Helping students to reason statistically is challenging enough without also having to provide in-class software instruction. “Practical Data Analysis with JMP, Second Edition” walks students through the process of analysis with JMP at their own speed at home, allowing faculty to devote class time to crucial or subtle statistical concepts and to addressing student-generated questions.

Consider, for example, the topic of descriptive bivariate co-variation. As a professor, what does Robert Carver want his students to understand (as opposed to memorize)? He focuses on the following “big ideas”:

- Many statistical investigations ask if variables X and Y have a relationship
- Two univariate graphs are not informative about how X and Y vary together
- The proper investigative technique depends on the data types involved
- With two variables, there are just three choices: 2 categorical, 2 numerical, or 1 of each
- Start with graphs. What kinds of patterns are typical when X & Y are/are not associated?
- We can distill some graphical patterns numerically.

Chapter 4 of Carver’s book is devoted to descriptive analysis of two variables at a time. Using two different data tables and posing realistic problems, the chapter leads students through the foundational concepts of bivariate description. It then provides step-by-step software instruction. By working through the steps, students find solutions to the motivating problems.

Instructors new to JMP 11 may want to lean heavily on Graph Builder to review the concepts during class, and then solidify students’ new-found skills to examine additional pairs of variables from the sample data tables. At the end of the chapter, you’ll find eight more scenarios suitable for homework exercises.

The final topic in the chapter is entitled “More Informative Scatter Plots” which introduces JMP’s Bubble Plot and revisits an earlier chapter example, this time adding additional dimensions to the bivariate plot. (see pages 78-79)

For more information on practical data analysis with JMP, grab Robert Carver’s latest book as an instrument to build your JMP knowledge and empower yourself to reason statistically. You can also read an excerpt from Carver’s book.