excel :

https://docs.google.com/spreadsheets/d/1F5Px6CXtf8pX40_azXZ2-e9WHL7q2Vai/edit?usp=sharing&ouid=113068968791942309329&rtpof=true&sd=true

This week's practice problem and questions extends the analysis of house prices Download house pricesin Springfield that was conducted in Week 1: Practice Problems. You will create a multiple regression model for Price.

1. Using R determine the multiple regression equation for Price using the quantitative as well as qualitative (categorical) variables in the dataset. Create dummy variables for the categorical variables and use "North" as the base (or reference) category for the Neighborhood variable, and "No Brick" as the base category for exterior construction material variable, Brick. Call this model Model 1. Copy and paste the command and output generated by R.

2. State the multiple regression equation for Model 1.

3. Interpret the meaning of the coefficients in Model 1 within the context of the problem.

4. Determine and interpret the coefficient of determination, r2, for Model 1.

5. Conduct a hypothesis test to determine the overall fit of Model 1. Use 0.10 level of significance.

a. State the null and alternative hypotheses.

b. Report the test statistic.

c. Report the p-value.

d. Determine the statistical conclusion and communicate the results of the test within the context of the problem.

6. Comment on the significance of each of the predictors in Model 1. Use 0.10 level of significance.

7. Using Model 1 estimate the price of a 1260 sq ft. brick house in the West that has 2 bedrooms, 3 bathrooms and has had 1 offer made on it. 8. Enhance Model 1 with interaction term for "Brick" and the neighborhoods. Call this model Model 2. Copy and paste the command and output generated by R. State the multiple regression equation for Model 2.

9. Using Model 2 estimate the price of a 1260 sq ft. brick house in the West that has 2 bedrooms, 3 bathrooms and has had 1 offer made on it.

10. Compare the overall fit between Model 1 and Model 2. What model would you recommend to predict Price? Explain.