Learn JMP Events

Hypothesis Tests and Confidence Intervals for Proportions

Use to estimate via a confidence interval and perform hypothesis tests for a population proportion. Confidence Intervals for Population Proportions From an open JMP^® data table, select Analyze > Distribution.Select one or more categorical variables from Select Columns, click Y, Columns (categorical variables have red or green bars).
Note: If you have summarized data (a column with counts), enter ...

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Feb 11, 2026 1:02 PM

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Two Proportions Test and Confidence Interval

Use to Estimate via a confidence interval and perform a hypothesis test for the difference between two population proportions. If comparing more than two proportions, refer to the Chi Square Tests for a Two-Way Table guide. Two Proportions Test From an open JMP^® data table, select Analyze > Fit Y by X.Choose the binary response variable for the Y, Response.Choose the 2 levels variable that def...

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Feb 11, 2026 1:01 PM

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One Sample t-Test and Confidence Interval

Use to estimate via a confidence interval or perform a hypothesis test for a population mean. Confidence Interval for the Mean From an open JMP^® data table, select Analyze > Distribution.Select one or more continuous variables from Select Columns, click Y, Columns (continuous variables have blue triangles), and click OK. Car Physical Data.jmp (Help > Sample Data Folder) The Upper...

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Feb 11, 2026 1:01 PM

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Chi Square Tests for a Two-Way Table

Use to test for independence or homogeneity of two categorical variables. If comparing only two groups with a binary outcome, refer to the Two Proportions Test and Confidence Interval guide. The Contingency Table Analysis From an open JMP^® data table, select Analyze > Fit Y by X.Click on a categorical variable from Select Columns, and click Y, Response (categorical variables have red or green b...

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Feb 11, 2026 1:00 PM

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Two Sample t-Test and Confidence Intervals

Use to Estimate via a confidence interval and perform a hypothesis test for the difference between two population means. If more than two means (more than two levels of the categorical X variable), refer to the One-Way ANOVA guide. Comparison of Two Population Means From an open JMP^® data table, select Analyze > Fit Y by X.Click on a continuous variable from Select Columns, ...

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Feb 11, 2026 12:59 PM

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Paired t-Test and CI

Use to test if the populations means of two paired (dependent or correlated) samples are statistically different. Note: The paired measurements must be stored in separate columns. Paired t-Test Using Matched Pairs From an open JMP^® data table, select Analyze > Specialized Modeling > Matched Pairs.Select two continuous variables from Select Columns, click Y, Paired Responses (continuous variables ...

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Feb 11, 2026 12:58 PM

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One-Way ANOVA

Use to test for a statistical differences in comparing three or more population means. One-Way Analysis of Variance From an open JMP^® data table, select Analyze > Fit Y by X.Click on a continuous variable from Select Columns, and Click Y, Response (continuous variables have blue triangles).Click on a categorical variable and click X, Factor (categorical variables have red or green bars). Click O...

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Feb 11, 2026 12:58 PM

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Two-Way (Factorial) ANOVA

Use to test and estimate the effect that two categorical factors and their interaction have on the population mean. From an open JMP^® data table, select Analyze > Fit Model.Click on a continuous variable from Select Columns, and click Y, Response (continuous variables have blue triangles).Click on two categorical variables from Select Columns, and click Macros, Full Factorial (categorical variab...

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Feb 11, 2026 12:57 PM

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Nonparametric Tests

This guide illustrates how to perform a variety of nonparametric tests. For information on nonparametric correlations and measures of association, see the page Nonparametric Correlations. One-Sample Nonparametric Tests From an open JMP data table, select Analyze > Distribution.Select one or more continuous variables from Select Columns, click Y, Columns, and click OK. The variable ‘Horsepower’ w...

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Feb 11, 2026 12:56 PM

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Bootstrapping

This guide provides instructions on the bootstrapping technique – a resampling method for estimating the sampling distribution of a statistic as a means to generate a confidence interval. Bootstrapping is available from many JMP reports. Bootstrapping in JMP Report Windows From an analysis platform report window, right-click on the report of interest and select Bootstrap. In this example we us...

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Feb 11, 2026 12:55 PM

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Prediction Interval

Use to produce an interval estimate of a single observation, a sample of n observations, or the sample mean and standard deviation of a sample of n observations. Prediction Interval for an Individual Observation From an open JMP data table, select Analyze > Distribution.Select one or more continuous variables from Select Columns, click Y, Columns (continuous variables have blue triangles), and cl...

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Feb 11, 2026 12:54 PM

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One Sample Equivalence Test for Mean

Use to determine if there is statistical evidence exists to demonstrate that a population mean is within a specified range (i.e., “equivalent”) to a hypothesized value. Equivalence Test for the Mean From an open JMP data table, select Analyze > Distribution.Select one or more continuous variables from Select Columns, click Y, Columns (continuous variables have blue triangles), and click OK.Fr...

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Feb 11, 2026 12:53 PM

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Correlation

This guide illustrates ways to visualize the relationship between two continuous variables and quantify the linear association via. pearson's correlation coefficient. For information on nonparametric correlations, see the Nonparametric Correlations guide. Correlation Between Two Variables From an open JMP^® data table, select Analyze > Fit Y by X.Click on a continuous variable from Select Columns,...

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Feb 11, 2026 12:52 PM

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Nonparametric Correlations

This guide illustrates how to compute nonparametric measures of association (Spearman’s Rho, Kendall’s Tau, and Hoeffding’s D). Nonparametric Correlations From an open JMP data table, select Analyze > Multivariate Methods > Multivariate.Select two or more continuous or discrete numeric (nominal or ordinal) from Select Columns, click...

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Feb 11, 2026 12:51 PM

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Simple Linear Regression

Use to model the bivariate relationship between a continuous explanatory variable with a continuous outcome variable. Useful to describe the relationship between the variables and to predict an outcome for different values of the explanatory variable. Simple Linear Regression Using Fit Y by X From an open JMP^® data table, select Analyze > Fit Y by X.Click on a continuous variable from Select Colu...

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Feb 11, 2026 12:51 PM

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Multiple Linear Regression

Use to model the relationship two or more continuous or categorical explanatory explanatory variables has with a continuous outcome variable. Useful to describe the relationships between the variables and to predict an outcome for different values of the explanatory variables. Multiple Linear Regression Using Fit Model From an open JMP^® data table, select Analyze > Fit Model.Click on a continuo...

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Feb 11, 2026 12:49 PM

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Stepwise Regression

Use to perform automated variable selection in multiple linear or logistic regression models. The method is particular useful when there is a large number of candidate explanatory variables. Stepwise Regression From an open table, select Analyze > Fit Model.Select a response variable from Select Columns and click Y.Select predictor variables and click Add.If desired, select a validation column (J...

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Feb 11, 2026 12:48 PM

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Fit Non-Linear Curve

Use to build non-linear models describing the relationship between an explanatory variable and an outcome variable. Fit Curve Select Analyze > Specialized Modeling > Fit Curve.Select a continuous variable from Select Columns, and add to Y, Response.Select a continuous explanatory variable for X, Regressor Add a categorical variable to Group to have a separate model fit for each value of a gr...

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Feb 11, 2026 12:47 PM

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ARIMA Modeling

Use ARIMA (Auto Regressive Integrated Moving Average) time series models to examine autocorrelation, describe patterns (trends and seasonality), and forecast future time periods. ARIMA Modeling From an open JMP^® data table, select Analyze > Specialized Modeling > Time Series.Select a continuous variable from Select Columns, and click Y, Time Series (continuous variables have blue triangles). Sel...

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Feb 11, 2026 12:46 PM

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Time Series Smoothing Models

Use smoothing based time series models to describe patterns and forecast future time periods. Smoothing Models From an open JMP^® data table, select Analyze > Specialized Modeling > Time Series.Select a continuous variable from Select Columns, and click Y, Time Series (continuous variables have blue triangles). Select a time and click X, Time ID (optional). Click OK.
- A time series graph of the...

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Feb 11, 2026 12:46 PM

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Time Series Forecasting

The Time Series Forecast platform builds a variety of different exponential smoothing models and automatically selects the with the best forecast performance. The platform is designed to forecast multiple time series. Time Series Forecast From an open JMP^® data table, select Analyze > Specialized Modeling > Time Series Forecast.

Select a continuous variable from Select Columns, and click Y (contin...

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Feb 11, 2026 12:45 PM

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Survey Analysis (Cross Tabulation)

Categorial platform provides myriad tools to tabulate and analyze multivariable categorical data, such as that which would come from surveys. Commonly referred to as cross-tabulation, these analysis methods can be used to compare responses across multiple factors and uncover relationships between categories. Categorical Note: The results displayed in the analysis demonstrated will be easier to i...

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Feb 11, 2026 12:44 PM

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Factor Analysis

Factor Analysis is an analysis technique that seeks to describe the variation in a set of observed variables in terms of a smaller number of unobserved latent variables or factors. Factor Analysis From an open JMP^® data table, select Analyze > Multivariate Methods > Factor Analysis.Select continuous variables from Select Columns, and Click Y, Columns (continuous variables have blue triangles).Cl...

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Feb 11, 2026 12:43 PM

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Principal Component Analysis

This guide provides instructions on performing a principal component analysis (PCA). This analysis method is often used to reduce the dimensionality of a data set (i.e., fewer variables) by creating a new set of variables that are linear combinations of the original variables statistically independent of each other and that capture the most information (i.e., variation and correlation) contained i...

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Feb 11, 2026 12:42 PM

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Clustering

Use Hierarchical or K-Means Clustering to form clusters (groups) of observations having similar characteristics. Hierarchical Clustering From an open JMP^® data table, select Analyze > Clustering > Hierarchical Cluster.Select one or more numeric variables from Select Columns and click Y, Columns. Here we used the 13 numeric variables.If available, select a Label variable.Select the desired method ...

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Feb 11, 2026 12:41 PM

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Analysis of Repeated Measures (MANOVA)

Use MANOVA (multivariate analysis of variance) for a way to analyze repeated measures data. The term repeated measures refers to data with multiple measurements taken on the same subjects, often taken over a period of time. The MANOVA platform provides tests of between and within subject effects across the repeated measurements. This example involves 16 dogs assigned to different treatment groups...

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Feb 11, 2026 12:40 PM

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Structural Equation Modeling

Use Structural Equation Modeling (SEM) to test causal theories and analyze relationships between observed variables and underlying latent constructs. SEM combines principles from factor analysis, which identifies factors from observed variables, and multiple regression analysis, which assesses how variables relate to each other. Structural Equation Modeling Note: SEM provides a framework to perf...

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Feb 11, 2026 12:39 PM

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Repeated Measures Analysis (Mixed Model)

This guide provides instructions on the analysis of repeated measures data using a mixed model (random and fixed effects) with nesting. The term repeated measures refers to data with multiple measurements taken on the same subjects, often taken over a period of time. This example involves six animal subjects randomly selected from two species. The miles traveled by each animal were measured ov...

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Feb 11, 2026 12:38 PM

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Mixed Model Analysis

Use a Mixed Model for an ANOVA or regression model with at least one factor specified as a random variable. JMP Pro® has a Mixed Model and a Generalized Linear Mixed Model platforms offering the more flexibility in fitting mixed models. This example uses standard JMP to fit an unbalanced design involving six people chosen at random to take measurements on three different machines. Analysis of L...

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Feb 11, 2026 12:37 PM

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Regression Trees (Partition)

Use to build a partition-based model (Decision Tree) that identify the most important factors that predict a continuous outcome and use the resulting tree to make prediction for new observations. Regression Trees From an open JMP^® table, select Analyze > Predictive Modeling > Partition.Select a continuous response variable from Select Columns and click Y, Response.Select explanatory variables an...

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Feb 11, 2026 12:36 PM

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Discriminant Analysis

Build a boundary based statistical model to predict a categorical outcome (classify) as a function of multiple continuous preditor variables. Discriminant Analysis From an open JMP^® data table, select Analyze > Multivariate Methods > Discriminant.Select one or more continuous variables from Select Columns, and click Y, Covariates (continuous variables have blue triangles).Click on a categorical v...

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Feb 11, 2026 12:34 PM

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Support Vector Machines - Classification

Build a boundary based statistical model to predict a categorical outcome (classify) as a function of multiple predictor variables. SVM is able to create much more flexible boundary shapes than the Classification Tree (Partition) and Discriminant Analysis method. Support Vector Machines From an open JMP^® table, select Analyze > Predictive Modeling > Support Vector Machines.Add a nominal or ordi...

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Feb 11, 2026 12:33 PM

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Support Vector Regression

Build a boundary based statistical model to predict a continuous outcome as a function of multiple predictor variables. SVR is able to create much more flexible boundary shapes than the Regression Tree (Partition) method. Support Vector Regression From an open JMP^® table, select Analyze > Predictive Modeling > Support Vector Machines.Add a continuous variable from Select Columns to the Y, ...

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Feb 11, 2026 12:32 PM

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K Nearest Neighbors

Use a proximity-based algorithm to predict a categorical outcome (classify) or prediction the value of a continuous outcome for new observations based upon the outcomes of similar observations (i.e., their nearest neighbors). K Nearest Neighbors From an open JMP^® table, select Analyze > Predictive Modeling > K Nearest Neighbors.Select a categorical or continuous response variable from Select Col...

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Feb 11, 2026 12:31 PM

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Neural Networks

Build a network based model to describe the impact that multiple predictor variables have on an outcome and to make predictions of a categorical outcome (classify) or a continuous outcome. Neural Networks From an open JMP^® data table, select Analyze > Predictive Modeling > Neural.Select a response variable from Select Columns and click Y, Response. Here we chose ‘Price’.Select explanatory variabl...

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Feb 11, 2026 12:30 PM

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Simple Logistic Regression

Use to model the relationship a continuous explanatory variable has with a categorical outcome variable. Useful for estimating the probability of the occurrence of an event for different values of the explanatory variable. Logistic Regression Using Fit Y by X From an open JMP^® data table, select Analyze > Fit Y by X.Click on a categorical variable from Select Columns, and click Y, Response (nomi...

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Feb 11, 2026 12:29 PM

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Multiple Logistic Regression

Use to model the relationship two or more continuous or categorical explanatory variables has with a categorical outcome variable. Useful for estimating the probability of the occurrence of an event for different values of the explanatory variables. Multiple Logistic Regression Using Fit Model From an open JMP^® data table, select Analyze > Fit Model.Click on a categorical variable from Select Co...

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Feb 11, 2026 12:29 PM

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Naive Bayes

Use this predictive modeling technique to predict a categorical outcome (classify) as a function of multiple predictor variables. The technique classifies observations by applying Bayes’ Theorem to conditional probabilities. Naive Bayes From an open JMP^® table, select Analyze > Predictive Modeling > Naive Bayes.Select a nominal or ordinal response variable from Select Columns and click Y, Respo...

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Feb 11, 2026 12:26 PM

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Creating a Validation Column (Holdout Sample)

Use to subset the data into a set used to build a model (training) and a set used to evaluate a model's predictive performance (validation). If multiple models are fit, the best performer on the validation data is often the one chosen. At times, a third set is used (test) to evaluate the chosen model's predictive performance on new data. This is considered to be a more accurate means to evaluate a...

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Feb 11, 2026 12:25 PM

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Model Comparison and Selection

Use this platform to summarize and compare the performance of multiple statistical models that have been fit to data. For details on fitting different statistical models, see the appropriate guides. Model Comparison – Continuous Response Example: We use the Body Fat.jmp data to predict Percent body fat. Formulas for several models, saved to the data table, are grouped under Prediction Formulas ...

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Feb 11, 2026 12:24 PM

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Text Mining – Describe Unstructured Text Data

The Text Explorer platform is used to explore frequently used words and phrases in unstructured text data such as text found in product reviews, social media posts, comment fields in surveys, incident reports, etc. Results are summarzied through frequency tables and word clouds. Tools such as recoding, combining terms, creating stop words, among others are available to clean the data and help targ...

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Feb 11, 2026 12:23 PM

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Text Mining – Sentiment Analysis

The Text Explorer platform is used to explore frequently used words and phrases in unstructured text data such as text found in product reviews, social media posts, comment fields in surveys, incident reports, etc. This guide shows how to perform a sentiment analysis – a methodology that assigns numerical scores to words and phrases with the intent of quantitatively measuring the positive and n...

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Feb 11, 2026 12:22 PM

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Text Mining – Advanced Analysis Methods

The Text Explorer platform is used to explore frequently used words and phrases in unstructured text data such as text found in product reviews, social media posts, comment fields in surveys, incident reports, etc. Additional tools are available in JMP^® Pro for further analysis. The text data must first be prepared for these analyses. See the Text Explorer – Describing Unstructured Text Data gu...

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Feb 11, 2026 12:22 PM

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Association Analysis (Market Basket Analysis)

Analyze transactional data such as product purchases and occurrence of events to identify those that are dependent upon each other or tend to occur together. Metrics such as the likelihood of items/events occuring based on the occurrence of other items/events, among others are produced. Note that the data must be in list format, where each row identifies the customer or transaction ID (in one c...

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Feb 11, 2026 12:20 PM

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Variables Control Charts – I/MR Charts

This guide provides instructions for creating I & MR (Individuals and Moving Range) control charts using the Control Chart Builder and the Control Chart platform. I/MR control charts are used to monitor a continuous variable where the data is sampled without subgroups. I/MR Charts – Control Chart Builder From an open JMP^® data table, select Analyze > Quality and Process > Control Chart Builder.Dr...

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Feb 11, 2026 12:20 PM

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Variables Control Charts – XBar & R/S Charts

This guide provides instructions for creating XBar & R or XBar & S control charts using the Control Chart Builder and the Control Chart platform. XBar & R or XBar & S control charts are used to monitor a continuous variable where the data is sampled with subgroups. XBar & R Charts – Control Chart Builder From an open JMP^®data table, select Analyze > Quality and Process > Control Chart Builder.D...

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Feb 11, 2026 12:19 PM

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Attribute Control Charts – P and NP Charts

This guide provides instructions on creating P and NP attribute control charts. P charts are often used to plot the proportion of nonconforming (defective) items per subgroup, while NP charts are often used to plot the number of nonconforming items p er subgroup. P Charts From an open JMP^® data table, select Analyze > Quality and Process > Control Chart > P Control ChartSelect one or more continu...

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Feb 11, 2026 12:18 PM

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Variables Control Charts – XBar & R/S Charts

This guide provides instructions for creating XBar & R or XBar & S control charts using the Control Chart Builder and the Control Chart platform. XBar & R or XBar & S control charts are used to monitor a continuous variable where the data is sampled with subgroups. XBar & R Charts – Control Chart Builder From an open JMP^®data table, select Analyze > Quality and Process > Control Chart Builder....

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Feb 11, 2026 12:17 PM

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Attribute Control Charts – C and U Charts

This guide provides instructions on creating C and U attribute control charts. C charts are often used to plot the number of nonconformities (or defects) in a subgroup that usually, but not necessarily, consists of one inspection unit, while U charts are often used to plot the number of nonconformities per unit, where the number of units per subgroup can vary. C Charts From an open JMP^® data ...

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Feb 11, 2026 12:16 PM

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Tolerance Interval

Use to produce an interval estimated to contain a specified proportion of a population. Tolerance Interval for Proportion (Normal Distribution) From an open JMP data table, select Analyze > Distribution.Select one or more continuous variables from Select Columns, click Y, Columns (continuous variables have blue triangles), and click OK.From the Distributions report window, select Tolerance Interv...

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Feb 11, 2026 12:15 PM

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Process Capability Analysis

This guide provides instructions on performing a capability analysis using the Distribution and Control Chart platforms. A process capability analysis is used to evaluate the performance of a process for a continuous variable to specifications (Lower and/or upper specification limit, and/or target). See Capability Analysis for Multiple Responses guide for performing process capability analyses on ...

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Feb 11, 2026 12:14 PM

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Capability Analysis for Multiple Responses

This guide provides instructions on performing a process capability analysis for multiple continuous process variables simultaneously. This platform produces a set of reports and graphs that are designed to compare the performance of the different process variables to each other. The Capability Analysis Dialog From a...

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Feb 11, 2026 12:12 PM

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MSA Continuous Data – Gauge R&R

This guide provides instructions on creating a variability chart and performing a gauge R&R (Repeatability and Reproducibility) measurement system analysis (MSA). This analysis is designed to evaluate and certify the performance of a measurement system that produces continuous data. For information on using the EMP (Evaluating the Measurement Process) method, see the MSA Continuous Data – EMP Meth...

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Feb 11, 2026 12:12 PM

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MSA Continuous Data – EMP Method

This guide provides instructions on performing a measurement system analysis (MSA) using the EMP (Evaluating the Measurement Process) method. This analysis is designed to evaluate and certify the performance of a measurement system that produces continuous data. Measurement Systems Analysis: EMP Method Select Analyze > Quality and Process > Measurement System Analysis.Click on a continuous variab...

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Feb 11, 2026 12:11 PM

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MSA Attribute Data

Use to evaluate the performance of a categorical based measurement system (e.g., pass or fail, classification of parts into multiple categories, ordinal ratings). This analysis is often used to assess the ability of observers (appraisers) to correctly and consistently classify inspected items. Attribute Measurement Systems Analysis Select Analyze > Quality and Process > Variability / Attribute G...

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Feb 11, 2026 12:10 PM

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Distribution Fitting (Life Distribution)

Use to model a single continuous variable with a probability distribution and estimate a variety of parameters (e.g., mean, percentiles, probabilities). This platform is designed to fit and compare many different distributions and can analyze data that is censored (incomplete). Note: Though frequently used to analyze time-to-event data, the analysis methods can be used to model any continous varia...

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Feb 11, 2026 12:09 PM

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Survival Analysis (Kaplan-Meier Estimation)

Use to estimate survival (or failure) rates. Kaplan-Meier estimation methods (also known as product limit estimation) is a non-parametric method and thus does not require many assumptions typically required in parametric methods (e.g., not needing to assume a specific probabiliy distribution to model the data). The method can be used with censored (incomplete) data. Note: Though frequently used ...

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Feb 11, 2026 12:08 PM

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Accelerated Life Testing (Fit Life by X)

Use to model the relationship a factor has on the time-to-event. It is often used to model a factor that accelerates the event as a means to predict the time-to-event for normal operating conditions where failures do not frequently occur. This platform is able to analyze data that is censored (incomplete). Accelerated Testing (Fit Life by X) From an open JMP^® data table, select Analyze > Reliabi...

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Feb 11, 2026 12:07 PM

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DOE Full Factorial Design

This guide provides instructions on designing a full factorial experiment. A full factorial experiment in where every possible treatment combination will be studied. For analysis of full factorial experiments, see the DOE Full Factorial Analysis guide. Create the Design (Full Factorial Design) Open the platform under DOE > Classical > Full Factorial Design.Specify the Response(s): Double-click on...

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Feb 11, 2026 12:06 PM

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DOE Full Factorial Analysis

This guide provides information on analyzing a full factorial experiment (experiments where every possible treatment combination is run). For instructions on designing of full factorial experiments, see the DOE Full Factorial Design guide. Specify the Model and Analyze From an open JMP^® table (for a completed full factorial experiment) select Analyze > Fit Model.
Note: The Fit Model platform can a...

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Feb 11, 2026 12:05 PM

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DOE Fractional Factorial Design

This guide provides instructions on designing a fractional factorial experiment using the Screening Design platform. Fractional factorial experiments are used when it's more efficient to not perform an experimental run for every possible treatment combination as is done in a full factorial design. Create the Design (Screening Design) Open the platform under DOE > Classical > Two Level Screening...

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Feb 11, 2026 12:04 PM

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DOE Fractional Factorial Analysis

This guide provides instructions on analyzing fractional factorial experiments (experiments where not every possible treatment combination in a full factorial design is run) using the Fit Model platform.
Specify the Model and Analyze Experiments designed in JMP^® will have a Model script saved to the data table. The model specification window will be populated with this model. To generate the model...

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Feb 11, 2026 12:03 PM

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DOE Screening Experiment Analysis

This guide provides instructions on analyzing screening experiments (e.g., fractional factorial) using the Fit Two Level Screening platform. Though the Fit Model platform can also be used, the Fit Two Level Screening platform produces graphs and analysis results designed for identifying the important effects in a fully saturated experiment. This example is a 20 run 2^5 screening experiment generat...

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Feb 11, 2026 12:01 PM

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DOE - Custom Designs

This guide provides instructions on designing optimal experiments using the flexible Custom Design platform. The Custom Designer can be used for almost any experimental situation, including factor screening, optimization, and mixture problems, and can accommodate designs with hard-to-change factors and other constraints. DOE: Generating A Custom Design Select DOE > Custom Design.Under Responses, ...

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Feb 11, 2026 12:01 PM

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Navigating the JMP interface in Microsoft Windows

This guide provides information on the Windows JMP interface. For information on creating a new data table, opening data tables, and finding help within JMP see Opening JMP and Getting Started. The Home Window When you first open JMP, you’ll see the Tip of the Day window.You’ll also see the JMP Home Window, which includes: Menus and toolbars (top).Recently used files (on the left).All open dat...

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Feb 11, 2026 11:59 AM

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Evaluate and Compare Designs

Use the Evaluate Design platform to assess the properties of an experimental design. To compare alternative designs, use the platform Compare Designs. Evaluate Design Most DOE platforms in JMP provide an Evaluate Design outline to assess a design before making the design table to run the experiment. The platform Evaluate Design shows the same diagnostics for any existing design, and can also be u...

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Feb 10, 2026 1:42 PM

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Monte Carlo Simulation

The JMP Profiler, with the Monte Carlo Simulator, can be used to optimize process performance in the presence of random variation. This enables you to estimate response distributions as a function of real-world random variation. Monte Carlo simulation is available from JMP Prediction Profilers using the Simulate red triangle option. Example Setup ...

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Feb 10, 2026 1:41 PM

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Bubble Plot

Use a Bubble Plot to explore the relationship between two continuous variables and how the values change over time. Bubble color and size can be used to incorporate additional variables. Bubble Plot From an open JMP® data table select Graph > Bubble Plot.Drag a continuous variable into the Y and X roles.Place one or more variables into the ID role so that labels can be added to the bubbles (opti...

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Feb 10, 2026 1:39 PM

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Summarizing Data Using Tabulate

The Tabulate platform provides a tool to interactively construct tables of descriptive statistics. Drag and Drop to Summarize Data From an open JMP^® data table select Analyze > Tabulate.Drag and drop variables from the column list to the drop zone for rows and columns. Country (below, left) is in the rows drop zone – the number of observations per country is displayed.Horsepower (middle) is i...

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Feb 10, 2026 1:38 PM

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Classification Trees (Partition)

Use to build a partition-based model (Decision Tree) that identifies the most important factors that predict a categorical outcome (classify) and use the resulting tree to make predictions for new observations.
Classification Trees From an open JMP^® table, select Analyze > Predictive Modeling > Partition.Select a nominal or ordinal response variable from Select Columns and click Y, Response.Selec...

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Feb 10, 2026 1:38 PM

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Sample Size and Power for Two Sample Means

Use to interactively explore the relationships between Power, Sample Sizes, and Differences to Detect for testing a hypothesis comparing two population means. See the Two Sample t-test and Confidence Interval guide to learn how to perform a statistical test comparing two sample means. Sample Size and Power - Two Sample Means Select DOE > Sample Size Explorers and choose Power > Power for Two Inde...

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Feb 10, 2026 1:36 PM

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Sample Size and Power for One Sample Mean

Use to interactively explore the relationships between Power, Sample Size, and Difference to Detect in testing a hypothesis for a single population mean. See the One Sample t-test and Confidence Interval guide to learn how to perform a statistical test for a population mean. Sample Size and Power - One Sample Mean Select DOE > Sample Size Explorers and choose Power > Power for One Sample Mean. Ch...

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Feb 10, 2026 1:35 PM

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Box Plots

Use to display the distribution of continuous variables. Boxplots are based upon a set of summary statistics that describe the center and spread of data. Boxplots are a very useful way for comparing data bewteen groups. Box Plots – One Variable From an open JMP^® data table, select Analyze > Distribution.Click on one or more continuous variables from Select Columns, and Click Y, Columns (continuou...

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Feb 10, 2026 1:34 PM

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Scatterplots

Use to display the relationship between two continuous variables. Multiple scatterplots can be displayed together in a matrix plot. Scatterplots – Two Variables From an open JMP^® data table, select Analyze > Fit Y by X.Click on a continuous response (or dependent) variable in Select Columns, and Click Y, Columns.Click on a continuous predictor (or independent) variable, and click X, Factor. Click...

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Feb 10, 2026 1:33 PM

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Mapping in Graph Builder

Use the Graph Builder to create interactive maps of U.S. states, U.S. counties, and worldwide countries and provinces. JMP® ships with these shape files. Other shape files (e.g., ESRI) can be used or you create your own custom maps. Basic Mapping From an open JMP data table, select Graph > Graph Builder.Drag a shape variable from the Variables list (for example, State) to main drop zone.Drag vari...

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Feb 10, 2026 1:33 PM

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