Predictive Modeling Hands-on Activities

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These hands-on activities allow you to practice predictive modeling using JMP. The data files for the activity are in the zip file attached to this post. Extract the data in Predictive Modeling Hands-on Activity.zip for use in the following activities. Click this link for the solutions. A PDF file of these activities and solutions (in English) is attached to this post.

Continuous response, continuous predictors

1. Use the data in PM 1.jmp to build predictive models. These data were collected over time.
   1. Visualize the data using Graph Builder and Multivariate. Are there any data problems?
   2. Build a response surface model using Fit Model and the Response Surface macro. What is the R² of the full model? Are there any problems with the model fit seen in the Residual by Predicted plot? Which predictor variables are most important for predicting Y? Save the prediction formula to the data table.
   3. Build a neural network model using the default settings of the Neural platform. What is the R² of the model on the validation set? Are there any problems with the model fit seen in the Residual by Predicted plot? Fit another model with 50 nodes. Does this model have appreciably better predictive capability? Save the prediction formula to the data table.
   4. Build a decision tree model using the Partition platform with a 25% validation set. What is the R² of the model on the validation set? How many splits are in the model? Are there any problems with the model fit seen in the Actual by Predicted plot? Which variables are most important for predicting Y? Save the prediction formula to the data table.
   5. Build an Actual by Predicted graph for the three models using Graph Builder. Which model do you prefer?

Categorical response, continuous and categorical predictors

2. Use the data in PM 2.jmp to build predictive models.
   1. Visualize the data using Graph Builder. Are there any data problems?
   2. Build an ordinal regression model using Fit Model and the Response Surface macro. What is the misclassification rate of the full model? Hint: open the Fit Details report. What is the misclassification rate for the Acceptable group? Hint: open the Confusion Matrix report.
   3. Build a neural network model using the default settings of the Neural platform. What is the misclassification rate of the model on the validation set? In particular, what is the misclassification rate of the Acceptable group? Fit another model with 50 nodes. What is the misclassification rate of the model on the validation set? What is the misclassification rate of the Acceptable group?
   4. Build a decision tree model using the Partition platform with a 25% validation set. What is the misclassification rate of the model on the validation set? What is the misclassification rate of the Acceptable group?