| House |
Selling Price |
Square Footage |
Bedrooms |
Age |
| 1 |
$64,000 |
1,670 |
2 |
30 |
| 2 |
$59,000 |
1,339 |
2 |
25 |
| 3 |
$61,500 |
1,712 |
3 |
30 |
| 4 |
$79,000 |
1,840 |
3 |
40 |
| 5 |
$87,500 |
2,300 |
3 |
18 |
| 6 |
$92,500 |
2,234 |
3 |
30 |
| 7 |
$95,000 |
2,311 |
3 |
19 |
| 8 |
$113,000 |
2,377 |
3 |
7 |
| 9 |
$115,000 |
2,736 |
4 |
10 |
| 10 |
$138,000 |
2,500 |
3 |
1 |
| 11 |
$142,500 |
2,500 |
4 |
3 |
| 12 |
$144,000 |
2,479 |
3 |
3 |
| 13 |
$145,000 |
2,400 |
3 |
1 |
| 14 |
$147,500 |
3,124 |
4 |
0 |
| 15 |
$144,000 |
2,500 |
3 |
2 |
| 16 |
$155,500 |
4,062 |
4 |
10 |
| 17 |
$165,000 |
2,854 |
3 |
3 |
| Average |
$114,588 |
2,408 |
3 |
14 |
Use the data and develop a regression model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with 3 bedrooms.
Question 1) State the linear equation.
Question 2) Explain the overall statistical significance of the model.
Question 3) Explain the statistical significance for each independent variable in the model
Question 4) Interpret the Adjusted R2.
Question 5) Is this a good predictive equation(s)? Which variables should be excluded (if any) and why? Explain.