The section Use Regression with One Predictor showed you how to build simple regression models consisting of one predictor variable and one response variable. Multiple regression predicts the average response variable using two or more predictor variables.
This example uses the Candy Bars.jmp data table, which contains nutrition information for candy bars.
Use multiple regression to predict the average response variable using these three predictor variables.
1.
Select Help > Sample Data Library and open Candy Bars.jmp.
2.
Select Graph > Scatterplot Matrix.
3.
Select Calories and click Y, Columns.
4.
Select Total fat g, Carbohydrate g, and Protein g, and click X.
5.
Figure 3.103 Scatterplot Matrix Results
Continue to use the Candy Bars.jmp sample data table.
1.
Select Analyze > Fit Model.
2.
Select Calories and click Y.
3.
Select Total Fat g, Carbohydrate g, and Protein g and click Add.
4.
Next to Emphasis, select Effect Screening.
Figure 3.104 Fit Model Window
5.
Click Run.
Figure 3.105 Actual by Predicted Plot
Another measure of model accuracy is the RSq value (which appears below the plot in Figure 3.105). The RSq value measures the percentage of variability in calories, as explained by the model. A value closer to 1 means a model is predicting well. In this example, the RSq value is 0.99.
Figure 3.106 Parameter Estimates Report
Figure 3.107 Prediction Profiler
Figure 3.108 Factor Values for the Milky Way

Help created on 7/12/2018