Create a Linear Regression

From the toolbar, click the icon next to the icon. Select Linear Regression from the drop-down list. Minimize the Decision Tree visualization and Tree Overview window.

In this example, the variable of interest is Age at Death, which should be the first variable listed in the Measure section of the Data pane. Because you want this variable to be the response variable, click, drag, and drop Age at Death from the Data pane onto the model pane. Notice that Age at Death now appears in the Response field on the Roles tab.

The next step is to choose the effect variables or interaction terms that you want to include in the analysis. One option is to make every available variable an effect variable and let SAS Visual Statistics perform variable selection. However, this is not always feasible from a computational resources perspective. This example creates an interaction term to use as an effect variable and includes a few other variables as effect variables.

Because you suspect that systolic blood pressure and diastolic blood pressure interact with each other, create an interaction term for these variables. In the Data pane, click the icon, and select New Interaction Effect. In the New Interaction Effect window, move Diastolic and Systolic from the Available columns area into the Effect elements area. Click Create. The interaction term Diastolic*Systolic appears in the Interaction Effects group of the Data pane.

Click, drag, and drop Diastolic*Systolic onto the model pane. A model is created based on that single effect because the Auto-update model option is selected in the right pane. Each time a change is made to the model, the Linear Regression automatically updates. If you anticipate making many changes or if you are experiencing server performance issues, deselect the Auto-update model option. When auto-updates are disabled, you must click Update in the right pane to update the model.

Next, add more effects to the model. Hold down the Ctrl key, and select Blood Pressure Status, Cause of Death, Leaf ID 1, Sex, Smoking Status, Cholesterol, Height, Smoking, and Weight. Drag and drop these variables onto the model pane. The Linear Regression updates to include these effects.

In the right pane, select the Properties tab. In this model, Informative missingness and Use variable selection are not selected. Disabling Informative missingness means that observations with missing values are not included in the analysis. Disabling Use variable selection means that all variables are used in the model, regardless of how significant they are to the model. For this model, keep the default properties settings.

The Fit Summary window indicates that Cause of Death, Leaf ID (1), and Height are the three most important effects in this model.

The Assessment window indicates that the observed average and predicted average are approximately equal for most bins.

Save the project.