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