Subscribe Bookmark RSS Feed



May 28, 2014

Building Better Models with JMP Pro Textbook Page

Building Better Models with JMP Pro  book cover

This is the textbook support page for Building Better Models with JMP Pro, by Grayson, Gardner and Stephens, SAS Press, 2015.

jgrayson, mathstatchem​, and mia.stephens.

The data sets for the case studies used in the book, along with some scripts used for illustration, can be downloaded from  Note that some data sets are in the JMP Sample Data Library.  Course materials developed by two professors are attached below.  Eratta, which will be addressed in the next edition, are listed here as well.

Links to useful resources:

  • A collection of add-ins used for data mining and predictive modeling, including add-ins for setting the random seed and changing the cutoff value for classification for a confusion matrix (note that both of these add-ins are referred to in the book):

Collection:  Data Mining Tools Add-Ins

  • Webinars for getting started with JMP, data analysis, graphics, data preparation, and modeling:

Collection: Recent Academic Webinar Recordings

  • Business-oriented case studies, from basic graphics to multiple linear and logistic regression:

Collection: Business Cases

  • Resources for teaching regression analysis using JMP, including one page guides and short videos, teaching scripts, simulations, case studies and more:

Collection: Regression Modeling and Analysis with JMP

  • One page guides and short videos on a number of topics:

Collection: One-Page Guides

Course materials:

Jim Grayson taught a graduate level course at Georgia Regents University (summer 2015), Business Analytics for Managers, prior to the release of this book.  His course materials, including his schedule and syllabus, are attached.  The course used some examples from Business Statistics, 3e (Sharpe, DeVeaux and Velleman).  However, Jim uses a good deal of content from Building Better Models (Chapter 3), and the materials for the modeling chapters (Chapters 4-6, and part of Chapters 8 and 9) are based on Building Better Models.

Bob Nydick is teaching a graduate level online course at Villanova University (fall 2015), Analytical Methods for Data Mining, using this book.  Bob has shared materials he has developed for the course (not including Multiple Regression and Logistic Regression).  Thanks Bob!

Do you have materials or resources for this book or a similar course, or for specific topics covered in the book, that you'd be willing to share?  Please consider posting on the Academic Community.  Or, we'd be happy to post on your behalf.

We hope you enjoy this book!  Questions, comments and suggestions?  Please use the "Add a comment" button at the bottom of this page.

Instructor solutions are under development.  To request instructor access to exercise solutions, please contact mia.stephens.


Figure 4.24 - needs to be retaken with Log(Bal_Total) instead of Bal_Total.

Figures 4.26 and 4.27 need to be retaken with AccountAge.

Page 88 - change "11 remaining" to "12 remaining"

Chapter 5, remove Exercise 5.7

Figure 6.10 is incorrect.  It should be:

Figure 6.12 is incorrect.  It should be (the one shown is after 6 splits):

Bottom of page 158 - last sentence, missing parenthesis.  It should read, "... has 15 splits (see Figure 6.26).  Note..."

Figure 7.22:  Add a note on page 202, results will be slightly different on a Mac (using the same random seed).

Figure 7.33 and 7.34:  The output will be slightly different on a Mac (using the random seed of 1000).

Page 218, Exercise 7.1.  Should be changed to: In the example, we fit a neural network with one layer and three notes, all using the TanH activation function.

a.  Fit the model described in Example 1, and also fit a model with one layer with three nodes, using TanH, linear, and Gaussian activation functions.

Page 232, first sentence in the example should read:

     " illustrate how cross-validation is used in building predictive models."

Page 234:  Add the following sentence, "Note: Rows are randomly assigned to the training, validation and test sets. So, your validation column will likely be different than what is shown here.  Use Boston Housing BBM Ch8 with to produce the results shown in the remainder of this chapter."

Page 251 and 252, exercise 8.1 and 8.2:  the data set is Boston Housing BBM

Page 253, exercise 8.5: The Credit Card Marketing data is described in Example 1 in Chapter 6.

Figure 9.6 - the caption should read "Boosted Tree Prediction formula for Prob[Survived=Yes]"

Note:  On 12/7/2015 an updated version of the data sets was uploaded to the SAS textbook page.  The following changes were made:

  1. The missing value column property was removed from the CreditRiskModeling data table (used in Chapter 7). 
  2. The CreditRiskModeling BBM was added.  This file has been prepared for modeling (see page 211).

Jim Grayson's course materials are attached.  The course was developed prior to the release of the book, so the content doesn't exactly match up.  See the note above for details.  Thanks for sharing Jim!


Bob Nydick's course materials are also attached.  See the description above.  Thank you Bob!


Excellent book and resources.

I have adapted the book for the upcoming Exec MBA Course (ISL680/HCM680: Data Mining) this Fall.



Glad to hear it Prakash!  Thanks!  Hope your semester gets off to a good start!


A professor pointed out that in the dataset there are spaces in front of the values in all of the Nominal columns.  For example, for the variables IntlPlan and VMPlan the values are " yes" and " no", rather than "yes" and "no".  This isn't a problem for modeling, but if you save the prediction formula to the data table and then and enter a new row, you need to enter the values with the space in order for JMP to make a prediction. 

An alternative is to first recode the columns to remove the space.  This is easily done by selecting the columns in the data table, and using Cols > Utilities > Recode - click on the top red triangle in Recode and select Trim Whitespace.


Adding a request from a professor:

I’d like to suggest including a discussion of the FDR (False Discovery Rate) checkbox and what it means in your next edition of Building Better Models with JMP Pro.

Thanks Dave T for the suggestion!