# 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.

Want to try out these example scripts for yourself and don't own a JMP license?

- = Capability only available in JMP Pro.
- = Denotes new capability in JMP 12.

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. | Analyze > Multivariate Methods > Multivariate > Biplot |

## 3D scatterplot | A three-dimensional spinnable view of your data. | Graph > Scatterplot 3D |

### A

Term | Definition | Example of how to access in JMP |
---|---|---|

## ABCD design | Screening design for mixtures. | 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. | 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 | A generalized regression estimation method that applies both an L1 (absolute value) and an L2 (squared) penalty to the likelihood when estimating parameters. | Analyze > Fit Model > Personality > Generalized Regression > Estimation Method |

## adaptive lasso | A generalized regression estimation method that applies an L1 (absolute value) penalty to the likelihood when estimating parameters. | 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. | Analyze > Fit Model > Personality:Standard Least Square > Leverage Plot |

## adjusted inertia | Benzecri (1979) stated that the relative inertia was a poor estimate of the quality of fit of the Multiple Correspondence Analysis solution and proposed an adjusted inertia. Greenacre (1984) argued the Benzecri adjustment overestimates the quality of fit and provided an alternate adjustment. | Analyze > Consumer Research > Multiple Correspondence Analysis |

## 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. | General |

## adjusted R
| A measure of degree of fit that has been adjusted to reflect the number of parameters in the model. Unlike the unadjusted R | 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. | 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. | DOE > Custom Design > Optimality Criterion > Make Alias Optimal Design |

## all possible models | Runs all possible models using combinations of the regression parameters specified. | 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. | 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. | 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. | Analyze > Fit Model > Personality:Standard Least Square |

## analysis of mean ranges | A chart which shows how ranges vary across groups. | 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 | Response values at the specified design settings calculated using the Anticipated Coefficients specified in the Power Analysis report. Found in the DOE Design report. | 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. | 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. | Analyze > Time Series > Autocorrelation |

## autoregression | See ARIMA. Note, autoregression models with autocorrelated and heteroscedastic errors are available in the SAS/ETS | Analyze > Modeling > Time Series |

## average chart | XBar Control Chart. Shows the process mean and its variability. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | DOE > Custom Design > Design Generation > Number of Whole Plots |

## BCI | 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. | 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. | 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. | 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. | Analyze > Multivariate Methods > principal Components > Biplot |

## Birnbaum's Component Importance (BCI) | 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 |

## Bland-Altman plot | A plot for matched pairs analysis which shows the relationship between the differences versus the means of the paired observations. | 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. | Analyze > Modeling > Neural > Boosting |

## boosting (boosted tree) | The process of building a large, additive decision tree by fitting a sequence of smaller trees. | 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. | 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. | 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. | 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 y | 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. | 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. | 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. | 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. | Graph > Bubble Plot |

## Burt table | A Burt table is a partitioned symmetric table of all pairs of categorical variables. | Analyze > Consumer Research > Multiple Correspondence Analysis > (Red Triangle) > Cross Table |

### 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. | 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. | 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. | 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. | 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. | Analyze > Quality and Process > Capability > Capability Box Plots |

## caption box (Graph Builder) | Shows a summary statistic value for the data. | 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. | Analyze > Modeling > Categorical |

## Cauchy regression | A robust regression technique. | Analyze > Fit Y By X > (Red Triangle) > Robust > Fit Cauchy |

## 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. | 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 G | Analyze > Fit Y by X > Contingency > Tests > ChiSquare |

## choice analysis | Fits choice models for market research. Conjoint Experiments. | 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. | 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. | 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. | Analyze > Distribution > Customize Summary Statistics > CV |

## coefficient of variation (C
| 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. | 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. | 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. | Analyze > Fit Y by X > Oneway > Display Options > Comparison Circles |

## concatenate data tables | Concatenate to append tables end to end. | 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. | 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. | General |

## constellation plot | Plot which shows cluster joins as points and observations as endpoints. | Analyze > Multivariate Methods > Cluster > Options = Hierarchical > OK > Constellation Plot |

## constraints for DOE factors | Defines the allowable region for factors in a designed experiment. | DOE > Custom Design > Continue > Define Factor Constraints |

## contour plot | A two-dimensional plot that displays level curves of a function or a bivariate density. | Graph > Contour Plot |

## contour plot (Graph Builder) | A two-dimensional plot that displays level curves of a function or a bivariate density. | 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. | 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. | 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) | A control chart for rare events that plots a count of units or occurrences between rare events. | 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) | 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. | 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) | 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 > 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. | 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. | 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. | 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. | Analyze > Multivariate Method > Multivariate > Covariance Matrix |

## covering array design | A design in which for any t columns all possible combinations of factor levels occur at least once. | DOE > Covering Array |

## 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. | 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. | 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. | 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 | Shows overlaid cumulative gains curves, which measure the effectiveness of a predictive model, for each level of the response. | 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. | 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. | 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 | Three-level designs where: main effects are independent of two-factor interactions; quadratic effects are estimable; second-order effects are only partially aliased. | DOE > Definitive Screening Design |

## degradation analysis | Analyzes product degradation (or deterioration) over time and anticipates product quality in the future. | 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. | 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. | 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. | Analyze > Modeling > Time Series > ADF |

## disallowed combinations | Disallows any combination of levels of categorical factors in Design of Experiments. | DOE > Custom Design > Disallowed Combinations |

## discrete choice analysis | Fits choice models for market research. Conjoint Experiments. | 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. | Analyze > Quality and Process > Variability/Attribute Gauge Chart > Gauge Studies > Discrimination Ratio |

## distance matrix | Matrix containing the distances between the observations. | 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 | 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. | 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. | 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. | 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’. | 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. | 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. | Analyze > Quality and Process > Variability/Attribute Gauge Chart > Chart Type:Attribute > Effectiveness Report |

## elastic net | A generalized regression estimation method that applies both an L1 (absolute value) and an L2 (squared) penalty to the likelihood when estimating parameters. | Analyze > Fit Model > Personality > Generalized Regression |

## ellipses - bivariate density | The contours of the bivariate normal distribution are ellipses. | Analyze > Fit Y by X > Bivariate > Density Ellipse |

## EMP Gauge R&R | A Gauge R&R analysis based on the EMP (Evaluating the Measurement Process) method developed by Donald Wheeler. | Analyze > Quality and Process > Measurement Systems Analysis > EMP Gauge RR Results |

## EMP study | An approach to evaluating a measurement process based on Donald Wheeler’s book “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 R
| 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 R |

## 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 |

## equivalence test | Equivalence tests assess whether there is a practical difference in means. | Analyze > Fit Model > (Red Triangle) > Estimates > Multiple Comparisons > Turkey HSD All Pairwise Comparisons (Red Triangle) > Equivalence Tests |

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

## estimation efficiency | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | Analyze > Multivariate Methods > Multivariate > Factor Analysis |

## factor loading plot | A scatterplot matrix where each cell is a plot of the loadings for each variable on a pair of factors. | 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) | 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 |

## fast flexible filling design | A design whose points are quasi-uniformly distributed throughout the design space. Useful when the design region is not rectangular. | 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 | 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 | 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. | 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. | 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 | 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 |

## G
| 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. | 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. | Analyze > Modeling > Gaussian Process |

## gauge chart | Chart that is used to study how a measurement process varies across gauges. | 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. | 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. | 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. | 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 R
| Generalization of the R | Analyze > Fit Model > Personality:Nominal Logistic > Whole Model Test > Generalized RSquare |

## generalized regression | 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. | 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 n | Analyze > Distribution > Summary Statistics > Customize Summary Statistics > Geometric Mean |

## goal plot | A goal plot shows a point corresponding to the mean and standard deviation of each variable, standardized to its specification limits, on X and Y axes. | 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 | Plots the hazard rate (or instantaneous failure rate) over time. | 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. | Graph > Graph Builder > Histogram |

## historical mean | Mean gathered from a previous process that can be used in computing Gauge R&R summaries. | 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. | Analyze > Fit Y by X > Oneway > Unequal Variances |

## Hotelling’s T
| 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 > T |

## 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’. | 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. | 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. | 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. | 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. | 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 | 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. | 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) |

## inertia | The total Pearson Chi-square for a two-way frequency table divided by the sum of all observations in the table. | Analyze > Consumer Research > Multiple Correspondence Analysis |

## informative missing | 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. | 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. | 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. | Analyze > Multivariate Methods > Cluster > Options:KMeans |

## K out of N node | 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. | 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. | 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. | Analyze > Fit Y by X > Contingency > Measures of Association > Kendall’s Tau-B |

## knot node | 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. | 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. | 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. | Analyze > Fit Y by X > Oneway > Nonparametric > Wilcoxon Test |

## kurtosis | The statistic that measures the 4 | 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. | 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 | A generalized regression estimation method that applies an L1 (absolute value) penalty to the likelihood when 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. | 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. | 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). | Analyze > Fit Model > Personality:Standard Least Squares |

## leave-one-out | 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. | 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 | 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. | 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. | Analyze > Quality and Process > Control Chart > Levey Jennings |

## lift curve | Similar to an ROC curve, but constructed to show the initial ordering. | 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. | 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’. | 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. | 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. | 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 | Calculates the inverse of the logistic function for the selected column. The column values must be between 0 and 1. | 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. | 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. | 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. | 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. | Analyze > Multivariate Methods > Multivariate > Factor Analysis |

## maximum R
| The maximum attainable value of R | 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. | Analyze > Fit Y by X > Contingency > Exact Test > Exact Agreement Statistic > Bowker’s Test |

## mean of a single population | The expected value of the underlying distribution for the response variable. | Analyze > Distribution > Summary Statistics > Custom Summary Statistics > Means |

## mean time to failure (MTTF) | Mean or average life in reliability analysis. | 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. | 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 |

## measurement systems analysis (MSA) | A platform that evaluates a measurement system for numeric responses using either traditional Gauge R&R or the EMP (Evaluating the Measurement Process) method. | Analyze > Quality and Process > Measurement Systems Analysis |

## 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. | Analyze > Fit Y by X > Oneway > Nonparametric > Median Test |

## minimum potential design | Space filling design that spreads points out inside a sphere. | DOE > Space Filling Design > Space Filling Design Methods:Minimum Potential |

## missing data pattern | Shows pattern of missing values in data table. | 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 | 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. | 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. | 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. | 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. | Analyze > Quality and Process > Control Chart > Control Chart Builder > Right Click:Limits > Sigma > Moving Range (Also available through Control Chart Builder) |

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

## multiple correspondence analysis | Extends correspondence analysis to more than two nominal or ordinal variables. | Analyze > Consumer Research > Multiple Correspondence Analysis |

## 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. | 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 | A screening design with orthogonal main effects. Useful when interactions are considered negligible. | 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. | Analyze > Modeling > Neural |

## NIPALS | Method for partial least squares analysis that works by extracting one factor at a time. By working on one factor at a time, NIPALS does not require calculation of the overall covariance matrix. | Analyze > Multivariate Methods > Partial Least Squares |

## 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 iterative partial least squares (NIPALS) | Method for partial least squares analysis that works by extracting one factor at a time. By working on one factor at a time, NIPALS does not require calculation of the overall covariance matrix. | Analyze > Multivariate Methods > Partial Least Squares |

## 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | Analyze > Distribution > Continuous Fit > Normal Mixtures |

## normal plot | Helps you identify effects that deviate from the normal lines. | 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 | 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. | 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 | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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 |

## penalized regression | Penalized regression methods introduce bias into the estimation of b to reduce variability in the estimates. | Analyze > Fit Model > Generalized Regression |

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

## presummarized chart | A plot showing presummarized sample means. | 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. | 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. | Analyze > Multivariate Methods > Multivariate > Principal Component |

## prior communality estimates: SMC | 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. | 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. | 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 | 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. | 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. | 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 | Analyze > Distribution > Normal Quantile Plot | |

## quadratic discriminant analysis | Classifying points using discriminant analysis with a different covariance matrix for each group. See ‘discriminant analysis’. | Analyze > Multivariate Methods > Discriminant > Discriminant Method:Quadratic, Different Covariances |

## quadratic discriminant analysis | Uses a separate covariance matrix for each group. | Analyze > Multivariate Methods > Discriminant > Discriminant Method:Quadratic, Different Covariances |

## quality | A measure of how well the Multiple Correspondence Analysis solution represents the level of a variable. | Analyze > Consumer Research > Multiple Correspondence Analysis |

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

## quantile: normal quantile plot, single population | 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 |

## R
| A measure of degree of fit, ranging from 0 (no fit) to 1 (exact fit). R | Analyze > Fit Model > Personality:Standard Least Square > Summary of Fit > RSquare |

## R
| A measure of degree of fit that has been adjusted to reflect the number of parameters in the model. Unlike the unadjusted R | Analyze > Fit Model > Personality:Standard Least Square > Summary of Fit > RSquare Adj |

## R
| 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 R |

## R
| Generalization of the R | Analyze > Fit Model > Personality:Nominal Logistic > Whole Model Test > Generalized RSquare |

## R
| The maximum attainable value of R | 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 Forest
| Creates many trees and computes the final predicted value by averaging the predicted values. | 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. | 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. | 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. | Analyze > Reliability and Survivall > Fit Proportional Hazard > Risk Ratios |

## rare events chart | 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. | 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. | 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. | 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. | 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’. | 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. | 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). | 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). | 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 | 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. | 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. | 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. | 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 | 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. | 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. | 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. | Analyze > Fit Model > Personality:Standard Least Square > Row Diagnostics > Plot Residual |

## response screening | 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. | 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 | A generalized regression estimation method that applies an L2 penalty in estimating parameters. | 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). | Analyze > Reliability and Survivall > Fit Proportional Hazard > Risk Ratios |

## robust fit (bivariate) | Fits a line using estimates for the parameters that are less sensitive to outliers than the usual least squares estimates. Uses Huber M-estimation. | Analyze > Oneway Analysis > Robust Fit |

## robust fit (oneway) | Provides estimates that are resistant to outliers. Conducts the ANOVA test using these statistics. Uses Huber M-estimation. | Analyze > Oneway Analysis > Robust Fit |

## robust mean | Provides an estimate of the mean that is resistant to outliers. Uses Huber M-estimation. | 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 | Provides an estimate of the standard deviation that is resistant to outliers. Uses Huber M-estimation. | 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 | In factor analysis, provides loadings for each variable on the rotated factors. A loading reflects the correlation between a variable and the factor. | Analyze > Consumer Research > Factor Analysis > OK > Rotated Factor Loading |

## 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. | 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 | A scatterplot matrix where each cell is a plot of the scores for each variable on a pair of factors. | Analyze > Consumer Research > Factor Analysis > OK > Score Plot |

## score summaries | Provides a table showing misclassification results for classification based on the scores. | 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. | 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. | Analyze > Modeling > Screening |

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

## SIMPLS | Method for partial least squares analysis that seeks to optimize a statistical criterion. | Analyze > Multivariate Methods > Partial Least Squares |

## 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. | 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 3 | Analyze > Fit Y by X > Fit Spline |

## solution path
| The solution path is the path that parameter estimates take to solve the estimation problem using elastic net, Lasso, or ridge regression (and their adaptive versions). | Analyze > Fit Model > Generalized Regression |

## 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 | 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. | 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. | 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 3 | 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. | 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. | DOE > Custom Design > Design Generation > Hard to change...to change factors |

## squared cosines | Squared cosines indicate the quality of each point for the dimension listed in Multiple Correspondence Analysis. | General |

## 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 | 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. | 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 |

## statistically inspired modification of PLS (SIMPLS) | Method for partial least squares analysis that seeks to optimize a statistical criterion. | Analyze > Multivariate Methods > Partial Least Squares |

## Steel with control test | Performs the Steel-Dwass test on each pair. | 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. | 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. | DOE > Custom Design > Design Generation > Hard to change...to change factors |

## Stuart’s Tau-C | 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’. | 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. | 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. | 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 | 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 |

## T
| 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 > T |

## 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. | Analyze > Distribution > Test of Proportions |

## test-retest error | Used to check for detectable differences between the different levels of the potential nuisance component. | 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. | Analyze > Distribution > Tolerance Interval |

## transfer function model | Model based on the past values of explanatory variables and residuals are modeled as an ARIMA model. | 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. | 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. | 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. | 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. | 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’. | 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. | Analyze > Fit Y by X > Contingency >Tests |

## two sample t-test | Used to determine whether two population means are equal. | 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. | 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. | 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. | 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. | 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. | 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. | |

## update data tables | Update one table with values from another table. | Tables > Update |

## uplift | 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. | 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 | A method for assessing the importance of variables that is independent of model type. Only available for continuous responses. | Predicition Profiler > Assess Variable Importance |

## variable importance for projection (VIP) | A VIP score is a measure of a variable’s importance in modeling both X and Y. | Analyze > Multivariate Methods > Partial Least Squares > (red triangle) > Variable Importance Plot |

## 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 | 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. | Graph > Graph Builder > Contour |

## VIP | A VIP score is a measure of a variable’s importance in modeling both X and Y. | Analyze > Multivariate Methods > Partial Least Squares > (red triangle) > Variable Importance Plot |

### 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. | 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. | Analyze > Quality and Process > Control Chart > XBar > XBar/R > Tests > All Tests |

## Westgard rules | Tests for special causes that follow the Westgard Rules. | 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. | 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. | 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 | How much a price must change allowing for the new feature settings to produce the same predicted outcome. | Analyze > Consumer Research > Choice Models > Willingness to Pay |

## Winter’s Method | Fitting a seasonal moving average process for forecasting a time series. | 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 | 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 |