# JMP Teaching Materials

The SAS Global Academic Program provides JMP course materials to qualified professors teaching degree seeking students. The teaching materials are an electronic copy of the content used in SAS Education’s corporate JMP courses, including chapter-by-chapter instructor’s notes, PowerPoint slides, data sets, and practice exercises.

Instructors may use some or all of materials provided, and can tailor the materials to meet the needs of the class. Click below to review the contents of each course.

Note: Select e-Learning courses are freely available to all faculty and students covered under an annual site license.

### JMP Software: Data Exploration

##### Introduction

Introducing JMP features, exploring user-assistance options, accessing data and opening different types of files in JMP, understanding modeling types, introducing the Table panel, Columns panel, and Rows panel, creating a JMP journal

##### Data Exploration and Manipulation

Using the Columns and Rows menus, creating new columns, creating new tables, using the Tabulate option to create summary tables

##### Graphical Data Exploration

Exploring relationships between continuous columns, using the Graph Builder, mapping with the Graph Builder, exploring relationships between categorical data columns, using recursive partitioning for exploratory analysis

##### Reporting and Presenting Results

Modifying a journal, using a journal for presentations, saving and sharing results

Note: This is the classic course, based on JMP 11. As of JMP 12 a case-study based version of this data exploration course is available.

### JMP Software: A Case Study Approach to Data Exploration

##### Introduction

Creating a data table in JMP, setting modeling types, exploring data with the Graph Builder, using the Map function in Graph Builder

##### Banking Case Study

Importing data from Excel, creating formulas, graphing data to answer questions, generating and presenting the results

##### Pharmaceutical Case Study

Using graphs to identify potential data problems, graphing data to answer questions, using the Column Switcher to animate graphs, create new tables with Stack and Join

##### Manufacturing Case Study

Identify questions to ask of data, create new tables to answer questions, save output to a journal

### JMP Software: ANOVA and Regression

##### Introduction to Statistics

Reviewing populations and samples, exploring data with descriptive statistics and graphs, generating and understanding confidence intervals, understanding statistical hypothesis tests

##### Comparing Means

Analyzing a one-sample t-test, analyzing paired data, comparing two independent groups using a t-test

##### Analysis of Variance

Fitting one-way ANOVA models, fitting one-way ANOVA models with multiple levels, performing multiple comparisons, examining ANOVA assumptions, fitting N-way ANOVA models, analyzing power and sample size

##### Simple Linear Regression

Using scatterplots and correlation statistics, performing simple linear regression, fitting polynomial regression models

##### Multiple Regression

Performing multiple regression, performing multiple regression with interactions, performing stepwise regression

##### Regression Diagnostics

Examining model assumptions, discovering multivariate outliers, investigating collinearity

##### Analysis of Covariance

Fitting ANCOVA models with and without interactions

### JMP Software: Introduction to Categorical Data Analysis

##### Associations

Recognizing the difference between continuous and categorical data, examining associations among variables, conducting hypothesis tests of association, interpreting a correspondence plot, performing recursive partitioning

##### Logistic Regression

Introducing likelihood and maximum likelihood estimation, introducing logit transformation, performing logistic regression including odds ratio analysis, fitting ordinal logistic regression models, fitting nominal logistic regression models

##### Generalized Linear Models

Introducing generalized linear models, using a GLM for a binary response, using a GLM for counts

##### Categorical Platform (Self-Study)

Special cases handled by Categorical platform, using Categorical for survey data

### JMP Software: Analyzing Multidimensional Data

##### Introduction to Multivariate Data

Examples of multidimensional data, review of matrix algebra

##### Principal Components Analysis: Dimension Reduction

Interpretation of principal components, finding principal components

##### Factor Analysis: Finding Latent Factors

Factor extraction, factor rotation

##### Cluster Analysis: Grouping Data

Introduction to cluster analysis, hierarchical clustering, k-means clustering

### JMP Software: Modeling Multidimensional Data

##### Introduction to Multivariate Data

Examples of supervised learning, review of matrix algebra

##### Discriminant Analysis

Geometry of discrimination, linear and quadratic discrimination, variable selection, validation, classification

##### Principal Components Regression

Review of principal components, principal component regression, variable selection

##### Partial Least Squares Regression

PLS algorithms, PLS regression

### JMP Software: Predictive Modeling Using JMP Pro

##### Introduction to Predictive Modeling

Supervised classification, regression, honest assessment using holdout data, assessment plots and statistics

##### Classification and Regression Trees Using the Partition Platform

Optimizing model complexity, interpreting models, model fit statistics, statistical graphics, boosted trees, random forest

##### Predictive Modeling with Neural Networks

Optimizing model complexity, interpreting models, model fit statistics, statistical graphics, boosted neural networks

##### Model Implementation

Scoring new data

### JMP Software: Classic Design of Experiments

##### Introduction to Design and Analysis of Experiments

Review of basic statistical concepts, introduction to experimental design, completely randomized designs

##### Multiple Factor Designs and Blocking

Randomized complete block designs, randomized incomplete block designs, full factorial designs, 2k factorial designs, split-plot designs

##### Screening Designs

Fractional factorial designs, blocking with screening designs

##### Response Surface Methodology

Concepts and terms, classic response surface designs for second-order models, steepest ascent method

##### Custom Designs

Custom design generation, custom response surface designs

### JMP Software: Custom Design of Experiments

##### Introduction

Characteristics and benefits of custom design, introduction to custom design interface and training simulators

##### Experiments without Factors

Information in binary or continuous response, location and spread statistics

##### Experiments with One Factor

Categorical and continuous factors, sample size and balanced testing, test point spacing and replication, model verification and design augmentation, standard errors of parameter estimates and prediction variance, optimality criteria

##### Experiments with Two Factors

More information without adding resources, blocked runs for homogeneous response, the interaction effect, response surface models, grouped runs and restricted randomization (split-plot design)

##### Multi-Factor Case Studies

Optimal factor settings for multiple responses, team activity: independently solve design and analysis problems, simulation of live test results

### JMP Software: Modern Screening Designs

##### Need for Screening

Learning the important principles in screening such as: the impact that choice of design has on the variance of estimates and bias in model prediction, inflation of variance in parameter estimates due to correlation, and estimation efficiency as measured by the estimate's confidence interval size

##### Planning Experiments

With classic screening solutions including fractional factorial and Plackett-Burman designs

##### Combinatorial Screening Designs

Planning experiments with orthogonal or near-orthogonal arrays, planning experiments with definitive screening designs, planning experiments with custom designs, like Bayesian D-optimal designs and split-plot structures for restricted randomization

##### Model Selection in Screening

Identifying active factors using the effect screening tools in the Fit Least Squares platform, selecting models with the Screening platform, selecting models with forward step-wise regression, selecting models with all-subsets with heredity restrictions

### JMP Software: Design and Analysis of Mixture Experiments

##### Introduction to Mixture Designs

Review of experimental design principles, using factorial-type designs for mixture scenarios

##### Basic Mixture Designs

Simplex-lattice design, simplex-centroid design, ABCD design

##### Analysis of Mixture Designs

Linear Scheffe model, quadratic Scheffe model, cubic Scheffe model, Cox model

##### Advanced Mixture Designs

Constrained mixture designs, analysis of constrained mixture designs, combined mixture-process designs

### JMP Software: Measurement Systems Analysis

##### Introduction

Introduction to measurement systems, discrimination, bias and variability of a measurement system, evaluation of the measurement process (EMP) and gauge repeatability and reproducibility (R&R) methodologies

##### Variability – EMP

Graphical and statistical analysis for estimation of repeatability, graphical and statistical analysis for estimation of reproducibility, evaluating the impact of measurement system variability on the process, gauge study models

##### Variability - Gauge R&R

Graphical and statistical analysis for more complex models, gauge study design

##### Bias

Evaluating bias and linearity of a measurement system

##### Attribute Gauge Studies

Graphical analysis, rater agreement

### JMP Software: Statistical Process Control

##### Introduction to Statistical Quality Control

Reviewing basic statistical concepts, exploring concepts in statistical quality control, identifying control charts for variable and attribute data

##### Control Charts for Variable Data

Understanding variable data, constructing and interpreting mean, range, and standard deviation charts, saving and retrieving control limits, generating and interpreting Operating Characteristic curves, constructing and interpreting Individual and Moving Range charts, constructing and interpreting three-way control charts for batch processing situations

##### Control Charts for Attribute Data

Understanding attribute data, reviewing the binomial distribution, generating and interpreting P and NP charts, reviewing the Poisson distribution, generating and interpreting C and U charts

##### Pareto Charts

Generating and interpreting Pareto charts

##### Process Capability

Understanding process capability analysis, comparing control limits to specification limits, calculating and interpreting capability indices

##### Ishikawa diagrams

Performing and interpreting Ishikawa diagrams (also known as Fishbone or Cause-and-Effect diagrams), performing and interpreting related nested-relationship diagrams

### JMP Software: Reliability Analysis for Non-Repairable Systems

##### Introduction to Reliability

Understanding principles of reliability, measures of reliability, and the nature of life data and censored observations, using nonparametric estimation of failure probabilities, applying parametric models of failure reliability and hazard, accounting for uncertainty about reliability estimates

##### Reliability with Factors

Identifying failure modes and competing causes, describing deficient data, including the effect of covariates in the reliability model

##### Rapid Reliability

Understanding stress factors and accelerated failures, computing acceleration factors, using acceleration relationships, designing accelerated life tests, performing degradation analysis for repeated measures and destructive tests

### JMP Software: Introduction to the JMP Scripting Language

##### Scripting Concepts

Introducing scripting, introducing object-oriented approach, reviewing how to generate scripts interactively, storing scripts in the Table panel

##### JSL Building Blocks

Understanding operators, numbers, and names, understanding lists and expressions, understanding comments, commas, parentheses, quoted strings, and matrices, introducing a visual JSL style to make reading, interpreting, and understanding JSL easy

##### Functions

Using the For, While, Break, and Continue functions, using the If, Match, and Choose functions

##### Data Table Scripts

Creating new tables and columns, changing or adding column properties, using basic matrix functions in JSL

##### Platform Scripts

Understanding platform layers (analysis and report), reporting display tree structure, building custom windows

##### Dialogs, List, and Custom Menu Items

Making and using custom dialogs, understanding substitution in expressions, lists, and strings, adding a custom menu item

### JMP Software: Designing and Building a Complete JMP Script

##### Script Design

Workflow for designing and building a script, introduction to case study for complete script, identification of the data for the script

##### Data Import Design

Importing flat files, importing data stored in a relational database

##### Data Preparation

Formatting and shaping data, cleansing data, processing text

##### Building Analytics, Reports, and Visualizations

Build the framework, build the expressions, review complete script

##### Best Practices

Review practices the improve script quality while reducing effort and mistakes

### JMP Software: Application Development Using the JMP Scripting Language

##### Introduction

Introduction to JMP applications, motivating scenario for the course

##### Instant Applications

Developing applications interactively, generalizing applications

##### Custom Applications

JSL essentials, single module applications, multiple module applications

##### Application Deployment

Deploying applications

### JMP Clinical: Monitoring Clinical Trials

##### Introduction

Overview of JMP Clinical, getting started with JMP Clinical

##### Creating and Using a Study Review

Using the Review Builder, viewing results

# Teaching Materials from SAS

The SAS Global Academic Program provides JMP course materials to qualified professors teaching degree seeking students. The teaching materials include an electronic copy of the content used in SAS Education’s corporate JMP courses:

- Chapter-by-chapter instructor’s notes.
- PowerPoint slides for easily presenting the material to your students.
- Course data sets for easily demonstrating the concepts being discussed.
- Practice exercises to enhance your students’ comprehension.

Instructors may use some or all of materials provided, and can tailor the materials to meet the needs of the class. Teaching materials include those directed at new users as well as more advanced topics. Click below to review the contents of each course.

### JMP Software: Analyzing Multidimensional Data

#### Introduction to Multivariate Data

- Examples of multidimensional data
- Review of matrix algebra

#### Principal Components Analysis: Dimension Reduction

- Interpretation of principal components
- Finding principal components

#### Factor Analysis: Finding Latent Factors

- Factor extraction
- Factor rotation

#### Cluster Analysis: Grouping Data

- Introduction to cluster analysis
- Hierarchical clustering
- K-means clustering

### JMP Software: ANOVA and Regression

#### Introduction to Statistics

- Reviewing populations and samples
- Exploring data with descriptive statistics and graphs
- Generating and understanding confidence intervals
- Understanding statistical hypothesis tests

#### Comparing Means

- Analyzing a one-sample t-test
- Analyzing paired data
- Comparing two independent groups using a t-test

#### Analysis of Variance

- Fitting one-way ANOVA models
- Fitting one-way ANOVA models with multiple levels
- Performing multiple comparisons
- Examining ANOVA assumptions
- Fitting N-way ANOVA models
- Analyzing power and sample size

#### Simple Linear Regression

- Using scatterplots and correlation statistics
- Performing simple linear regression
- Fitting polynomial regression models

#### Multiple Regression

- Performing multiple regression
- Performing multiple regression with interactions
- Performing stepwise regression

#### Regression Diagnostics

- Examining model assumptions
- Discovering multivariate outliers
- Investigating collinearity

#### Analysis of Covariance

- Fitting ANCOVA models with and without interactions

### JMP Software: Analysis of Dose-Response Curves

#### Overview

- Analyzing data from assays and dose-response curves

#### Introduction

- Distinguishing linear and nonlinear models

#### Estimation

- Determining unknown potency relative to a standard

#### Validation

- Checking results against assumptions

#### Methods

- Computing parameter estimates and statistics, as well as relative potency

#### Basic Example

- Using a simple, one-time curve fit

#### Intermediate Example

- Using a template for routine curve fitting

#### Advanced Example

- Using custom applications for complex curve fitting

#### Nonlinear design

- Designing the concentrations and assay replication that make the curve fitting optimal

### JMP Software: Application Development Using the JMP Scripting Language

#### Introduction

- Introduction to JMP applications
- Motivating scenario for the course

#### Instant Applications

- Developing applications interactively
- Generalizing applications

#### Custom Applications

- JSL essentials
- Single module applications
- Multiple module applications

#### Application Deployment

- Deploying applications

### JMP Software: Classic Design of Experiments

#### Introduction to Design and Analysis of Experiments

- Review of basic statistical concepts
- Introduction to experimental design
- Completely randomized designs

#### Multiple Factor Designs and Blocking

- Randomized complete block designs
- Randomized incomplete block designs
- Full factorial designs
- 2k factorial designs
- Split-plot designs

#### Screening Designs

- Fractional factorial designs
- Blocking with screening designs

#### Response Surface Methodology

- Concepts and terms
- Classic response surface designs for second-order models
- Steepest ascent method

#### Custom Designs

- Custom design generation
- Custom response surface designs

### JMP Software: Custom Design of Experiments

#### Introduction

- Characteristics and benefits of custom design
- Introduction to custom design interface and training simulators

#### Experiments without Factors

- Information in binary or continuous response
- Location and spread statistics

#### Experiments with One Factor

- Categorical and continuous factors
- Sample size and balanced testing
- Test point spacing and replication
- Model verification and design augmentation
- Standard errors of parameter estimates and prediction variance
- Optimality criteria

#### Experiments with Two Factors

- More information without adding resources
- Blocked runs for homogeneous response
- The interaction effect
- Response surface models
- Grouped runs and restricted randomization (split-plot design)

#### Multi-Factor Case Studies

- Optimal factor settings for multiple responses
- Team activity: independently solve design and analysis problems
- Simulation of live test results

### JMP Software: Data Exploration

#### Introduction

- Introducing JMP features
- Exploring user-assistance options
- Accessing data and opening different types of files in JMP
- Understanding modeling types
- Introducing the Table panel, Columns panel, and Rows panel
- Creating a JMP journal

#### Data Exploration and Manipulation

- Using the Columns and Rows menus
- Creating new columns
- Creating new tables
- Using the Tabulate option to create summary tables

#### Graphical Data Exploration

- Exploring relationships between continuous columns
- Using the Graph Builder
- Mapping with the Graph Builder
- Exploring relationships between categorical data columns
- Using recursive partitioning for exploratory analysis

#### Reporting and Presenting Results

- Modifying a journal
- Using a journal for presentations
- Saving and sharing results

### JMP Software: Design and Analysis of Mixture Experiments

#### Introduction to Mixture Designs

- Review of experimental design principles
- Using factorial-type designs for mixture scenarios

#### Basic Mixture Designs

- Simplex-lattice design
- Simplex-centroid design
- ABCD design

#### Analysis of Mixture Designs

- Linear Scheffe model
- Quadratic Scheffe model
- Cubic Scheffe model
- Cox model

#### Advanced Mixture Designs

- Constrained mixture designs
- Analysis of constrained mixture designs
- Combined mixture-process designs

### JMP Software: Instant Applications Using JMP Application Builder

#### Introduction

- Introduction to JMP applications
- Motivating scenario for the course

#### Instant Applications

- Developing applications interactively
- Generalizing applications

#### Application Deployment

- Deploying applications

### JMP Software: Introduction to Categorical Data Analysis

#### Associations

- Recognizing the difference between continuous and categorical data
- Examining associations among variables
- Conducting hypothesis tests of association
- Interpreting a correspondence plot
- Performing recursive partitioning

#### Logistic Regression

- Introducing likelihood and maximum likelihood estimation
- Introducing logit transformation
- Performing logistic regression including odds ratio analysis
- Fitting ordinal logistic regression models
- Fitting nominal logistic regression models

#### Generalized Linear Models

- Introducing generalized linear models
- Using a GLM for a binary response
- Using a GLM for counts

#### Categorical Platform (Self-Study)

- Special cases handled by Categorical platform
- Using Categorical for survey data

### JMP Software: Introduction to the JMP Scripting Language

#### Scripting Concepts

- Introducing scripting
- Introducing object-oriented approach
- Reviewing how to generate scripts interactively
- Storing scripts in the Table panel

#### JSL Building Blocks

- Understanding operators, numbers, and names
- Understanding lists and expressions
- Understanding comments, commas, parentheses, quoted strings, and matrices
- Introducing a visual JSL style to make reading, interpreting, and understanding JSL easy

#### Functions

- Using the For, While, Break, and Continue functions
- Using the If, Match, and Choose functions

#### Data Table Scripts

- Creating new tables and columns
- Changing or adding column properties
- Using basic matrix functions in JSL

#### Platform Scripts

- Understanding platform layers (analysis and report)
- Reporting display tree structure
- Building custom windows

#### Dialogs, List, and Custom Menu Items

- Making and using custom dialogs
- Understanding substitution in expressions, lists, and strings
- Adding a custom menu item

### JMP Software: Measurement Systems Analysis

#### Introduction

- Introduction to measurement systems
- Discrimination, bias and variability of a measurement system
- Evaluation of the measurement process (EMP) and gauge repeatability and reproducibility (R&R) methodologies

#### Variability - EMP

- Graphical and statistical analysis for estimation of repeatability
- Ggraphical and statistical analysis for estimation of reproducibility
- Evaluating the impact of measurement system variability on the process
- Gauge study models

#### Variability - Gauge R&R

- Graphical and statistical analysis for more complex models
- Gauge study design

#### Partial Least Squares Regression

- PLS algorithms
- PLS regression

#### Bias

- Evaluating bias and linearity of a measurement system

#### Attribute Gauge Studies

- Graphical analysis
- Rater agreement

### JMP Software: Modeling Multidimensional Data

#### Introduction to Multivariate Data

- Examples of supervised learning
- Review of matrix algebra

#### Discriminant Analysis

- Geometry of discrimination
- Linear and quadratic discrimination
- Variable selection
- Validation
- Classification

#### Principal Components Regression

- Review of principal components
- Principal component regression
- Variable selection

#### Partial Least Squares Regression

- PLS algorithms
- PLS regression

### JMP Software: Modern Screening Designs

#### Need for Screening

- Learning the important principles in screening such as: the impact that choice of design has on the variance of estimates and bias in model prediction, inflation of variance in parameter estimates due to correlation, and estimation efficiency as measured by the estimate's confidence interval size
- Planning experiments with classic screening solutions including fractional factorial and Plackett-Burman designs

#### Combinatorial Screening Designs

- Planning experiments with orthogonal or near-orthogonal arrays
- Planning experiments with definitive screening designs
- Planning experiments with custom designs, like Bayesian D-optimal designs and split-plot structures for restricted randomization

#### Model Selection in Screening

- Identifying active factors using the effect screening tools in the Fit Least Squares platform
- Selecting models with the Screening platform
- Selecting models with forward step-wise regression
- Selecting models with all-subsets with heredity restrictions

### JMP Software: Predictive Modeling Using JMP Pro

#### Introduction to Predictive Modeling

- Supervised classification
- Regression
- Honest assessment using holdout data
- Assessment plots anf statistics

#### Predictive Modeling with a Classification and Regression Trees Using the Partition Platform

- Optimizing model complexity
- Interpreting models
- Model fit statistics
- Statistical graphics
- Boosted trees
- Random forest

#### Predictive Modeling with Neural Networks

- Optimizing model complexity
- Interpreting models
- Model fit statistics
- Statistical graphics
- Boosted neural networks

#### Model Implementation

- Scoring new data

### JMP Software: Reliability Analysis for Non-Repairable Systems

#### Introduction to Reliability

- Understanding principles of reliability, measures of reliability, and the nature of life data and censored observations
- Using nonparametric estimation of failure probabilities
- Applying parametric models of failure reliability and hazard
- Accounting for uncertainty about reliability estimates

#### Reliability with Factors

- Identifying failure modes and competing causes
- Describing deficient data
- Including the effect of covariates in the reliability model

#### Rapid Reliability

- Understanding stress factors and accelerated failures
- Computing acceleration factors
- Using acceleration relationships
- Designing accelerated life tests
- Performing degradation analysis for repeated measures and destructive tests

### JMP Software: Stability Analysis

#### FDA Regulations, ICH Guidances, and guidance documents from the PhRMA CMC Statistics Group

- Review Stabilty Expert Team findings for definition of stability and scope of studies
- Relate requirements in 21 CFR parts to stability assessment and determination of expiry
- Use ICH guidance Q1A (R2) Stability Testing and New Drug Substances and Products
- Use ICH guidance Q1E Evaluation of Stability Data

#### Stability Studies Based on the Product Life Cycle

- Present a framework for the progression of stability studies
- Identify necessary stability studies for submissions for pre-clinical, clinical, and commercial batches of drug substances and products

#### Estimate Expiry from Accelerated Degradation Tests

- Learn to select and fit a degradation model
- Learn to extrapolate to long-term storage conditions

#### Determining Poolability and Verifing Expiry of Clinical and Commercial Batches

- Learn to use recommended methods
- Perform simple linear regression and inverse prediction
- Perform analysis of covariance (ANCOVA)
- Confidence, prediction, and tolerance intervals

#### Determining Out-of-Specification Batches and Alerts for Out-of-Trend Batches

- Learn to use recommended methods such as by time point method, slope control chart method, and regression control chart method

### JMP Software: Statistical Process Control

#### Introduction to Statistical Quality Control

- Reviewing basic statistical concepts
- Exploring concepts in statistical quality control
- Identifying control charts for variable and attribute data

#### Control Charts for Variable Data

- Understanding variable data
- Constructing and interpreting mean, range, and standard deviation charts
- Saving and retrieving control limits
- Generating and interpreting Operating Characteristic curves
- Constructing and interpreting Individual and Moving Range charts
- Constructing and interpreting three-way control charts for batch processing situations

#### Control Charts for Attribute Data (Optional)

- Understanding attribute data
- Reviewing the binomial distribution
- Generating and interpreting P and NP charts
- Reviewing the Poisson distribution
- Generating and interpreting C and U charts

#### Pareto Charts

- Generating and interpreting Pareto charts

#### Process Capability

- Understanding process capability analysis
- Comparing control limits to specification limits
- Calculating and interpreting capability indices

#### Ishikawa diagrams

- Performing and interpreting Ishikawa diagrams (also known as Fishbone or Cause-and-Effect diagrams)
- Performing and interpreting related nested-relationship diagrams

### Request Teaching Materials

Use of these materials is strictly limited to course use at degree granting academic institutions. To request a set of teaching materials, please provide your name, university department and course title along with the set(s) you are interested in to kits@jmp.com.

For a complete list of available teaching materials available from SAS, visit SAS Training & Bookstore.