.

Return to the Organics diagram. Attach the StatExplore tool to the ORGANICS data source and run it.

• In preparation for regression, you will see there are missing values that need to be imputed. Add an Impute node to the diagram and connect it to the Data Partition node. Set the node to impute the letter U for unknown class variable values (Default Input Method = Default Constant Value, and type U for the Default Character Value) and the overall mean for unknown interval variable values. Create imputation indicators for all imputed inputs.
• Add a Regression node to the diagram and connect it to the Impute node.
• Choose Stepwise as the Selection Model and the Validation Error as the Selection Criterion.
• Run the Regression node and view the results. Question 1: Which variables are included in the final model?

Question 2: What is the validation ASE?

• Explore the data and have a look at the skewness of different variables.
• Disconnect the Impute node from the Data Partition node.
• Add a Transform Variables node to the diagram and connect it to the Data Partition node.
• Connect the Transform Variables node to the Impute node.
• Apply a log transformation to the DemAffl and PromTime inputs.
• Run the Transform Variables node. Explore the exported training data. Did the transformations result in less skewed distributions?
• Rerun the Regression node. Question 3: Do the selected variables change?

Question 4: How about the validation ASE?

Create a full second-degree polynomial model. Question 5: How does the validation average squared error for the polynomial model compare to the original model?

A supermarket is offering a new line of arganic peoducts, The supermarkets management wants to determine which customers are likely to purchase these products. The supermarket has a customer loyalty program. As an initial buyer incentive plan, the sapernarket provided coupons for the ofganic prodocts to all of the loyalty program participans and collected data that includes whether these custamers purchased any of the organic prodacts. The ORGANICS data set contains 13 variables and over 22,000 observations. The variables in the data set are shown below with the appropriate roles and levels: - Although two target variables are listed, the tagget variable of interest to as is the binary variable TargetBuy. - Create a new diagram named Organics. - Define the data set AAEMLORGANICS as a data source for the project. - Set the model roles for the analysis variables as shown above. - Examine the distribution of the target variable. Question 1: What is the propertion of individuals who purchased organic products? - The variable DemClusterGroup contains collapsed levels of the varible DemClaster. Presume that, based en previous experience, you believe that DemClusterGroup is sufficicat for this type of modeling effort. Set the model role for DemCluster to Rejected. - As noted above, only TargetBuy will be used for this analysis and should have a role of Target. Set the role for TargetAmt to Rejected. - Finish the Organies data source definition. - Add the AAEM,ORGANICS data source to the Organics diagram workspace. - Add a Data Partition node to the diagram and connect it to the Data Seurce node. Assign 50% of the data for training and 50% for validation