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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.                  The Upper 95% Mean and Lower 95% Mean shows the 95% confid...

  gail_massari

  Feb 11, 2026 1:01 PM

  867 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 1:00 PM

  1195 views | 0 replies

• 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, ...

  gail_massari

  Feb 11, 2026 12:59 PM

  1966 views | 0 replies

• 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. [video] 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 var...

  gail_massari

  Feb 11, 2026 12:58 PM

  930 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:58 PM

  1434 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:57 PM

  939 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:56 PM

  810 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:55 PM

  508 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:54 PM

  485 views | 0 replies

• 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.   [video]   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 clic...

  gail_massari

  Feb 11, 2026 12:53 PM

  638 views | 0 replies

• 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,...

  gail_massari

  Feb 11, 2026 12:52 PM

  1882 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:51 PM

  605 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:51 PM

  1103 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:49 PM

  1145 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:48 PM

  969 views | 0 replies

• Fit Non-Linear Curve

  Use to build non-linear models describing the relationship between an explanatory variable and an outcome variable.  [video] 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 ...

  gail_massari

  Feb 11, 2026 12:47 PM

  842 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:46 PM

  731 views | 0 replies

• Time Series Smoothing Models

  Use smoothing based time series models to describe patterns and forecast future time periods.  [video] 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...

  gail_massari

  Feb 11, 2026 12:46 PM

  765 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:45 PM

  698 views | 0 replies

• 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.  [video] Categorical Note: The results displayed in the analysis demonstrated will be easi...

  gail_massari

  Feb 11, 2026 12:44 PM

  585 views | 0 replies

• 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.  [video] 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 triang...

  gail_massari

  Feb 11, 2026 12:43 PM

  880 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:42 PM

  1780 views | 0 replies

• Clustering

  Use Hierarchical or K-Means Clustering to form clusters (groups) of observations having similar characteristics. [video] 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 ...

  gail_massari

  Feb 11, 2026 12:41 PM

  1126 views | 0 replies

• 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. [video] This example involves 16 dogs assigned to different treatment...

  gail_massari

  Feb 11, 2026 12:40 PM

  908 views | 0 replies

• 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...

  gail_massari

  Feb 11, 2026 12:39 PM

  768 views | 0 replies