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• One-Way ANOVA

   Use to test for a statistical differences in comparing three or more population means. [video] 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). ...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:58 PM

  838 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. [video]  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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:57 PM

  611 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

  Learning Library |

  Feb 11, 2026 12:56 PM

  467 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

  Learning Library |

  Feb 11, 2026 12:55 PM

  302 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

  Learning Library |

  Feb 11, 2026 12:54 PM

  276 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.     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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:53 PM

  391 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

  Learning Library |

  Feb 11, 2026 12:52 PM

  1025 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

  Learning Library |

  Feb 11, 2026 12:51 PM

  382 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

  Learning Library |

  Feb 11, 2026 12:51 PM

  639 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

  Learning Library |

  Feb 11, 2026 12:49 PM

  627 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

  Learning Library |

  Feb 11, 2026 12:48 PM

  525 views | 0 replies

• 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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:47 PM

  500 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.  [video] 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 triangle...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:46 PM

  354 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

  Learning Library |

  Feb 11, 2026 12:46 PM

  480 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

  Learning Library |

  Feb 11, 2026 12:45 PM

  422 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.  Categorical Note: The results displayed in the analysis demonstrated will be easier to i...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:44 PM

  366 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.  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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:43 PM

  488 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

  Learning Library |

  Feb 11, 2026 12:42 PM

  1044 views | 0 replies

• 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 ...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:41 PM

  638 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. This example involves 16 dogs assigned to different treatment groups...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:40 PM

  455 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

  Learning Library |

  Feb 11, 2026 12:39 PM

  435 views | 0 replies

• 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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:38 PM

  740 views | 0 replies

• 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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:37 PM

  1061 views | 0 replies

• 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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:36 PM

  332 views | 0 replies

• 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...

  gail_massari

  Learning Library |

  Feb 11, 2026 12:34 PM

  643 views | 0 replies