CREATE-A-LINEAR-REGRESSION-MODEL-TO-PREDICT-EMPLOYEE-SALARIES-PMA PMA
Import and examine the data STEP1: From the Sources palette, add a Var. File node to a blank stream canvas, edit the node, point to employee_data.txt, and then close the Var. File dialog box. STEP2: From the Output palette, add a Table node downstream from the Var. File node, run it, and then examine the output. The dataset is comprised of 474 employees. Close the Table output window. STEP 3: From the Output palette, add a Data Audit node downstream from the Var. File node, run it, and then examine the output. Set measurement levels and roles: STEP 1: From Field Ops, add a Type node downstream from the Var. File node. STEP 2: Edit the Type node. Click Read Values STEP 3: set the Measurement for educational_level to Ordinal STEP 4: The Role from gender to months_previous_experience is set to Input STEP 5: set the Role for current_salary to Target
CREATE A LINEAR REGRESSION MODEL TO PREDICT EMPLOYEE SALARIES Create Linear Regression Model: STEP 1: From the Modeling palette, add a Linear node downstream from the Type node. STEP 2: Edit the Linear node. Click the Build Options tab STEP 3: click the Basics item and clear the Automatically prepare data check box STEP 4: click the Model Selection item and set the Model Selection method to Include all predictors STEP 5: click Run STEP 6: Edit the generated model nugget, and then click the Model Summary item in the pane on the left. STEP 7: Click the Predictor Importance item in the pane on the left. STEP 8: The job_category field is by far the most important predictor. Gender is the second most important field. Region and age are least important. STEP 9: Click the Predicted by Observed item in the pane on the left. STEP 10: The points are not scattered around the diagonal and the predicted values seem to break up in two categories. STEP 11: Click the Coefficients by Observed item in the pane on the left, and then, from the Style list, select Table.