Quality and Process > Measurement Systems Analysis > EMP Results > Intraclass Correlation

impute missing data

  NEW!

Imputes data in a partial least squares analysis using either an iterative EM-algorithm or the average value for that variable. The EM method produces a Missing Value Imputation report.

Example Script (JSL)

Analyze > Multivariate Methods > Partial Least Squares > Impute Missing Data

independence

Independence of two events means that the occurrence of one event does not make the other event more or less likely to occur.

General

indicator variables

The indicator variable uses the value of 1 for the chosen category or level profile row and 0 elsewhere.

General

individual measurement chart

An individual measurement chart and moving range chart.

Analyze > Quality and Process > Control Chart > IR > Individual Measurement (Also available through Control Chart Builder)

informative missing

  NEW!

Method for handling missing data that adds an indicator variable for missing continuous values into the model and uses the mean for the missing values. For categorical columns, the missing value is treated as a valid (non-missing) level of the variable.

Example Script (JSL)

Analyze > Fit Model > Model Specification

interaction plots - profile plots

How the response varies differently over one factor depending on levels of another factor.

Analyze > Fit Model > Personality:Standard Least Square > Profilers > Profiler > Interaction Profiler

interactions

Cross-product or interaction terms. See Fit Model documentation.

General

intercept

Point where a line crosses an axis.

General

interquartile range

The difference between the first and third quartiles. Displayed in a box plot. Box plots are also available in Distribution and Graph Builder

Analyze > Fit Y by X: Oneway > Quantiles

inverse prediction

Predicting which x value led to a particular y value, given other values.

Analyze > Fit Y by X > Inverse Prediction

IQR (interquartile range)

Difference between the first and 3rd quartiles.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Interquartile Range

IR chart

An individual measurement chart and moving range chart.

Analyze > Quality and Process > Control Chart > IR (Also available through Control Chart Builder)

Ishikawa diagrams

A hierarchical diagram to lay out root causes. Also called fishbone or cause and effect diagrams.

Analyze > Quality and Process > Diagram

item analysis

Using Item Response Theory (IRT), jointly models ability and probability of answering each question correctly from a data set of correct and incorrect question responses.

Analyze > Multivariate Methods > Item Analysis

item analysis - Cronbach’s a

A measure of item reliability on how consistently a set of variables measures overall response.

Analyze > Multivariate Methods > Multivariate > Item Reliability > Cronbach’s a

item response theory

Item Response Theory (IRT) jointly models ability and probability of answering each question correctly from a data set of correct and incorrect question responses.

Analyze > Multivariate Methods > Item Analysis

iteratively reweighted least squares

A technique where the weights depend on the estimates, and are thus done iteratively. Only the Nonlinear platform supports recalculated weights.

Analyze > Modeling > Nonlinear

J

Term Definition Example of how to access in JMP

jackknife values (outlier analysis)

Values obtained using the jackknife method, which is a resampling method that estimates sampling variance by sequentially running an analysis with m observations deleted. The delete-1 jackknife is used in JMP.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Outlier Analysis > Jackknife Distances

join data tables

Join tables side by side, or match values from one table in the other.

Tables > Join

K

Term Definition Example of how to access in JMP

K means cluster

The k-means approach to clustering performs an iterative alternating fitting process to form the number of specified clusters.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster > Options:KMeans

K out of N node

  NEW!

A K-out-of-N node in a reliability block diagram requires that K of the N paths leading into it are functional in order for the system to function.

Example Script (JSL)

Analyze > Realiability and Survival Methods > Reliability Block Diagram > Weibull

k-fold cross validation

Divides the original data into K subsets. In turn, each of the K sets is used to validate the model fit on the rest of the data, fitting a total of K models.

Example Script (JSL)

General

Kaplan-Meier Survival Estimates

A step-function estimate for the univariate survival distribution function. Also called Product limit estimates.

Analyze > Reliability and Survival > Survival > Plot Options > Show Kaplan Meier

kappa statistic

measures the degree of agreement of two similarly valued categorical variables on a scale up to 1. JMP does this automatically in the Contingency platform when the two variables have the same set of values.

Analyze > Fit Y by X > Contingency > Exact Test > Exact Agreement Statistic > Kappa Coefficient

Kendall’s t

A nonparametric measure of association. It is based on counting concordances and discordances on comparisons of pairs of rows on pairs of columns.

Analyze > Multivariate Methods > Multivariate > Nonparametric Correlations > Kendall’s t

Kendall’s Tau-B

A nonparametric measure of ordinal association that uses the counts of concordant and discordant pairs. A pair is concordant if an observation with a larger value of X also has a larger value of Y. A pair is discordant if an observation with a larger value of X has a smaller value of Y. Only appropriate when both variables are ordinal.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Measures of Association > Kendall’s Tau-B

knot node

  NEW!

A knot node in a reliability block diagram allows you to configure a K-out-of-N block shape for shapes having different distribution property settings.

Example Script (JSL)

Analyze > Realiability and Survival Methods > Reliability Block Diagram > Weibull

Kolmogorov-Smirnov test

Uses the empirical distribution function to test whether the distribution of the response is the same across groups.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Kolmogorov Smirnov Test

Kolmogorov-Smirnov-Lilliefors test

A test that a distribution is normally distributed. In the Distribution platform, when you fit a Normal, this test is used if n>2000. Otherwise a more powerful test is used, the Shapiro-Wilk test.

Analyze > Distribution > Continuous Fit > Normal > Goodness of Fit > Kolmogorov-Smirnov-Lilliefors Test

kriging

Models a surface by interpolating across a set of data points with respect to a distance-covariance specification, with each coordinate distance component parameterized by a different value.

Analyze > Modeling > Gaussian Process

Kruskal-Wallis test

A test that compares several distributions by ranking the data and comparing the ranks from each group.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test

kurtosis

The statistic that measures the 4th moment.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Kurtosis

L

Term Definition Example of how to access in JMP

L18 L36 designs

Orthogonal Designs.

DOE > Custom Design

lack of fit

The difference between the total error from the fitted model and pure error is called Lack-of-Fit error. It represents all the terms that might have been added to the model, but were not.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistic > Lack of Fit

lack of fit

Test to compare error against pure error due to exact replicates.

Analyze > Fit Model > Personality:Standard Least Square > Regression Reports > Lack of Fit

lagged variables

A lagged variable is a variable that takes on its past values.

Analyze > Modeling > Time Series

lasso

  NEW!

A generalized regression estimation method that applies an L1 penalty in estimating parameters.

Analyze > Fit Model > Personality > Generalized Regression > Estimation Method

Latin Hypercube

A design that tries to fill space such that it is distributed evenly along each factor.

Example Script (JSL)

DOE > Space Filling Design > Space Filling Design Methods:Latin Hypercube

Latin hypercube design

A design that tries to fill space such that it is distributed evenly along each factor.

DOE > Space Filling Design > Space Filling Design Methods:Latin Hypercube

least squares means

The predicted value at each level of the indicated term, with other terms being set to neutral values. The Fit Model platform produces these automatically for nominal terms.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Squares

least squares regression

Regression method that minimizes the sum of squared differences from each point to the fitted line (or curve).

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Squares

leave-one-out

NEW!

A cross validation technique where every observation is used as a validation set. For each observation, the model is estimated on the rest of the data and validated on that observation. The validation measures are combined over all observations.

Example Script (JSL)

Analyze > Multivariate Methods > Partial Least Squares > Validation Method

legend (Graph Builder)

Shows descriptions of graph elements. If you attempt to drop a variable here, the variable defaults to Overlay.

Graph > Graph Builder > Legend

Lenth’s Pseudo-standard error

For saturated models when the residual standard error cannot be estimated well, if you assume that most of your effects are ‘inactive’, This is a way to use the inactive effects to estimate the error.

Analyze > Screening

Levene’s test/Testing Variances across groups

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they are not.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Analysis of Means Method > ANOM for Variances with Levene (ADM)

leverage plot

A plot such that the distance from a point to the sloped line is the residual, and the distance to the horizontal line is what the residual would be under the hypothesis.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Squares > Leverage Plot

Levey Jennings chart

Charts that show a process mean with control limits based on a long-term σ. The control limits are placed at 3*σ distance from the center line.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > Levey Jennings

lift curve

Similar to an ROC curve, but constructed to show the initial ordering.

Example Script (JSL)

Analyze > Modeling > Partition > Lift Curve

likelihood-ratio ChiSquare

Formed by twice the difference in the log-likelihoods due to the hypothesis. The likelihood-ratio ChiSquare statistic is found in many different platforms.

Analyze > Fit Y by X > Contingency > Tests > Likelihood Ratio (Also available through Fit Model)

line (fitting, equation for)

See Fitting lines or polynomial.

General

line of fit (Graph Builder)

Shows a linear regression with confidence intervals.

Example Script (JSL)

Graph > Graph Builder > Line of Fit

line plot (Graph Builder)

Shows a response summarized by categories.

Graph > Graph Builder > Line

linear discriminant analysis

Classifying points using discriminant analysis with the same within-covariance matrix for all groups. See ‘discriminant analysis’.

Example Script (JSL)

Analyze > Multivariate Methods > Discriminant > Discriminant Method:Linear, Common Covariance

linear regression

An approach to modeling the relationship between a scalar variable y and one or more explanatory variables denoted X.

Example Script (JSL)

Analyze > Fit Y by X > Fit Line

linearity study

Performs a regression analysis using the standard variable as the X variable, and the bias as the Y. This analysis examines the relationship between bias and the size of the part.

Example Script (JSL)

Analyze > Quality and Process > Variability/Attribute Gauge Chart > Gauge Studies > Linearity Study

log-linear models

Enables you to model both the expected value and the variance of a response using regression models. The log of the variance is fit to one linear model simultaneously with the expected response fit to a different linear model.

Analyze > Fit Model > Personality:Loglinear Variance

log-logistic survival model; see Nonlinear documentation

A parametric survival distribution. Not supported directly by JMP, but you could use Nonlinear.

Nonlinear platform.

log-normal survival model - regression

A distribution for modeling survival times.

Analyze > Reliability and Survival > Survival > LogNormal Fit

log-normal survival model - univariate

A distribution for modeling survival times.

Analyze > Reliability and Survival > Survival > LogNormal Fit

log-rank test (Survival)

Test that the survival distribution is the same across groups.

Analyze > Reliability and Survival > Survival > Tests Between Groups > Log-Rank

logarithms

JMP can perform log transformations using a formula, or in Fit Y by X (bivariate fit special). Graphs can also be log-log or semi-log x or y.

General

logistic regression

Fitting the probability of a categorical response to a linear model.

Analyze > Fit Model > Personality:Nominal Logistic

logit transformation

NEW!

Calculates the inverse of the logistic function for the selected column. The column values must be between 0 and 1.

Example Script (JSL)

Analyze > Fit Model > Construct Model Effects > Transform

LSD - least significant difference

Tests of pairwise comparisons using Student’s t test.

Analyze > Fit Y by X > Oneway > Compare Means > Mean Comparisons > LSD Threshold Matrix

LSN - least significant N

The smallest sample size that would still yield a significant test statistic given the α level, effect size, and error variance.

Analyze > Fit Model > Personality:Standard Least Square > Estimates > Parameter Power > LSN

LSV - least significant Value

The smallest value of the estimate that would still yield a significant test statistic given the α, sample size, and error variance. Also the radius of the confid. interval for an estimate.

Analyze > Fit Model > Personality:Standard Least Square > Estimates > Parameter Power > LSV

M

Term Definition Example of how to access in JMP

Mahalanobis Distance

Measures how far a multivariate point is from a multivariate mean with respect to the covariance structure.

Analyze > Multivariate Methods > Multivariate > Outlier Analysis > Mahalanobis Distance

Mallow’s C(p)

Mallow’s C(p) is a measure to use when comparing models. Usually C(p) is plotted against p, the number of regressors.

Analyze > Fit Model > Personality:Stepwise > Cp

Mann-Whitney U Test

A test that is exactly equivalent to the Wilcoxon 2-sample (or Kruskal Wallis k-sample) test. See ‘Wilcoxon two group test’.

Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test

MANOVA - multivariate analysis of variance

When several responses are fit to the same linear model, and tests are needed that go across the responses.

Analyze > Fit Model > Personality:MANOVA

Mantel-Haenszel test

Computes ChiSquare statistics for stratifications of a two-way contingency table by a third variable.

Analyze > Fit Y by X > Contingency > Cochran Mantel Haenszel

map shape (Graph Builder)

Drop variables here to create map shapes. See Create Map Shapes. If you have a variable in the Map Shape zone, the X and Y zones disappear.

Graph > Graph Builder > Map Shape

map shapes (Graph Builder)

Creates a map on the graph.

Graph > Graph Builder > Map Shapes

margin of error

A measure of sampling error that includes the standard error along with a percentile from a test statistic.

General

marginal distributions (marginal probability)

The probability distribution of one variable derived from a joint probability distribution with a second variable.

General

marginal means

The predicted value at each level of the indicated term, with other terms being set to neutral values. The Fit Model platform produces these automatically for nominal terms.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > LSMeans

matched pairs sign test

This is a nonparametric version of the paired t-test that uses only the sign (positive or negative) of the difference for the test.

Example Script (JSL)

Analyze > Matched Pairs > Sign Test

Mauchly Criterion

Multivariate problems can be made univariate if the covariances have ‘sphericity’. The Mauchly Criterion is for testing this assumption of a spherical covariances.

General

maximum entropy design

Design that optimizes a measure of the amount of information contained in an experiment.

Example Script (JSL)

DOE > Space Filling Design > Space Filling Design Methods:Maximum Entropy

maximum likelihood

A general method in statistics to get estimates that maximize the likelihood, i.e. adjust the estimates to maximize the probability attributed to the data that you actually have. Many platforms in JMP already use maximum likelihood. If your problem does not fit, you might try using the Nonlinear platform, which enables you to specify the negative log-likelihood as a loss function to minimize.

General

maximum likelihood factor analysis

Finding directions among a large set of variables that seem to simplify the structure of the variables into a small number of ‘factors’. Usually the factor solution is rotated to be more interpretable. JMP implements three types of factor analysis: principal component analysis, non-iterated pincipal factor analysis with SMC, and maximum likelihood factor analysis.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Factor Analysis

maximum R2

The maximum attainable value of R2 for the given data if you had to fit a parameter for each unique combination of the regressors. Maximum R2 is included in the lack-of-fit report in many regression reports.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Regression Reports > Lack of Fit > Max R Sq

McNemar’s test

A nonparametric method used on nominal data; this test is applied to a 2x2 contingency table, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal. Bowker’s Test is a generalization of McNemar’s Test.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Exact Test > Exact Agreement Statistic > Bowker’s Test

mean time to failure (MTTF)

NEW!

Mean or average life in reliability analysis.

Example Script (JSL)

Analyze > Realiability and Survival Methods > Reliability Block Diagram > Mean Time to Failure

mean-difference plot

A plot for matched pairs analysis which shows the relationship between the differences versus the means of the paired observations.

Example Script (JSL)

Analyze > Matched Pairs > Plot Dif by Mean

means across groups grouping facility

Use the Summary Command in Tables menu.

Tables > Summary

means across groups one-way layout

Use the Oneway Platform, or Fit Y by X.

Analyze > Fit Y by X > Oneway

means: single population

The mean value for a single population.

Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Means

median - across groups

The middle value, for which half the data is above, and half below.

Analyze > Fit Y by X > Oneway > Nonparametric > Median Test

median - one sample

The middle value, for which half the data is above, and half below.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Median

median absolute deviation (MAD)

Robust measure of the variability of a univariate sample of quantitative data. It is the median of the absolute deviations from the median of the data.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Median Absolute Deviation

median test

A test that compares several distributions by finding the median and counting how many in each group are greater than the median.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Median Test

minimum potential design

Space filling design that spreads points out inside a sphere.

Example Script (JSL)

DOE > Space Filling Design > Space Filling Design Methods:Minimum Potential

missing data pattern

Shows pattern of missing values in data table.

Example Script (JSL)

Tables > Missing Data Pattern

mixed model

Random effects are effects, like subjects, where the levels are randomly selected from a larger population, and their effect on the response can be assumed to vary normally with some variance (the variance component). In Fit Model there are two methods of estimating mixed models.

Analyze > Fit Model > Personality:Standard Least Square

mixed model

  NEW!

A continuous-response linear model that can include both fixed and random effects as well as a specified covariance structure. Such models include random coefficient, repeated measures, split-plot, spatial, and hierarchical models.

Example Script (JSL)

Analyze > Fit Model > Mixed Models

mixed-level designs

Experimental designs when the factors do not all have the same number of levels.

DOE > Custom Design

mixture designs

Experimental designs where the factors sum to 1, as ingredients to a mixture.

DOE > Mixture Design

mode

The value that occurs most frequently in a set of univariate data.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Mode

model averaging

Average fits for a number of models, resulting in a model with improved prediction capability.

Example Script (JSL)

Analyze > Fit Model > Personality:Stepwise > Model Averaging

model comparison

 

Compares the fit of different models. Provides measures of fit, ROC, diagnostic plots, model averaging and profilers.

Analyze > Modeling > Model Comparison

moments

Quantitative measures of the shape of a set of points.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics

monitor classification

Don Wheeler’s classification method that helps interpret the intraclass correlation.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > EMP Results > Intraclass Correlation

mosaic plot

A mosaic plot is like a set of side-by-side divided bar charts, with the sides of rectangles showing the marginal or conditional rates and the areas showing frequency counts.

Analyze > Fit Y by X > Contingency > Mosaic Plot

mosaic plot (Graph Builder)

Shows counts using size for X and Y categories.

Graph > Graph Builder > Mosaic

moving average

Simple moving averages are calculated by taking arithmetic means based on a given set of most recent values.

General

moving range chart

Control chart that displays the moving ranges of two or more successive measurements.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > Control Chart Builder > Right Click:Limits > Sigma > Moving Range (Also available through Control Chart Builder)

MTTF

NEW!

Mean or average life in reliability analysis.

Analyze > Realiability and Survival Methods > Reliability Block Diagram > Mean Time to Failure

multi-vari chart

Shows variation from group to group. Like a Shewhart Control Chart, but used for variation across measurement groups, rather than across time. Use the Variability Chart platform.

Analyze > Quality and Process > Control Chart > Multivariate Control Chart

multicollinearity

Collinearity is when the factors in a model are almost linear combinations of other factors. Collinearity shows as a shrunken X scale on the factors’ leverage plots.

General

multinomial logit

Fits choice models for market research. Conjoint Experiments.

Example Script (JSL)

See Choice Analysis

multiple comparisons

Tests that compare group means in a one-way ANOVA, especially those that recognize that special care is needed to protect the significant level for multiple tests. JMP offers Tukey HSD for a general test, Hsu MCB for testing extreme means, and Dunnett’s test for testing with respect to a control group.

Analyze > Fit Y by X > Oneway > Nonparametric > Median Test

multiple comparisons

NEW!

Comparisons involving several user-defined groups. Includes comparisons with overall average, with a control, Tukey and Student’s t pairwise comparisons, and equivalence tests.

Example Script (JSL)

General

multiple regression

When a response is predicted by a linear combination of several factors.

Analyze > Fit Model > Personality:Standard Least Square

multivariate analysis

A wide collection of methods that analyze a group of responses.

Analyze > Multivariate Methods > Multivariate

multivariate analysis of variance

When several responses are fit to the same linear model, and tests are needed that go across the responses.

Analyze > Fit Model > Personality:MANOVA

multivariate control chart

Creates a chart to view summaries for monitoring problems where several related variables are of interest.

Example Script (JSL)

Analyze > Quality and Process > Multivariate Control Chart

multivariate tests

Four multivariate tests are supported in the MANOVA personality of the Fit Model platform. Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’s Maximum Root Criterion.

Analyze > Fit Model > Personality:MANOVA > Choose Response:Identity > Identity > Whole Model

N

Term Definition Example of how to access in JMP

near orthogonal design

NEW!

A screening design with orthogonal main effects. Useful when interactions are considered negligible.

Example Script (JSL)

DOE > Screening Design > Continue > Near Orthogonal

needle plot

A plot where lines are drawn from zero to the point.

Graph > Chart > Options:Needle Plot

nested designs, effects

In an experiment, when the label of a term B necessarily involves another term A, it is ‘nested’ and the term is written ‘B[A]’. The Fit Model dialog supports this with the ‘Nest’ button.

Analyze > Fit Model > Nest > Personality:Standard Least Square

neural net

Neural Network. Flexible fitting of Y’s to X’s within a specific framework of layering and s-shaped functions.

Example Script (JSL)

Analyze > Modeling > Neural

nominal factors

Nominal factors are categorical factors where there is no special treatment for the ordering of the values. In JMP fitting, they are parameterized with respect to the difference of each level from the average over levels.

General

nonlinear design

A nonlinear design is an optimal design that is nonlinear in its parameters. Such a design is generated using data tables that contain factors, a single response, and a formula column for the model.

DOE > Nonlinear design

nonlinear regression

Fitting equations that are nonlinear in the parameters. Nonlinear regression requires that you specify a formula for the column with parameters to estimate. Nonlinear regression is an iterative method that has no guarantees that it will converge to the best estimate unless you start out near enough to it.

Analyze > Modeling > Nonlinear

nonparametric density

Shows patterns in the point density of a scatterplot, which is useful when the scatterplot is so darkened by points that it is difficult to distinguish patterns.

Example Script (JSL)

Analyze > Fit Y by X > Nonpar Density

nonparametric: exact Kolmogorov-Smirnov test

 

Performs the exact version of the Kolmogorov-Smirnov test. This option is available only when the X factors has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Exact Test > Kolmogorov Smirnov Exact Test

nonparametric: exact van der Waerden test

 

Performs the exact version of the van der Waerden test. This option is available only when the X factor has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Exact Test > Van Der Waerden Exact Test

nonparametric: exact Wilcoxon test

 

Performs exact versions of the Wilcoxon test. This option is available only when the X factor has two levels.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Exact Test > Wilcoxon Exact Test

nonparametric: Hoeffding’s D

A nonparametric measure of association.

Analyze > Multivariate Methods > Multivariate > Nonparametric Correlations > Hoeffding’s D

nonparametric: Kendall’s t

A nonparametric measure of association. It is based on counting concordances and discordances on comparisons of pairs of rows on pairs of columns.

Analyze > Multivariate Methods > Multivariate > Nonparametric Correlations > Kendall’s t

nonparametric: Kolmogorov-Smirnov test

Uses the empirical distribution function to test whether the distribution of the response is the same across groups.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Kolmogorov Smirnov Test

nonparametric: Kruskal-Wallis

A test that compares several distributions by raking the data and comparing the ranks from each group.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test

nonparametric: Median test

A test that compares several distributions by finding the median and counting how many in each group are greater than the median.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Median Test

nonparametric: Spearman’s Rho

Spearman’s Rho is a correlation coefficient computed on the ranks of the data values instead of on the values themselves.

Analyze > Multivariate Methods > Multivariate > Nonparametric Correlations > Spearman’s Rho

nonparametric: Steel with control test

Performs the Steel-Dwass test on each pair.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Steel with Control

nonparametric: Steel-Dwass all pairs test

Performs the Steel-Dwass test on each pair. This is the nonparametric version of the All Pairs, Tukey HSD option found on the Compare Means menu.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Steel-Dwass All Pairs

nonparametric: Van der Waerden test

A test that compares several distributions by ranking the data, using the ranks to form normal scores and comparing the mean scores across groups.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Van Der Waerden Test

nonparametric: Wilcoxon signed-ranks test

Nonparametric test that a mean is equal to a given value.

Analyze > Distribution > Test Mean > Wilcoxon Signed Rank

nonparametric: Wilcoxon test

Performs the Wilcoxon test (rank test for errors with logistic distributions) on each pair, and does not control for the overall α level.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test

nonparametric: Wilcoxon two group test

A test that compares several distributions by ranking the data and comparing the ranks from each group.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test

normal curve

The Normal or Gaussian distribution is the familiar bell-shaped curve, the distribution that arises of summing many small independent random values.

Analyze > Distribution > Continuos Fit > Normal

normal mixtures clustering

Clustering method appropriate when the data are assumed to come from a mixture of multivariate normal distributions.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster > Options:KMeans > Method:Normal Mixtures

normal mixtures fit

Fits a mixture of k normal distributions, where k is the number of groups or clusters.

Example Script (JSL)

Analyze > Distribution > Continuous Fit > Normal Mixtures

normal plot

Helps you identify effects that deviate from the normal lines.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Effect Screening > Normal Plot

normal probability plot

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Analyze > Distribution > Normal Quantile Plot

normal quantile plot

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Analyze > Distribution > Normal Quantile Plot

normal quantile plot by group

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Analyze > Fit Y by X > Oneway > Normal Quantile Plot

normal quantile plot of effects

In Fitting models with large numbers of effects assuming that only a few effects are nonzero, plotting the effect sizes in a normal quantile plot helps identify which effects are active, rather than due to random variation. The feature here ensures that the effects are uncorrelated and have the same variance.

Analyze > Personality:Standard Least Square > Effect Screening > Normal Plot

normality: normal curve on histogram

Use the Distribution Platform, Fit Distribution command, Normal.

Analyze > Distribution > Histogram Options > Histogram > Continuous Fit > Normal

normality: quantile plot

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Example Script (JSL)

Analyze > Distribution > Normal Quantile Plot

normality: Shapiro-Wilk and KSL tests

To test if a distribution is non-normal. The Shapiro-Wilk test is used up to sample sizes of 2000, and the Kolmogorov-Smirnov-Lilliefors test is used above that.

Analyze > Distribution > Continuous Fit > Normal > Goodness of Fit

normalized box plots

Shows one box plot for each column, where the box plots are standardized to a mean of zero and a standard deviation of one.

Example Script (JSL)

Analyze > Quality and Process > Capability > Normalized Box Plots

NP chart

A plot showing the numbers of nonconforming items in subgroup samples.

Analyze > Quality and Process > Control Chart > NP

O

Term Definition Example of how to access in JMP

O’Brien’s test

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they are not.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Unequal Variances

OC (operating characteristic) curve

The OC curve shows how the probability of accepting a lot changes with the quality of the sample.

Analyze > Quality and Process > Control Chart > XBar > Chart Type:XBar > OC Curve

odds ratio confidence interval

The odds ratios are exponentiated linear functions of the parameters. Therefore, the odds ratio confidence interval is calculated by computing a confidence interval for the linear function of the parameters and exponentiating the resulting interval.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistic > Odds Ratio

odds ratios

The ratio of the probability that the event of interest occurs versus the probability that it does not occur.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistic > Odds Ratio

one sample t-test

A test that the mean is some hypothesized value.

Analyze > Fit Y by X > Oneway > Compare Means > Each Pair, Student’s T

one sample two level proportion test

Test if proportions are different than hypothesized values. In the two-sided case, this test is a chi-square test; in either of the one-sided cases, this test is an exact one-sided binomial test.

Analyze > Fit Y by X > Contingency > Tests

one way ANOVA

Fitting means across a grouping variable, and testing if they are significantly different.

Analyze > Fit Y by X > Oneway > ANOVA

operating characteristic curve

The OC curve shows how the probability of accepting a lot changes with the quality of the sample.

Analyze > Quality and Process > Control Chart > XBar > Chart Type:XBar > OC Curve

optimal Bayesian D-optimal designs

This type of design allows the precise estimation of all of the Necessary terms while providing omnibus detectability and some estimability of the If Possible terms.

Example Script (JSL)

DOE > Custom Design

optimal Bayesian I-optimal designs

This type of design minimizes the average prediction variance over the design region and at the same time has the ability to detect and estimate some higher-order terms.

DOE > Custom Design

optimal design

An experimental design that minimizes the determinant of the variance matrix of the regression parameters. The DOE platform does this under Custom Design.

Go to custom designer.

optimal randomized block designs

Optimal designs with a blocking factor where runs within a block are more homogeneous than runs in different blocks.

Example Script (JSL)

Go to custom designer.

optimal split-split plot and strip-plot designs

Designs in which the Very-Hard-to-change factors stay fixed within each whole plot. In the middle stratum, the Hard-to-change factors stay fixed within each subplot.

Example Script (JSL)

General

ordinal factors

Ordinal factors are categorical factors where the ordering of levels matters. In JMP fitting, they are parameterized with respect to the difference of each level from the previous level. For example, the design matrix columns are coded [level>=2], [level>=3] and so on, and the parameters measure the response mean differences [level=2]-[level=1], [level=3]-[level=2], and so on.

General

ordinal logistic regression

Fitting an ordinal (ordered categorical) response probabilities to a linear model. The model fits the probability that a response is less than or equal to specific response levels. Ordinal logistic regression is distinguished from nominal logistic regression in that each response level needs only one new intercept parameter rather than a full new set of parameters for each effect in the model.

Analyze > Fit Model > Personality:Ordinal Logistic

ordinal responses

When a response in a model has modeling type "ordinal", then the fitting system uses ordinal logistic regression to fit it.

Analyze > Fit Model > Personality:Ordinal Logistic

orthogonal array design

Any design in which the columns are orthogonal to each other.

General

orthogonal regression

Regression method that minimizes the orthogonal (perpendicular) distances from the data points to the fitted line.

Example Script (JSL)

Analyze > Fit Y by X > Fit Orthogonal

outlier box plot

A box plot enclosing the inner quartiles of points with lines to the farthest point within 1.5 interquartile ranges from the quartiles.

Example Script (JSL)

Analyze > Distribution > Outlier Box Plot

outlier: Mahalanobis distance

Measures how far a multivariate point is from a multivariate means with respect to the covariance structure.

Analyze > Multivariate Methods > Multivariate > Outlier Analysis > Mahalanobis Distance

overlay plot (Graph Builder)

Groups the Y variables by the selected variables, overlays the response, and marks the levels with different colors.

Graph > Graph Builder > Overlay

overlay plots

Use the Overlay Plot platform.

Graph > Overlay Plot

P

Term Definition Example of how to access in JMP

P chart

A plot showing the proportions of nonconforming items in subgroup samples.

Analyze > Quality and Process > Control Chart > P

p-value

The probability of observing a value of the sample statistic at least as extreme as the value observed in the data if the null hypothesis is true. Compared with the level of significance to make a decision about the null hypothesis.

General

paired t test

For a pair of matched responses, tests that they have the same mean, using Student’s t distribution.

Analyze > Matched Pairs > Reference Frame

parallelism plot

An overlay plot that reflects the average measurement values for each part.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Parallelism Plots

parametric survival models

Fits a regression model to the parameters of a life distribution, such as Weibull.

Analyze > Reliabilty and Survival > Fit Parametric Survival

Pareto chart - general

A bar chart from highest to lowest that also shows the cumulative total.

Analyze > Quality and Process > Pareto Plot

Pareto Plot (effects in a model)

In fitting models to screening designs, shows how the absolute effect sizes add up to the total, in decreasing order from the largest. Fit Model is the platform. Distribution of Effects is the option. Bayes plot is a suboption.

Analyze > Quality and Process > Pareto Plot

partial correlation - adjusted

The correlations between two variables after they have become residuals from being fit to all the other variables in the set.

Analyze > Multivariate Methods > Multivariate > Partial Correlation

partial correlation - group

The correlations of a group of variables after they have become residuals from being to fit to another set of variables.

Anayze > Fit Model > Personality:MANOVA > Partial Correlation

partial least squares

Partial Least Squares. Predicting Y’s with many X’s, especially when there are more X’s than rows.

Analyze > Multivariate Methods > Partial Least Square

partial plot (leverage plot)

A plot such that the distance from a point to the sloped line is the residual, and the distance to the horizontal line is what the residual would be under the hypothesis.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Leverage Plot

partition

Recursively partition the data to predict a response. Classification and regression trees.

Analyze > Modeling > Partition

path diagrams

Available via the structural equation modeling (SEM) SAS add-in.

General

PCA

Finds the rotation of the variables that orders the variation from largest to smallest.

Analyze > Multivariate Methods > Multivariate > Principal Components

PDF plot

A plot of the probability density function.

Analyze > Distribution > Fit Continuous > Normal

Pearson ChiSquare

The ChiSquare statistic in frequency tables for categorical variables which is formed by a weighted sum of squares between observed and expected counts.

Analyze > Fit Y by X > Contingency > Tests > Pearson

percentiles

The value of a variable below which a certain percent of observations fall.

Analyze > Distribution > Quantiles > Percentiles

phase chart

For each phase level, a new σ, set of limits, zones, and resulting tests are done for the control chart.

Example Script (JSL)

Analyze > Quality and Process > Control Chart Builder > Add Phase

pie chart

Use the Chart Platform.

Graph > Chart > Options:Pie Chart

pie chart (Graph Builder)

Shows portions of a whole.

Graph > Graph Builder > Pie

piecewise Weibull change point detection

A method to detect a time point where the reliability model changes.

Analyze > Reliability and Survival > Reliability Growth > Fit Model > Piecewise Weibull NHPP Change Point Detection

Pillai’s Trace

Four multivariate tests are supported in the MANOVA personality of the Fit Model platform. Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’s Maximum Root Criterion.

Analyze > Fit Model > Personality:MANOVA > Choose Response:Identity > Identity > Whole Model > Pillai’s Trace

pivot table (Tabulate)

Drag and drop variables to create a table of counts and statistics.

Tables > Tabulate

Plackett-Burman designs

A type of experimental design for screening.

DOE > Screening Design > Design List > Plackett-Burman

plot - scatterplot

In JMP, there are two primary scatterplot platforms: Bivariate [which supports fitted lines], and OverlayPlot [which supports multiple Y variables].

Analyse > Fit Y by X > Bivariate > Show Points

PLS

Partial Least Squares. Predicting Y’s with many X’s, especially when there are more X’s than rows.

Analyze > Multivariate Methods > Partial Least Square

points (Graph Builder)

Shows data values.

Graph > Graph Builder > Points

Poisson

Distribution, probability, quantile. See Scripting Index in JMP for examples.

Cols > Formula or for a discrete column Distribution > Discrete Fit > Poisson

Poisson regression model

Poisson regressions are useful for data sets with counts as the response. They are fit using JMP’s Fit Model platform with the Generalized Linear Model personality.

Analyzed > Fit Model > Personality:Generalized Linear Model

polynomial regression

Fitting a response to a polynomial of a specified order in one term.

Analyze > Fit Y by X > Fit Polynomial

pooled variance, sum of squares

Average of several group variances, where each group variance is weighted for the size of the group.

General

post hoc tests

Tests that compare group means in a one-way ANOVA. See least significant difference (LSD), Hsu’s MCB test, Tukey-Kramer HSD test, and Dunnett’s test for treatments vs. control.

General

power

The probability of getting a significant result. The probability of rejecting the NULL hypothesis. For prospective power, use the Sample Size platform. For retrospective power, fit a model and look for Power Details.

DOE > Sample Size and Power

power for K sample means

The probability of concluding that there are differences among k means when there truly are differences among the k means.

DOE > Sample Size and Power > k Sample Means

power for one sample mean

The probability of concluding that the mean is different from a hypothesized (null) value when the mean truly is different from the hypothesized value.

DOE > Sample Size and Power > One Sample Mean

power for one sample proportion

The probability of concluding that the proportion is different from a hypothesized (null) value when the proportion truly is different from the hypothesized value.

DOE > Sample Size and Power > One Sample Proportion

power for one sample standard deviation

The probability of concluding that the standard deviation is different from a hypothesized (null) value when the standard deviation truly is different from the hypothesized value.

DOE > Sample Size and Power > One Sample Standard Deviation

power for two sample means

The probability of concluding that two means are different when the two means truly are different from each other.

DOE > Sample Size and Power > Two Sample Means

power for two sample proportion

The probability of concluding that two proportions are different when the two proportions truly are different from each other.

DOE > Sample Size and Power > Two Sample Proportions

PP

An indicator of Process Performance.

Analyze > Distribution > Capabilty Analysis > PP

PPK

Adjustment of PP for the effect of non-centered distribution.

Analyze > Distribution > Capabilty Analysis > CPK

PPL

Adjustment of PP for a lower limit only.

Analyze > Distribution > Capabilty Analysis > CPL

PPM

Adjustment of PP around a target.

Analyze > Distribution > Capabilty Analysis > CPM

PPU

Adjustment of PP for an upper limit only.

Analyze > Distribution > Capabilty Analysis > CPU

prediction interval

Interval that, with a specified degree of confidence, contains either a single observation, or the mean and standard deviation of the next randomly selected sample.

Example Script (JSL)

Analyze > Distribution > Prediction Interval

PRESS RMSE

Residual sum of squares where the residual for each row is computed after dropping that row from the computations.

Example Script (JSL)

Analyze > FIt Model > Personality:Standard Least Square > Row Diagnostics > Press > Press RMSE

presummarized chart

A plot showing presummarized sample means.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > Presummarize

prewhitening

Finds an adequate model for the input series, applies the model to the output, and gets residuals from both series.

Example Script (JSL)

Analyze > Modeling > Time Series > Input Time Series Panel > Input Series > Prewhitening

principal components

Finds the rotation of the variables that orders the variation from largest to smallest.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Principal Component

prior communality estimates: SMC

NEW!

In factor analysis, for each variable, a prior estimate of the proportion of the variance of that variable that is explained by the common factors. Obtained using the squared multiple correlation (SMC) for the regression of that variable on the others.

Example Script (JSL)

Analyze > Consumer Research > Factor Analysis > Common Factor Analysis

probability density function plot

A plot of the probability density function.

Analyze > Distribution > Fit Continuous > Normal

probability I-optimal design

A design that minimizes the prediction variance when predicting the failure probability for the times given in Diagnostic Choices.

DOE > Custom Design

probability models or distributions

Available via the Formula Editor.

Cols > Formula

probable error

Median error for a single measurement.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > EMP Results > Probablility Warning

probit model

Models the probability that a categorical response is a certain level as a function of regressors. Either use Logistic regression, which is similar, or you can look up how to use Nonlinear.

Analyze > Nonlinear

Process Capability Index

Estimates what the process is capable of producing, considering that the process mean might not be centered between the specification limits.

Analyze&; Quality and Process > Capability&; Capability Indices Report

product-limit (Kaplan-Meier) Survival Estimates

An step-function estimate for the univariate survival distribution function. Also called Product limit estimates.

Analyze > Reliability and Survival > Survival > Plot Options > Show Kaplan Meier

profile-likelihood confidence intervals

Confidence limits for parameters corresponding to changes in the likelihood function. These are produced by various nonlinear models, like logistic regression.

Analyze > Fit Model > Personality:Nominal Logistic > Confidence Intervals

profit matrix

NEW!

A specification of weights to categorical outcomes. This specification can then be used to weight the outcomes of a prediction model to obtain a decision model. Can be specified as a column property.

Example Script (JSL)

Analyze > Modeling > Partition > OK > Specify Profit Matrix

proportion

Estimating, confidence intervals, testing.

General

proportional hazards (Cox) Model

A semi-parametric model to fit survival times.

Analyze > Reliability and Survival > Fit Proportional Hazard

pure error

The error variance estimate for exact replicates is called pure error because it is independent of whether the model is right or wrong. It represents the best that can be done in fitting these terms to the model for this data.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Lack of Fit > Pure Error

Q

Term Definition Example of how to access in JMP

Q-Q plot see normality: normal quantile plot

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Analyze > Distribution > Normal Quantile Plot

quadratic discriminant analysis

Classifying points using discriminant analysis with a different covariance matrix for each group. See ‘discriminant analysis’.

Example Script (JSL)

Analyze > Multivariate Methods > Discriminant > Discriminant Method:Quadratic, Different Covariances

quadratic discriminant analysis

Uses a separate covariance matrix for each group.

Example Script (JSL)

Analyze > Multivariate Methods > Discriminant > Discriminant Method:Quadratic, Different Covariances

quality control

See "Quality and Reliability Methods" documentation book.

General

quantile box plot

A box plot that shows the quantiles that would be equally spaced if the data were normally distributed.

Analyze > Distribution > Quantile Box Plot

quantile: normal quantile plot, across groups

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Analyze > Fit Y by X > Oneway > Normal Quantile Plot

quantile: normal quantile plot, single population

A plot of a batch of values versus normal quantiles calculated on ranks. If the data is normal, the points tend to be in a straight line.

Analyze > Distribution > Normal Quantile Plot

quantiles

The value of a variable below which a certain percent of observations fall.

Analyze > Distribution > Display Options > Quantiles

quartiles

Three values that cut a distribution of values into four equal groups. Also in Distribution under Quantiles

Analyze > Fit Y by X: Oneway > Quantiles

R

Term Definition Example of how to access in JMP

R chart

A plot showing a sequence of samples to detect an out-of-control situation in statistical process control.

Analyze > Quality and Process > Control Chart > XBar > R

R2

A measure of degree of fit, ranging from 0 (no fit) to 1 (exact fit). R2 is reported in most regression fit reports.

Analyze > Fit Model > Personality:Standard Least Square > Summary of Fit > RSquare

R2, adjusted

A measure of degree of fit that has been adjusted to reflect the number of parameters in the model. Unlike the unadjusted R2, the adjusted R2 does not always increase as more terms are added to the model.

Analyze > Fit Model > Personality:Standard Least Square > Summary of Fit > RSquare Adj

R2, entropy

Measure of fit that compares the log-likelihoods from the fitted model and the constant probability model.

Analyze > Fit Model > Personality:Nominal Logistics > Whole Model Test > Entropy R2

R2, generalized

Generalization of the R2 measure of fit that simplifies to the regular R2 measure for continuous normal responses. It is similar to the entropy R2 measure, but instead of using the log-likelihood, it uses the 2/n root of the likelihood.

Analyze > Fit Model > Personality:Nominal Logistic > Whole Model Test > Generalized RSquare

R2, maximum

The maximum attainable value of R2 for the given data if you had to fit a parameter for each unique combination of the regressors. Maximum R2 is included in the lack-of-fit report in many regression reports.

Analyze > Fit Model > Personality:Nominal Logistic > Lack of Fit > Rsquare Max

random effect

A discrete variable is considered random if the levels are randomly selected from a larger population.

Analyze > Fit Model > Construct Model Effects > Attributes > Random Effect

random effects

Random effects are effects, like subjects, where the levels are randomly selected from a larger population, and their effect on the response can be assumed to vary normally with some variance (the variance component). In Fit Model there are two methods of estimating mixed models.

General

Random Foresttm

 

Creates many trees and computes the final predicted value by averaging the predicted values.

Example Script (JSL)

Analyze > Modeling > Partition > Method:Bootstrap Forest

range

Difference between the maximum and minimum observation.

Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Range

range chart

A plot of the variability statistic for each combination of the part and X variables.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Range Chart

range odds ratios

Calculates the change in the ratio of probabilities across the entire range of the continuous independent variable.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistic > Odds Ratio

range risk ratio

Shows the risk change over the whole range of the regressor in a proportional hazards model.

Example Script (JSL)

Analyze > Reliability and Survivall > Fit Proportional Hazard > Risk Ratios

rare events chart

NEW!

Control charts used to determine whether rare events are occurring more frequently. G-charts plot the number of events between rare events, and T-charts plot the time between rare events.

Example Script (JSL)

Analyze > Quality and Process > Control Chart Builder > Control Chart Types

recoding

Discrete values in a column can be recoded to recategorize or make corrections to the existing values.

Cols > Recode

recurrence analysis

Analyzes how a recurring event is distributed over time, per system, or until the system goes out of service.

Example Script (JSL)

Analyze > Reliability and Survival > Recurrence Analysis

recursive Partitioning

Recursively partition the data to predict a response. Classification and regression trees.

Analyze > Modeling > Partition

regression diagnostics

Analysis of residuals, influence of outliers, leverage and multicollinearity.

General

regression tree

A decision tree for a continuous response variable.

Example Script (JSL)

Analyze > Modeling > Partition > Display Options > Show Tree

regression: logistic

Fitting the probability of a categorical response to a linear model.

Analyze > Fit Model > Personality:Nominal/Ordinal Logistic

regression: multiple regression

When a response is predicted by a linear combination of several factors.

Analyze > Fit Model

regression: one regressor

Fitting a line through a set of points using the least squares criterion.

Example Script (JSL)

Analyze > Fit Y by X > Fit Line

regression: polynomial

Fitting a response to a polynomial of a specified order in one term.

Analyze > Fit Model > Macros > Polynomial to Degree

regularized discriminant analysis

Classifying points using discriminant analysis with a compromise between linear and quadratic approaches. See ‘discriminant analysis’.

Example Script (JSL)

Analyze > Multivariate Methods > Discriminant > Discriminant Method:Regularized, Compromise Method

regularized discriminant analysis

Analysis that is a compromise between the linear and quadratic methods. Regularized discriminant analysis allows for specification of two parameters, lambda and gamma. Lambda specifies how to mix the individual and group covariance matrices. Gamma specifies whether to deflate the non-diagonal elements.

Example Script (JSL)

Analyze > Multivariate Methods > Discriminant > Discriminant Method:Regularized, Compromise Method

relative risk

A measure of the relative likelihood of an event occurring between two distinct groups (in the context of a 2x2 contingency table).

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Relative Risk

relative risk ratio

A measure of the relative likelihood of an event occurring between two distinct groups (in the context of a 2x2 contingency tables).

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Relative Risk

reliability - failure time

Reliability models the failure times, in the same way that survival analysis does. For simple univariate models, this is done with the Survival platform. For models with more regressors, Fit Model is used with one of the Survival personalities.

Analyze > Reliability and Survival > Reliability Growth

reliability block diagram

  NEW!

A diagram of a complex system, constructed from simple components. Distributions assigned to the components allow you to analyze the reliability of the entire system.

Example Script (JSL)

Analyze > Realiability and Survival Methods > Reliability Block Diagram

reliability demonstration

A reliability demonstration consists of testing a specified number of units for a specified period of time. If fewer than k units fail, you pass the demonstration, and conclude that the product reliability meets or exceeds a reliability standard.

DOE > Sample Size and Power > Reliability Demonstration

reliability forecast

Analysis that predicts future product failures based on previous production and failure counts.

Example Script (JSL)

Analyze > Reliability and Survival > Reliability Forecast

reliability growth model

Model that allows for tracking reliability of a process during development testing to determine whether changes to the process are improving process reliability over time.

Example Script (JSL)

Analyze > Reliability and Survival > Reliability Growth > Fit Model > Crow AMSAA

reliability test plan

Determines the sample size or the length of study needed to obtain a certain precision about a fitted quantile or probability.

DOE > Sample Size and Power > Reliability Test Plan

remaining life BCI

  NEW!

A measure of the impact of a component to system reliability over time, given that the system has survived a specified amount of time. A large BCI indicates that the system is sensitive to the component.

Example Script (JSL)

Analyze > Realiability and Survival Methods > Reliability Block Diagram > Show BCI

repeated measures - multivariate

Repeated measures can be modeled as a multivariate model. Each measurement on a subject is a separate column, and a response for the model.

Analyze > Fit Model > Personality:MANOVA > Choose Response:Repeated Measures

repeated measures - univariate sphericity-adjusted

Multivariate problems can be made univariate if the covariances have ‘sphericity’. The MANOVA of personality of ‘Fit Model’ with the ‘Univariate’ option tests sphericity (Mauchly Criterion) and calculates using two adjustments to degrees of freedom: Greenhouse-Geisser and Huynh-Feldt.

Analyze > Fit Model > Personality:MANOVA > Univariate Tests Also > Choose Response:Repeated Measures > Within Subjects > Sphericity Test

repeated measures-univariate (mixed-models)

Repeated measures can be modeled in a mixed model with a random effect for subject. Each measurement is a row in the table.

Analyze > Fit Model > Personality:MANOVA > Univariate Tests Also > Choose Response:Repeated Measures > Within Subjects > Sphericity Test

replicates

Fully repeated set of test conditions.

Example Script (JSL)

DOE > Custom Design > Design Generation > Number of Replicate Runs

residual analysis

The use of residuals (including residual plots) to evaluate linear regression models. Different types of plots of the residuals from a fitted model provide information about the adequacy of different aspects of the model.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Row Diagnostics > Plot Residual

response screening

NEW!

Automates the process of conducting tests across a large number of responses. Provides plots of false discovery rate p-values and tests based on robust estimates.

Example Script (JSL)

Analyze > Specialized Models > Response Screening

response surface designs

An experimental designs that allow curvature terms with the idea of finding the optimum value for a response. Central composite and Box-Behnken.

DOE > Response Surface Design

response surface methodology

The fitting and analysis of response surfaces to help determine optimums, or directions to look for the optimum.

Analyze > Fit Model > Attributes:Response Surface Effect > Personality:Standard Least Square

ridge regression

  NEW!

A generalized regression estimation method that applies an L2 penalty in estimating parameters.

Example Script (JSL)

Analyze > Fit Model > Generalized Regression Models > Ridge Regression

risk ratios (proportional hazard)

A measure of the relative likelihood of an event occurring between two distinct groups (in the context of a proportional hazards model).

Example Script (JSL)

Analyze > Reliability and Survivall > Fit Proportional Hazard > Risk Ratios

robust fit (bivariate)

NEW!

Fits a line using estimates for the parameters that are less sensitive to outliers than the usual least squares estimates. Uses Huber M-estimation.

Example Script (JSL)

Analyze > Oneway Analysis > Robust Fit

robust fit (oneway)

NEW!

Provides estimates that are resistant to outliers. Conducts the ANOVA test using these statistics. Uses Huber M-estimation.

Example Script (JSL)

Analyze > Oneway Analysis > Robust Fit

robust mean

NEW!

Provides an estimate of the mean that is resistant to outliers. Uses Huber M-estimation.

Example Script (JSL)

Analyze > Distribution > Customize Summary Statistics > Robust Mean

robust regression

Fitting models such that the fit is not sensitive to outliers or to a non-normal distribution. JMP does not offer specific techniques for this, but the documentation for Nonlinear describes techniques for it.

General

robust standard deviation

NEW!

Provides an estimate of the standard deviation that is resistant to outliers. Uses Huber M-estimation.

Example Script (JSL)

Analyze > Distribution > Customize Summary Statistics > Robust Std Dev

ROC curve

The Receiver Operating Characteristic curve is a graphical plot of the sensitivity, or true positive rate, versus false positive rate.

Analyze > Fit Model > Personality > Ordinal Logistic > ROC Curve

root means square error (RMSE)

Standard error of regression.

General

rotated factor loading

NEW!

In factor analysis, provides loadings for each variable on the rotated factors. A loading reflects the correlation between a variable and the factor.

Example Script (JSL)

Analyze > Consumer Research > Factor Analysis > OK > Rotated Factor Loading

Roy’s Maximum Root Criterion

Four multivariate tests are supported in the MANOVA personality of the Fit Model platform. Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’s Maximum Root Criterion.

Analyze > Fit Model > Personality:MANOVA > Choose Response:Identity > Identity > Whole Model > Roy’s Max Root

run chart

A plot showing a run of the samples.

Analyze > Quality and Process > Control Chart > Run Chart

S

Term Definition Example of how to access in JMP

S chart

Control chart that displays the subgroup standard deviations.

Analyze > Quality and Process > Control Chart > XBar > S

sample size

The Sample Size dialog calculates the sample size needed to achieve a given power for a test.

DOE > Sample Size and Power

sampling

In statistics, sampling refers to the selection of a subset of individuals from a population in order to estimate certain characteristics of the population.

General

scatterplot

In JMP, there are two primary scatterplot platforms: Bivariate [which supports fitted lines], and OverlayPlot [which supports multiple Y variables].

Analyse > Fit Y by X > Bivariate > Show Points

scatterplot 3D

A three-dimensional spinnable view of your data.

Example Script (JSL)

Graph > Scatterplot 3D

scatterplot matrix

A grid of scatterplots of all pairings of a set of variables.

Graph > Scatterplot Matrix

schematic plot

Another name for an outlier box plot.

Analyze > Distribution > Outlier Box Plot

score plot

NEW!

A scatterplot matrix where each cell is a plot of the scores for each variable on a pair of factors.

Example Script (JSL)

Analyze > Consumer Research > Factor Analysis > OK > Score Plot

score summaries

NEW!

Provides a table showing misclassification results for classification based on the scores.

Example Script (JSL)

Analyze > Multivariate Methods > Discriminant Analysis > Score Summaries

scree plot

A plot of the eigenvalues versus the number of components in principal components analysis. This plot is useful for visualizing the dimensionality of the data space.

Example Script (JSL)

Analyze > Multivariate Methods > Multivariate > Scree Plot

screening analysis

Model data where most effects are assumed to be inactive. The smaller estimates can help estimate the error in the model and determine whether the larger effects are real.

Example Script (JSL)

Analyze > Modeling > Screening

screening design

A screening design is used to discover active factors from a large number of potential factors.

Example Script (JSL)

DOE > Screening Design

screening designs

Use the DOE platform, either Screening or Custom designs.

DOE > Screening/Custom Design

seasonal models

Seasonal Models capture and estimate the regular pattern of changes that repeats over time (i.e., seasonality) in a time series.

General

self-organizing maps

(SOM) is a type of neural network; the goal is to form clusters on a cluster grid, such that points in clusters that are near each other in the SOM grid are also near each other in multivariate space.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster > Options:KMeans > Method:Self Organizing Maps

sequential sum of squares

Sums of squares that depend on the order of the effects in the model. Sequential tests show the reduction in the residual sum of squares as each effect is entered into the model. Also called Type I sums of squares (Type I SS).

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Squares > Estimates > Sequential Tests

Shapiro-Wilk test for normality

To test if data is normally distributed.

Analyze > Distribution > Continuous Fit > Normal > Goodness of Fit > Shapiro-Wilk W Test

Shewhart Charts

The standard type of control chart for statistical quality control.

Analyze > Quality and Process > Control Chart > XBar > S (Also available through Control Chart Builder)

signed-rank test

Nonparametric test that a mean is equal to a given value.

Analyze > Distribution > Test Mean > Wilcoxon Signed Rank

simple linear regression

Fitting a line through a set of points using the least squares criterion.

Analyze > Fit Y by X > Fit Line

simple moving average

Time series model based on the unweighted mean of the previous n data points.

Example Script (JSL)

Analyze > Modeling > Time Series > Smoothing Model > Simple Moving Average

simplex centroid design

An experimental design for mixtures that takes the vertices and various degrees of means (centroids) of those vertices.

DOE > Mixture Design > Choose Mixture Design Type > Simplex Centroid

simplex lattice design

An experimental design for mixtures that creates a triangular grid of values.

DOE > Mixture Design > Choose Mixture Design Type > Simplex Lattice

Six Sigma

A business management strategy that seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes.

General

size by variable (Graph Builder)

Scales map shapes according to the size variable, minimizing distortion.

Graph > Graph Builder > Size

skewness

The third moment of a distribution, which helps measure the asymmetry of a distribution.

Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Skewness

slope

The gradient or steepness of a line. See fitting lines.

General

smoothed empirical likelihood quantiles

Quantiles with smoothed empirical likelihood confidence intervals.

Example Script (JSL)

Analyze > Distribution > Display Options > Custom Quantiles

smoother, smoothing spline (Graph Builder)

Shows a smooth curve through the data. The smoother is a cubic spline with a lambda of 0.05 and standardized X values.

Graph > Graph Builder > Smoother

smoothers - splines

A fitted line that is gently curved as needed to better fit points. It is optimizing a compromise between a better fit and a smoother line. It is formed by 3rd degree polynomial segments that are spliced together to be continuous and smooth.

Analyze > Fit Y by X > Fit Spline

Somer’s D

A nonparametric measure of ordinal association that uses the counts of concordant and discordant pairs. A pair is concordant if an observation with a larger value of X also has a larger value of Y. A pair is discordant if an observation with a larger value of X has a smaller value of Y. Only appropriate when both variables are ordinal.

Analyze > Fit Y by X > Contingency > Measures of Association > Somer’s D

sort data tables

Sort by any number of variables in either ascending or descending order.

Tables > Sort

Space filling

A design that tries to fill space, so that all points in the factor space are not far from a design point.

DOE > Space Filling Design

space filling design

NEW!

A design constructed so as to minimize bias in estimating models for systems that are deterministic or near-deterministic. The Space Filling Design option provides a number of methods that spread the points over the design space to achieve this goal.

Example Script (JSL)

DOE > Mixture Design > Continue > Space Filling

Spearman’s rho

A nonparametric measure of association. Calculated as the correlations of the ranks of the two variables.

Analyze > Multivariate Methods > Multivariate > Nonparametric Correlations > Spearman’s Rho

Sphere packing

A design that tries to fill space such that the largest sphere can be drawn around each point without spheres intersecting.

DOE > Space Filling Design > Space Filling Design Methods:Sphere Packing

sphericity test and adjustments

Multivariate problems can be made univariate if the covariances have ‘sphericity’. The MANOVA personality of ‘Fit Model’ with the ‘Univariate’ option tests sphericity (mauchly Criterion) and calculates using two adjustments to degrees of freedom: Greenhouse-Geisser and Huynh-Feldt.

Analyze > Fit Model > Personality:MANOVA > Univariate Tests Also > Choose Response:Repeated Measures > Within Subjects > Sphericity Test

spinning plot

A scatterplot that becomes 3-dimensional by rotating it in real time.

Example Script (JSL)

Graph > Scatterplot 3D

splines - smoothing

A fitted line that is gently curved as needed to better fit points. It is optimizing a compromise between a better fit and a smoother line. It is formed by 3rd degree polynomial segments that are spliced together to be continuous and smooth.

Analyze > Fit Y by X > Fit Spline

split data tables

Split columns to create a shorter, wider table.

Tables > Split

Split Plot Design

An experimental design that has been randomized in layers, with whole plot factors defining the upper layer, usually with hard to change factors, and subplot factors nested to form the lower layer.

Example Script (JSL)

DOE > Custom Design > Design Generation > Hard to change...to change factors

Split Plot Fitting

It is important to declare an effect defining the Whole plots with the Random attribute for a correct analysis of split plot designs.

Analyze > Fit Model

split-split plot design

Designs in which the Very-Hard-to-change factors stay fixed within each whole plot. In the middle stratum, the Hard-to-change factors stay fixed within each subplot.

Example Script (JSL)

DOE > Custom Design > Design Generation > Hard to change...to change factors

stack data tables

Stack columns to create a long, narrow table.

Tables > Stack

standard deviation

A measure of true spread in a distribution around the mean, or sometimes its estimate. Standard deviation is a pervasive concept, but if you are looking for a simple estimate in a batch of values, the Distribution platform or the Summary command are used.

Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Std Dev

standard error

The standard deviation of a statistic, such as the mean or the difference between two means.

General

standardize robustly

NEW!

In cluster analysis, standardizes the variables so as to minimize the influence of outliers in distance calculations. This is done by inflating the standard deviation estimate.

Example Script (JSL)

Analyze > Multivariate Methods > Cluster Analysis > Hierarchical > Standardize Robust

standardized regression coefficients

Parameter estimates that would result if you standardized your variables before fitting a model. In the Fit Model platform, they are available by unhiding columns in the parameter estimates table, using a context-click.

Analyze > Fit Y by X > Bivariate > Parameter Estimates > Columns > Std Beta

standardizing

Create a new column and create for it a formula like: (x-Col Mean(x))/Col Std Dev(x). Or use Distribution and select ‘Save Standardized’. For grouped data use Oneway and its ‘Save Standardized’ command.

Analyze > Distribution > Save > Standardized

Steel with control test

Performs the Steel-Dwass test on each pair.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Steel with Control

Steel-Dwass all pairs test

Performs the Steel-Dwass test on each pair. This is the nonparametric version of the All Pairs, Tukey HSD option found on the Compare Means menu.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Steel-Dwass All Pairs

stem and leaf plot

The stem and leaf plot is similar to a histogram, but the bars are consist of digits representing the later digits of the values.

Analyze > Distribution > Stem Leaf Plot

step plots

A point-to-point plot where the connecting lines are drawn as a horizontal and vertical step, rather than as a direct line.

Graph > Overlay Plot > Y Options > Step

stepwise regression

A regression approach that involves incrementally adding or deleting terms to a regression model.

Analyze > Fit Model > Personality:Stepwise

strip plot design

An experimental design involving Hard and Very Hard to change factors. Hard to change factors can vary independently of Very Hard to change factors.

Example Script (JSL)

DOE > Custom Design > Design Generation > Hard to change...to change factors

Stuart’s Tau-C

A nonparametric measure of ordinal association that uses the counts of concordant and discordant pairs. A pair is concordant if an observation with a larger value of X also has a larger value of Y. A pair is discordant if an observation with a larger value of X has a smaller value of Y. Only appropriate when both variables are ordinal.

Example Script (JSL)

Analyze > Fit Y by X > Contingency > Measures of Association > Stuart’s Tau-C

Student’s t

The distribution that results when a normally distributed estimator with [hypothesized] mean zero is divided by an independent estimator of its standard error. Test statistics are formed that measure significance by how improbably large a value the estimate is if the true mean were zero.

Analyze > Fit Model > Personality:Standard Least Square > LSMeans Student’s t

Student’s t: Each Pair

Individual pairwise comparisons of group means using Student’s t test. See ‘multiple comparisons’.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Compare Means > Each Pair, Student’s t

Student’s t: Paired

For a pair of matched responses, tests that they have the same means, using Student’s T distribution.

Analyze > Matched Pairs > Reference Frame

Student’s t: test parameter in linear model

In fitting platforms, estimates are routinely reported with their standard error and the t-ratio, the ratio of their value to the standard error, which has a Student’s t distribution under the hypothesis that the true parameter is zero.

Analyze > Distribution > Parameter Estimates

Student’s t: test that mean=value

A test that the mean is some hypothesized value.

Example Script (JSL)

Analyze > Distribution > Test Mean

Student’s t: two groups equal variance

The standard test that the means between two groups are equal. It is assumed that the error variance is the same in the two groups. This is a special case of the F test in one-way anova.

Analyze > Fit Y by X > Oneway > Compare Means > Each Pair, Student’s t

Student’s t: two groups unequal variance

A Student’s t test that has been weighted to adjust for different variances in the two groups.

Analyze > Fit Y by X > Oneway > Compare Means > Each Pair, Student’s t

subset data tables

Subset selected rows and columns.

Tables > Subset

summarize data tables

Summarize columns from the active tables.

Tables > Summary

summary statistics

Mean, Std Dev, Std Err Mean, Upper Mean Confidence Limits, Lower Mean Confidence Limits, N, Sum Weight, Sum, Variance, Skewness, Kurtosis, CV, N Missing, N Zero, N Unique, Uncorrected SS, Corrected SS, Autocorrelation, Median, Mode, Trimmed Mean, Geometric Mean, Range, Interquartile Range, Median Absolute Deviation.

Distribution >Summary Statistics > Customize Summary Statistics

surveys

See documentation for categorical analysis.

Analyze > Modeling > Categorical

survival - parametric survival models

Fits a regression model to the parameters of a life distribution, such as Weibull.

Example Script (JSL)

Analyze > Reliability and Survival > Fit Parametric Survival

survival analysis

Models the distribution of ‘failure’ event times. For simple univariate models, this is done with the Survival platform. For more models with regressors, Fit Model is used with one of the Survival personalities.

Analyze > Reliability and Survival > Survival

survival estimates - product limit (Kaplan-Meier)

An step-function estimate for the univariate survival distribution function. Also called Product limit estimates.

Analyze > Reliability and Survival > Survival > Plot Options > Show Kaplan Meier

T

Term Definition Example of how to access in JMP

T chart

NEW!

A control chart for rare events to determine whether rare events are occurring more frequently than expected by graphing time between events.

Analyze > Quality and Process > Control Chart Builder > Sigma > Weibull

t distribution (Student’s t distribution)

A family of continuous probability distributions defined by the degrees of freedom, used to estimate population parameters when the sample size is small and/or when the population variance is unknown.

General

t test: See Student’s t

The distribution that results when a normally distributed estimator with [hypothesized] mean zero is divided by an independent estimator of its standard error. Test statistics are formed that measure significance by how improbably large a value the estimate is if the true mean were zero.

Analyze > Fit Y by X > Oneway > Compare Means > Each Pair, Student’s t

T2 statistic

A measure of multivariate distance that takes into account the variances and covariances. Equal to the squared Mahalanobis distance.

Analyze > Multivariate Methods > Multivariate > Outlier Analysis > T2

Taguchi robust parameter design

A type experimental design that has an inner design for control factors, and an outer design over noise factors, with a response calculated from a signal-to-noise statistic over the noise factors.

DOE > Taguchi Arrays

ternary plot

A triangular plot showing how three factors that add up. Usually they are ingredients in a mixture, and they add up to 1. Because each factor is a function of the other two, it can be represented in two dimensions. If there are more than three factors two are identified and the others are grouped into ‘other factors’.

Graph > Ternary Plot

test of proportions

Test if proportions are different than hypothesized values. In the two-sided case, this test is a chi-square test; in either of the one-sided cases, this test is an exact one-sided binomial test.

Example Script (JSL)

Analyze > Distribution > Test of Proportions

test-retest error

Used to check for detectable differences between the different levels of the potential nuisance component.

Example Script (JSL)

Analyze > Quality and Process > Measurement Systems Analysis > Test-Retest Error Comparison

three-level designs

Use the DOE platform, either Screening or Custom designs.

DOE > Screening/Custom Design

time I-optimal design

Design that minimizes the prediction variance when predicting the time to failure for the probability given in Diagnostic Choices.

DOE > Custom Design

time series analysis

Analysis of how values change across equally-spaced time.

Analyze > Modeling > Time Series

tobit model

A model where the response is truncated with a lower bound of zero. This is handled similarly to censoring in survival models. JMP does not have a specific feature, but a Tobit example is shown in the documentation for Nonlinear.

Analyze > Modeling > Nonlinear

tolerance interval

Interval defined as the upper specification limit minus the lower specification limit.

Example Script (JSL)

Analyze > Distribution > Tolerance Interval

transfer function model

Model based on the past values of explanatory variables and residuals are modeled as an ARIMA model.

Example Script (JSL)

Analyze > Modeling > Time Series > Transfer Function

transformations

In Fit Y by X, fit special for transformations, or use a column formula.

General

transpose data tables

Transpose to interchange rows and columns.

Example Script (JSL)

Tables > Transpose

treatment

In experiments, a treatment is a condition applied to the experimental units.

General

tree map

Chart that tiles a rectangle with category rectangles. This is useful when there are a lot of categories and a bar chart of the data creates a long line of bars.

Graph > Tree Map

treemap (Graph Builder)

Shows a response summarized by categories.

Example Script (JSL)

Graph > Graph Builder > Treemap

trees

Recursively partition the data to predict a response. Classification and regression trees.

Analyze > Modeling > Partition

trimmed mean

The mean calculated after removing the smallest p% and the largest p% of the data.

Example Script (JSL)

Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Trimmed Mean

Tukey mean-difference plot

A plot for matched pairs analysis which shows the relationship between the differences versus the means of the paired observations.

Example Script (JSL)

Analyze > Matched Pairs > Plot Dif by Mean

Tukey-Kramer HSD test

A general test for differences among a set of means that controls the significance level for multiple comparisons. See ‘multiple comparisons’.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Compare Means > All Pairs, Tukey HSD

two sample proportion test

Test if proportions between two samples are different. You can test it by Pearson’s or likelihood ratio chi-squared test.

Example Script (JSL)

Analyze > Fit Y by X > Contingency >Tests

two sample t-test

Used to determine whether two population means are equal.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Means/ANOVA/Pooled t

two-level designs

Use the DOE platform, either Screening or Custom designs.

DOE > Screening/Custom Design

two-way ANOVA

Examines the influence of two different categorical independent variables on one dependent variable.

Example Script (JSL)

Analyze > Fit Model > Personality:Standard Least Square > Analysis of Variance

type I and type II errors

Type I error (false positive): The null hypothesis is rejected when it is true. Type II error (false negative): The null hypothesis is not rejected when it is false.

General

types of sums of squares

JMP produces results similar to SAS GLM’s Types III and IV. JMP’s SS agree with GLM when Types III and IV agree themselves, which occurs in all designs without missing cells.

General

U

Term Definition Example of how to access in JMP

U chart

A plot showing the numbers of nonconformities per inspection unit in subgroup samples.

Analyze > Quality and Process > Control Chart > U

uncorrected sum of squares

Total sum of squares, uncorrected for the mean.

Example Script (JSL)

Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Uncorrected SS

uniform precision see response surface designs

Designs whose variance is relatively uniform. See DOE Platform for Response Surface.

DOE > Response Surface Design > Choose a Design > CCD-Uniform Precision

Uniform Space filling Design

A design that tries to fill space such that the multivariate empirical cumulative distribution function is closest to the uniform distribution.

DOE > Space Filling Design > Space Filling Design Methods:Uniform

Uniformly weighted moving average (UWMA) chart

A plot showing a uniformly weighted moving average chart.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > UWMA

unit odds ratios

Calculates the change in the ratio of probabilities as the continuous independent variable changes by 1 unit.

Example Script (JSL)

Analyze > Fit Model > Personality:Nominal Logistic > Odds Ratio

unit risk ratio

Shows the risk change over one unit of the regressor in a proportional hazards model.

Example Script (JSL)

Analyze > Reliability and Survivall > Fit Proportional Hazard > Risk Ratios

univariate distribution

Use Distribution Platform.

Analyze > Distribution

univariate repeated measures

Set up a new column and add a formula to it. Some fitting platforms provide features for doing this without needing a new column.

Multiple

univariate repeated measures

See repeated measures topics.

Analyze > Fit Model > Personality:MANOVA > Univariate Tests Also > Choose Response:Repeated Measures > Within Subjects > Sphericity Test

update data tables

Update one table with values from another table.

Tables > Update

uplift

  NEW!

A partition model which helps identify characteristics of individuals who are likely to respond to an intervention or treatment.

Analyze > Consumer Research > Uplift Model Report

upper and lower control limit

See control charts.

General

V

Term Definition Example of how to access in JMP

validation column

 

Validation column role in many modeling platforms. Used to provide honest assessment of model performance by splitting data into training, validation and test sets.

Use validation column in many modeling platforms

Van der Waerden Test

A test that compares several distributions by ranking the data, using the ranks to form normal scores and comparing the mean scores across groups.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Van Der Waerden Test

variability analysis

Variability Chart shows variation across groups, and can fit variance components.

Analyze > Quality and Process > Variability/Attribute Gauge Chart

variable importance

NEW!

A method for assessing the importance of variables that is independent of model type. Only available for continuous responses.

Example Script (JSL)

Predicition Profiler > Assess Variable Importance

variables clustering (principal component analysis)

 

Variable clustering option for predictor variable reduction prior to modeling.

Analyze > Multivariate Methods > Principal Components > Cluster Variables

variance

A measure of how far a set of numbers is spread out.

Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Variance

variance components

Random effects are effects, like subjects, where the levels are randomly selected from a larger population, and their effect on the response can be assumed to vary normally with some variance (the variance component). In Fit Model there are two methods of estimating mixed models.

ANalyze >Quality and Process >Variability/Attribute Gauge Chart

variance homogeneity

Testing that the variances are equal in a one-way layout and providing a weighted (Welch) Anova in case they are not.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Unequal Variances

variance inflation factors (VIF)

In regression where the regressors are highly correlated, a measure of interest is how much inflated the variance of the estimator compared with what its variance would be without the effect of the other regressors. In Fit Model, the VIF is available by context-clicking in the Parameter Estimates report table.

Analyze > Fit Model > Personality:Standard Least Square > Parameter Estimates > Right Click:Columns > VIF

violin plot (Graph Builder)

Shows regions of density or value contours. If you specify only one continuous variable for X or Y, a violin plot appears instead of a contour plot.

Example Script (JSL)

Graph > Graph Builder > Contour

W

Wald Test

A ChiSquare test that is a linear approximation to a likelihood ratio ChiSquare. It is cheaper but considered less reliable than the likelihood ratio ChiSquare. The Fit Model platform for nominal/ordinal responses calculates Wald tests automatically, but waits for a request to do the more consuming likelihood ratio ChiSquares.

Analyze > Fit Model > Personality:Ordinal Logistic > Wald Tests

Weibayes analysis

A reliability analysis that can be used when there are no failures in the observed data.

Example Script (JSL)

Analyze > Reliability and Survival > Life Distribution > Weibull

Weibull Plot - survival

A Weibull plot for failure times puts the log(-log(Survival probability)) on the y axis, and the log(time) on the x axis. If the events are Weibull-distributed, the points tend to follow a straight line.

Analyze > Reliability and Survival > Survival > Weibull Plot

Weibull survival model

Weibull is the most common probability distribution used to model failure time responses, in survival and reliability studies. Weibull models are supported in the Survival platform for univariate studies, and in the parametric survival personality of Fit Model.

Analyze > Reliability and Survival > Survival > Weibull Fit

weighting

Weighting is a statistical method that adjusts your sample data in the estimation of population parameters.

General

Welch Anova

A oneway analysis of variance that is weighted according to the different variances estimated from the data. The 2-group Student’s t test for unequal variances is a special case of this.

Analyze > Fit Y by X > Oneway > Unequal Variances > Welch’s Test

Western Electric rules

Tests for special causes.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > XBar > XBar/R > Tests > All Tests

Westgard rules

Tests for special causes that follow the Westgard Rules.

Example Script (JSL)

Analyze > Quality and Process > Control Chart > Levey Jennings > Westgard Rules

Wilcoxon each pair test

Performs the Wilcoxon test (rank test for errors with logistic distributions) on each pair, and does not control for the overall α level.

Example Script (JSL)

Analyze > Fit Y by X > Oneway > Nonparametric > Nonparametric Multiple Comparisons > Wilcoxon Each Pair Test

Wilcoxon rank sum test

A nonparametric statistical hypothesis test for assessing whether one of two samples of independent observations tends to have larger values than the other. Also called the Mann-Whitney U test. When more than two groups called a Kruskal-Wallis test.

Example Script (JSL)

Analyze > Fit Y by X > Nonparametric > Wilcoxon Test

Wilcoxon signed-ranks test

Nonparametric test that a mean is equal to a given value.

Analyze > Distribution > Test Mean > Wilcoxon Signed Rank

Wilcoxon test (Survival)

For univariate survival models across several groups, this is one of the tests that the survival distribution is the same across groups.

Analyze > Reliability and Survival > Survival > Tests Between Groups > Wilcoxon

Wilcoxon two group test

A test that compares several distributions by ranking the data and comparing the ranks from each group.

Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test

Wilks’ Lambda

Four multivariate tests are supported in the MANOVA personality of the Fit Model platform. Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawly Trace, and Roy’s Maximum Root Criterion.

Analyze > Fit Model > Personality:MANOVA > Choose Response:Identity > Identity > Whole Model > Wilks’ Lambda

willingness to pay

NEW!

How much a price must change allowing for the new feature settings to produce the same predicted outcome.

Example Script (JSL)

Analyze > Consumer Research > Choice Models > Willingness to Pay

Winter’s Method

Fitting a seasonal moving average process for forecasting a time series.

Example Script (JSL)

Analyze > Modeling > Time Series > Smoothing Model > Winters Method

wrap (Graph Builder)

Subsets or partitions the data based on the variable or variables that you select. Wraps the data horizontally and vertically. Once a variable is placed here, no variable can be placed in Group X.

Graph > Graph Builder > Wrap

X

Term Definition Example of how to access in JMP

X,Y (Graph Builder)

Drop variables here to assign them the X or Y role

Graph > Graph Builder > X/Y

XBar chart

A plot showing a sequence of samples to detect an out-of-control situation in statistical process control.

Example Script (JSL)

Analyze > Quality and Process > Conrol Chart > XBar

Z

Term Definition Example of how to access in JMP

z test

A test on means that is appropriate if the standard deviation of the data is known. If estimates are substituted for the standard deviations, then this becomes the Student’s t test.

Analyze > Distribution > Test Mean > Enter True Standard Deviation to do z-test

z-score (z-value, normal score, standard score)

The distance between the raw score and the population mean in units of the standard deviation.

Cols > Formula

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