Question

MBA 6300 Case Study No. 2

There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), number of bedrooms, and number of bathrooms. The data in the Case Study No. 2.xlsx file pertains to a random sample of houses located in a particular geographic area.

1. Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence. Write the regression equation for each model.
   1. Sale price based upon square feet of living area.
   2. Sale price based upon number of bedrooms.
   3. Sale price based upon number of bathrooms.
2. Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence. Write the regression equation for each model.
   1. Sale price based upon square feet of living area and number of bedrooms.
   2. Sale price based upon square feet of living area and number of bathrooms.
   3. Sale price based upon number of bedrooms and number of bathrooms.
   4. Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.
3. Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
4. Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
5. Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.
6. Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.
7. Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms. (Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)

Prepare a single Microsoft Excel file using a separate worksheet for each question and upload your Excel file.

The system will not let me post all of the data needed to answer the question... it says that it is too long . could you save this information so i can add the data ?

Answer 1

Since we are asked to use excel , we should have the data analysis tool which can be added in add-in

1)Now in data analysis select regression and enter sales price data in input y range and the confidence level as .90

2)First enter the living room area in input x range and select output range and click continue

The regression equation is of the form Y = a+bX , where a is the coefficient value of constant and b is the coefficient value of living area size .

Now repeat step 1 and in step 2 , replace ling room size by the number of bedrooms and write down the regression equation from the output

For the third one repeat the same with number of bathrooms in x range and write the corresponding equation

From the three outputs , the independent variable is insignificant when the p value of the corresponding regression coefficient is less than level of significance (.1)

Now for the multiple regression model , everything remains the same and the input range is being added with a comma with living area and number of bedrooms , living room area and number of bathrooms , number of bedrooms and number of bathrooms and finally all the three independent variables and right the corresponding models from the output and the interpretation is given based on the p value of regression coefficient .

When the level of significance is reduced there is a greater chance of getting the more significant models because many will be eliminated when the p value is greater than .05

For predicting the sales price , choose the model for which there is atleast one significant independent variable and if there are many models having significant independent variables , select the model with the largest R² value

Substitute the necessary values in the selected model and get the predicted sales price

If still any doubts , copy the data and paste the entire data in table format in the comment section so that I can help you out