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JMP® 9 - Quick Guide

Instructions presume an open data table, default preference settings, and appropriately typed, user-specified variables of interest.

RMC = Click Right Mouse Button

Download the JMP 9 Quick Guide

Graphing
Frequency Distribution	Analyze > Distribution (For categorical variables, frequencies are displayed. Otherwise, quantiles and moments are.)
Bar Chart	Graph > Graph Builder > Drag Continuous Variable to Y and Categorical to X > RMC > Points > Change to > Bar Graph > Chart
Pie Chart	Graph > Chart > Options > Pie Chart
Histogram	Analyze > Distribution
Stem and Leaf Plot	Analyze > Distribution; select Stem and Leaf
Scatter Plot 2D	Graph > Graph Builder > Drag Continuous Variable to Y and another one to X Analyze > Fit Y by X (Bivariate) Graph > Overlay Plot
Scatter Plot 3D	Graph > Scatterplot 3D
Scatter Plot Matrix	Graph > Scatterplot Matrix Analyze > Multivariate Methods > Multivariate
Trellis Plot	Graph > Graph Builder > Drag Column to Y and one to X; Drag Nominal or Ordinal Column to Wrap
Line Chart	Graph > Graph Builder > Drag Continuous Variable to Y and another one to X > RMC in graph > Smoother > Change to > Line Graph > Overlay Plot; select y options > Connect Thru Missing
Box Plot - One Level	Graph > Graph Builder > Continuous column to Y > RMC (Right Mouse Click) > Points > Change to > Box Plot Analyze > Distribution
Box Plot - Two or More Levels	Graph > Graph Builder > Continuous column to Y and categorical to X > RMC > Points > Change to > Box Plot Analyze > Fit continuous Y by categorical X; select Display Options > Box Plot

Graphing

Frequency Distribution

Analyze > Distribution (For categorical variables, frequencies are displayed. Otherwise, quantiles and moments are.)

Bar Chart

Graph > Graph Builder > Drag Continuous Variable to Y and Categorical to X > RMC > Points > Change to > Bar
Graph > Chart

Pie Chart

Graph > Chart > Options > Pie Chart

Histogram

Analyze > Distribution

Stem and Leaf Plot

Analyze > Distribution; select

Stem and Leaf

Scatter Plot 2D

Graph > Graph Builder > Drag Continuous Variable to Y and another one to X
Analyze > Fit Y by X (Bivariate)
Graph > Overlay Plot

Scatter Plot 3D

Graph > Scatterplot 3D

Scatter Plot Matrix

Graph > Scatterplot Matrix
Analyze > Multivariate Methods > Multivariate

Trellis Plot

Graph > Graph Builder > Drag Column to Y and one to X; Drag Nominal or Ordinal Column to Wrap

Line Chart

Graph > Graph Builder > Drag Continuous Variable to Y and another one to X > RMC in graph > Smoother > Change to > Line
Graph > Overlay Plot; select y options > Connect Thru Missing

Box Plot - One Level

Graph > Graph Builder > Continuous column to Y > RMC (Right Mouse Click) > Points > Change to > Box Plot
Analyze > Distribution

Box Plot - Two or More Levels

Graph > Graph Builder > Continuous column to Y and categorical to X > RMC > Points > Change to > Box Plot
Analyze > Fit continuous Y by categorical X; select Display Options > Box Plot

Basic Statistics
Descriptive statistics	Analyze > Distribution; (basic stats are shown by default; to see more select Display Options > More Moments Tables > Summary Tables > Tabulate
z- or t- test 1-Sample 2-Sample Paired t	Analyze > Distribution; select Test Mean Analyze > Fit Y by X; select t Test or Means/ANOVA/Pooled t Analyze > Matched Pairs
Testing Proportions (make 0/1 indicator Nominal or Ordinal) 1 Proportion 2 Proportions	Analyze > Distribution; select Test Probabilities Analyze > Fit Y by X
Contingency table – Chi-Square test	Analyze > Fit Y by X (both X and Y must be categorical)
Covariance	Analyze > Multivariate Methods > Multivariate; select Covariance matrix
Correlation	Analyze > Multivariate Methods > Multivariate Analyze > Fit Y by X > Density Ellipse
Test for Normality/Goodness-of-fit	Analyze > Distribution; select Continuous Fit > Normal; select by Fitted Distribution > Goodness of Fit

Basic Statistics

Descriptive statistics

Analyze > Distribution; (basic stats are shown by default; to see more select Display Options > More Moments
Tables > Summary
Tables > Tabulate

z- or t- test

1-Sample
2-Sample

Paired t

Analyze > Distribution; select Test Mean
Analyze > Fit Y by X; select t Test or Means/ANOVA/Pooled t
Analyze > Matched Pairs

Testing Proportions (make 0/1 indicator Nominal or Ordinal)

1 Proportion
2 Proportions

Analyze > Distribution; select Test Probabilities
Analyze > Fit Y by X

Contingency table – Chi-Square test

Analyze > Fit Y by X (both X and Y must be categorical)

Covariance

Analyze > Multivariate Methods > Multivariate; select

Covariance matrix

Correlation

Analyze > Multivariate Methods > Multivariate
Analyze > Fit Y by X > Density Ellipse

Test for Normality/Goodness-of-fit

Analyze > Distribution; select

Continuous Fit > Normal; select

by Fitted Distribution > Goodness of Fit

Probability/Random Variables
Probability Variables	On data table select Columns > New Column; RMC on new column > Formula; select Probability from Functions Window; select desired probability function. Note: For more information on the expected parameters see help under Probability Functions
Random Variables	On data table select Columns > New Column; RMC on new column > Column Info; Click on drop down box next to Initialize Data > Random On data table select Columns > New Column; RMC on new column > Formula; select Random from Functions Window; select desired Random function. Note: For more information on the expected parameters see help under Random Function
Distribution Fitting	Analyze > Distribution; select Continuous Fit, then select desired distribution(s).

Probability/Random Variables

Probability Variables

On data table select

Columns > New Column; RMC on new column > Formula; select Probability from Functions Window; select desired probability function. Note: For more information on the expected parameters see help under Probability Functions

Random Variables

On data table select Columns > New Column; RMC on new column > Column Info; Click on drop down box next to Initialize Data > Random
On data table select Columns > New Column; RMC on new column > Formula; select Random from Functions Window; select desired Random function.

Note: For more information on the expected parameters see help under Random Function

Distribution Fitting

Analyze > Distribution; select

Continuous Fit, then select desired distribution(s).

Analysis of Variance
One-Way	Analyze > Fit Y by X; select Means/Anova (Y must be continuous; X categorical)
Two or more Factors	Analyze > Fit Model
Randomized Blocks	Analyze > Fit Y by X; include a categorical column in Block role
Multiple Comparison Methods	Analyze > Fit Y by X; select Means/Anova; select Compare Means
Test for Equal/Unequal Variances	Analyze > Fit Y by X; select Means/Anova; select Unequal Variances

Analysis of Variance

One-Way

Analyze > Fit Y by X; select

Means/Anova (Y must be continuous; X categorical)

Two or more Factors

Analyze > Fit Model

Randomized Blocks

Analyze > Fit Y by X; include a categorical column in Block role

Multiple Comparison Methods

Analyze > Fit Y by X; select

Means/Anova; select

Compare Means

Test for Equal/Unequal Variances

Analyze > Fit Y by X; select

Means/Anova; select

Unequal Variances

Regression
Scatter Plot	Analyze > Fit Y by X (Bivariate)
Simple Least Squares One Variable One or More Independent Variables	Analyze > Fit continuous Y by continuous X; select Fit Line Analyze > Fit Model
Logistic Regression One Variable One or More Independent Variables	Analyze > Fit categorical Y by continuous X; select Fit Line Analyze > Fit Model
Multiple Regression	Analyze > Fit Model
Stepwise Regression	Analyze > Fit Model > Personality – Select Stepwise
Residual Analysis	Analyze > Fit Model; Run Model; select Row Diagnostics Analyze > Fit Y by X; select and choose a fit; select from fit report and “Save Residuals” or “Plot residuals”
Interaction Plots	Analyze > Fit Model with interaction effects; Run Model; select Factor Profiling > Interaction Plots
Durbin-Watson Test	Analyze > Fit Model; Run; select Row Diagnostics > Durban Watson Test

Regression

Scatter Plot

Analyze > Fit Y by X (Bivariate)

Simple Least Squares

One Variable
One or More Independent Variables

Analyze > Fit continuous Y by continuous X; select Fit Line
Analyze > Fit Model

Logistic Regression

One Variable
One or More Independent Variables

Analyze > Fit categorical Y by continuous X; select Fit Line
Analyze > Fit Model

Multiple Regression

Analyze > Fit Model

Stepwise Regression

Analyze > Fit Model > Personality – Select Stepwise

Residual Analysis

Analyze > Fit Model; Run Model; select Row Diagnostics
Analyze > Fit Y by X; select and choose a fit; select from fit report and “Save Residuals” or “Plot residuals”

Interaction Plots

Analyze > Fit Model with interaction effects; Run Model; select

Factor Profiling > Interaction Plots

Durbin-Watson Test

Analyze > Fit Model; Run; select

Row Diagnostics > Durban Watson Test

Nonparametric techniques
Wilcoxon Rank Sum Test	Analyze > Fit Y by X; select Nonparametric > Wilcoxon Test
Fishers Sign Test (for 2x2 tables only)	Analyze > Fit categorical Y by categorical X
Wilcoxon Signed Rank Sum Test	Analyze > Distribution on continuous X; select Test Mean > Check Wilcoxon Signed Rank Box
Kruskal-Wallis Test	Analyze > Fit continuous Y by categorical X; select Nonparametric > Wilcoxon Test
Spearman’s p	Analyze > Multivariate Methods > Multivariate; select Nonparametric Correlations > Spearman’s p

Nonparametric techniques

Wilcoxon Rank Sum Test

Analyze > Fit Y by X; select

Nonparametric > Wilcoxon Test

Fishers Sign Test (for 2x2 tables only)

Analyze > Fit categorical Y by categorical X

Wilcoxon Signed Rank Sum Test

Analyze > Distribution on continuous X; select

Test Mean > Check Wilcoxon Signed Rank Box

Kruskal-Wallis Test

Analyze > Fit continuous Y by categorical X; select

Nonparametric > Wilcoxon Test

Spearman’s p

Analyze > Multivariate Methods > Multivariate; select

Nonparametric Correlations > Spearman’s p

Time Series
Time Series Plot	Analyze > Modeling > Time Series
Moving Averages	Analyze > Modeling > Time Series; select ARIMA
Exponential Smoothing	Analyze > Modeling > Time Series; select Smoothing Models
Holt-Winters Method	Analyze > Modeling > Time Series; select Smoothing Model > Winters Method

Time Series

Time Series Plot

Analyze > Modeling > Time Series

Moving Averages

Analyze > Modeling > Time Series; select

ARIMA

Exponential Smoothing

Analyze > Modeling > Time Series; select

Smoothing Models

Holt-Winters Method

Analyze > Modeling > Time Series; select

Smoothing Model > Winters Method

Data Mining
Decision Trees	Analyze > Modeling > Partition
Clustering	Analyze > Multivariate Methods > Cluster
Neural Networks	Analyze > Modeling > Neural
Logistic & Multiple Regression	Analyze > Fit Model

Data Mining

Decision Trees

Analyze > Modeling > Partition

Clustering

Analyze > Multivariate Methods > Cluster

Neural Networks

Analyze > Modeling > Neural

Logistic & Multiple Regression

Analyze > Fit Model

Quality Control
Control Charts Run Chart X-bar Individual Measurements (IR) P Chart U Chart CUSUM	Graph > Control Chart > Run Chart Graph > Control Chart > XBar Graph > Control Chart > IR Graph > Control Chart > P Graph > Control Chart > U Graph > Control Chart > CUSUM
Pareto	Graph > Pareto Plot
Cause & Effect Diagram	Graph > Diagram
Variability Chart	Graph > Variability/Gauge Chart
Capability Capability with additional graphs on same output	Graph > Capability Graph > Control Chart > IR; check Capability Box. > OK, Fill in Specification Limits

Quality Control

Control Charts

Run Chart
X-bar
Individual Measurements (IR)
P Chart
U Chart
CUSUM

Graph > Control Chart > Run Chart
Graph > Control Chart > XBar
Graph > Control Chart > IR
Graph > Control Chart > P
Graph > Control Chart > U
Graph > Control Chart > CUSUM

Pareto

Graph > Pareto Plot

Cause & Effect Diagram

Graph > Diagram

Variability Chart

Graph > Variability/Gauge Chart

Capability
Capability with additional graphs on same output

Graph > Capability
Graph > Control Chart > IR; check Capability Box. > OK, Fill in Specification Limits

Design of Experiments (DOE)
Factorial Design	DOE > Full Factorial Design DOE > Screening Design
Screening Design	DOE > Screening Design
Response Surface Design	DOE > Response Surface Design
Sample Size and Power Calculations	DOE > Sample Size and Power

Design of Experiments (DOE)

Factorial Design

DOE > Full Factorial Design
DOE > Screening Design

Screening Design

DOE > Screening Design

Response Surface Design

DOE > Response Surface Design

Sample Size and Power Calculations

DOE > Sample Size and Power