To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:

Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using multiple linear regression. Use Sale Price as the output variable and all the other variables as input variables. To generate a pool of models to consider, execute the following steps. In Step 2 of XLMiner's Multiple Linear Regression procedure, click the Best subset option. In the Best Subset dialog box, check the box next to Perform best subset selection, enter 6 in the box next to Maximum size of best subset:, enter 1 in the box next to Number of best subsets:, and check the box next to Exhaustive search.
a. From the generated set of multiple linear regression models, select one that you believe is a good fit. Express the model as a mathematical equation relating the output variable to the input variables.
b. For your model, what is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?

Question

To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:

Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using multiple linear regression. Use Sale Price as the output variable and all the other variables as input variables. To generate a pool of models to consider, execute the following steps. In Step 2 of XLMiner's Multiple Linear Regression procedure, click the Best subset option. In the Best Subset dialog box, check the box next to Perform best subset selection, enter 6 in the box next to Maximum size of best subset:, enter 1 in the box next to Number of best subsets:, and check the box next to Exhaustive search. 
a. From the generated set of multiple linear regression models, select one that you believe is a good fit. Express the model as a mathematical equation relating the output variable to the input variables.
b. For your model, what is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?

Quizplus · Accepted Answer

The Answer of To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:

Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using multiple linear regression. Use Sale Price as the output variable and all the other variables as input variables. To generate a pool of models to consider, execute the following steps. In Step 2 of XLMiner's Multiple Linear Regression procedure, click the Best subset option. In the Best Subset dialog box, check the box next to Perform best subset selection, enter 6 in the box next to Maximum size of best subset:, enter 1 in the box next to Number of best subsets:, and check the box next to Exhaustive search. 
a. From the generated set of multiple linear regression models, select one that you believe is a good fit. Express the model as a mathematical equation relating the output variable to the input variables.
b. For your model, what is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?

To Examine the Local Housing Market in a Particular Region