Consider the real estate data on 60 homes in a middle-class neighborhood of Southwest Houston provided on the Excel data file. The variables are:

Price (Y) in $1000, Square Feet (X1), # of Rooms (X2), # of Bedrooms (X3), # of Bathrooms (X4), Age (X5)

1. Construct Scatter plot of all 5 variables (one at a time) vs. Price (Y) (5 graphs), insert best fitted linear equations and comment on the goodness of the fit based on R-squared values.

2. Construct the correlation matrix of all 6 variables and rank variables based on absolute values correlations with Price. Discuss correlation of variables against Price in plain language.

3. Construct a full model using all independent variables vs. Price (Y), find the equation, R-squared, significant F, and comment on the goodness of the model.Rank the independent variables based on degree of contribution to the model based on their P-values.

4. Based on independent variable ranking in part C, select 4 best variables and construct 4 variable multiple linear regression model to predict Price. Make sure to obtain residual values in the model. Outline the regression equation, R-squared, significant F, and comment on the goodness of the model. Make sure to interpret R-Squared and Significant F values.

5. Use the residuals output in part (d) and identify top 5 overvalued and 5 undervalued homes in this neighborhood.

6. Based on model in part (d), estimate the value for 4000 sq. ft. home with 6 rooms, 3.5 baths, 4 bedrooms, and age of 20.