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Latest Posts

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 21, 2025 9:56 AM
    172 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 21, 2025 9:51 AM
    168 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 21, 2025 9:05 AM
    216 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 21, 2025 8:57 AM
    88 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 21, 2025 6:37 AM
    244 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 20, 2025 12:03 PM
    134 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 20, 2025 11:49 AM
    138 views | 0 replies

  • 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 ...

    gail_massari gail_massari
    Learning Library |
    Aug 20, 2025 11:42 AM
    155 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 20, 2025 11:36 AM
    121 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 20, 2025 11:29 AM
    151 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 19, 2025 6:57 AM
    182 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 19, 2025 6:44 AM
    291 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 13, 2025 10:51 AM
    129 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 13, 2025 10:46 AM
    86 views | 0 replies

  • 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, ...

    gail_massari gail_massari
    Learning Library |
    Aug 13, 2025 10:39 AM
    118 views | 0 replies

  • 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...

    gail_massari gail_massari
    Learning Library |
    Aug 13, 2025 10:39 AM
    191 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 gail_massari
    Learning Library |
    Aug 8, 2025 8:52 AM
    289 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 gail_massari
    Learning Library |
    Aug 8, 2025 8:46 AM
    123 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:37 AM
    264 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:29 AM
    242 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:24 AM
    181 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:23 AM
    138 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:03 AM
    198 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:03 AM
    252 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 gail_massari
    Learning Library |
    Aug 8, 2025 7:02 AM
    180 views | 0 replies