1 Week 2 Exercises (Include screenshots in your responses when applicable)

Book: Building Better Models with JMP Pro (Chapter 3) Use the Equity.jmp dataset for this exercise (data table available on Canvas). Save this file using a new name, for example EquityYourName.jmp. a. Use the Graph Builder to create visual explorations similar to Figure 3.9 in your book. Please explore the relationships between BAD versus ALL the continuous (numeric) predictors in this data set (hint: one predictor at a time). Doing any of the predictors appear to be related to BAD? Only include screenshots where a relationship is apparent. b. Use the Graph Builder to recreate the graph in Figure 3.10, using BAD versus REASON. Interpret this graph - does there appear to be a relationship between BAD and REASON? Please explain your interpretation as if I were your manager. c. Use Analyze > Fit Y by X to analyze the relationship between BAD (Y, Response) and LOAN (X, Factor). Don't be afraid to explore the options under the red triangle. i. Describe the relationship between BAD and LOAN. d. Use the Distribution platform to create a histogram and summary statistics for DELINQ, VALUE, and MORTDUE. Describe the shapes of these distributions, as well as any other important observations worth highlighting. Do you see anything concerning? e. Recreate (or adapt) the formula in Figure 3.21 to bin DELINQ into three groups. i. After creating the formula, use the Distribution platform to graph DELINQ and this new column entitled DELINQ Binned, and check your work (to make sure the binning was done correctly). Please share your visualization below. ii. Later in this course, we'll create a model to predict BAD from the available predictors. In this context, does binning DELINQ make sense? What impact will using the binned data (over the original variable) have on our model?

2 f. Refer to the distributions of VALUE and MORTDUE created in part d above. Use the Graph Builder and dynamic transformations (shown in Figure 3.20) to explore different transformations of these variables. This is intended to allow you to practice applying transformations and interpreting the shape of the resulting distributions. i. Which transformations, if any, appear to normalize the variables? Copy & paste your visualizations below. ii. From the context of modeling, explain why it might make sense to transform these variables.