In a town, a sample of 24 recently sold houses is collected. We would like to predict the sale price in $/10000 (Y) using the size of the home in sq. ft./1000 (X,) and the number of rooms (X,) as predictor variables. The data is shown in the house.csv file. 田 Y X 1.5 X2 29.5 7 ... 25.9 0.998

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In a town, a sample of 24 recently sold houses is collected. We would like to predict the sale
price in $/10000 (Y) using the size of the home in sq. ft./1000 (X,) and the number of
rooms (X2) as predictor variables. The data is shown in the house.csv file.
Y
X
X,
29.5
1.5
...
25.9
0.998
7
a) Fit the model Y = Bo + B,X, + B,X2 + e to the house price data, and give the least
%3D
squares function.
b) Find the value of SSE that is minimized by the least squares method.
c) Estimate o, the standard deviation of e
d) Conduct the ANOVA F-test for model usefulness at the a = 0.05 significance level (be
sure to specify the null and alternative hypotheses).
e) Conduct the individual t-tests for B, and B, at the a = 0.05 significance level (be sure to
specify the null and alternative hypotheses).
f) Find and interpret the coefficient of determination R2, and the adjusted coefficient of
determination R.
g) Which model do you think would be best: a simple linear regression with X, as the
predictor variable, a simple linear regression with X, as the predictor variable, or the
first-order multiple regression model with both X, and X,?
h) Using the chosen model from part g), predict the sale price for a 1000 square foot
house with 6 rooms. Give a 95% prediction interval for this estimate.
Transcribed Image Text:In a town, a sample of 24 recently sold houses is collected. We would like to predict the sale price in $/10000 (Y) using the size of the home in sq. ft./1000 (X,) and the number of rooms (X2) as predictor variables. The data is shown in the house.csv file. Y X X, 29.5 1.5 ... 25.9 0.998 7 a) Fit the model Y = Bo + B,X, + B,X2 + e to the house price data, and give the least %3D squares function. b) Find the value of SSE that is minimized by the least squares method. c) Estimate o, the standard deviation of e d) Conduct the ANOVA F-test for model usefulness at the a = 0.05 significance level (be sure to specify the null and alternative hypotheses). e) Conduct the individual t-tests for B, and B, at the a = 0.05 significance level (be sure to specify the null and alternative hypotheses). f) Find and interpret the coefficient of determination R2, and the adjusted coefficient of determination R. g) Which model do you think would be best: a simple linear regression with X, as the predictor variable, a simple linear regression with X, as the predictor variable, or the first-order multiple regression model with both X, and X,? h) Using the chosen model from part g), predict the sale price for a 1000 square foot house with 6 rooms. Give a 95% prediction interval for this estimate.
A1
fx Prize
A
B.
C
Prize
Size
Rooms
29.5
1.5
27.9
1.175
25.9
1.232
6
29.9
1.121
29.9
0.988
6
30.9
1.24
7
8.
28.9
1.501
6.
35.9
1.225
10
31.5
1.552
31
0.975
12
30.9
1.121
6.
13
30
1.02
14
36.9
1.664
8
15
41.9
1.488
7
16
40.5
1.376
6
17
43.9
1.5
7
18
37.5
1.256
6
19
37.9
1.69
6.
20
44.5
1.82
8.
21
37.9
1.652
6.
22
38.9
1.777
8
23
36.9
1.504
7
24
45.8
1.831
8
25
25.9
0.998
26
27
28
29
30
Re
31
32
33
34
35
house(1) (1)
76
6.
1.
12
345 67
Transcribed Image Text:A1 fx Prize A B. C Prize Size Rooms 29.5 1.5 27.9 1.175 25.9 1.232 6 29.9 1.121 29.9 0.988 6 30.9 1.24 7 8. 28.9 1.501 6. 35.9 1.225 10 31.5 1.552 31 0.975 12 30.9 1.121 6. 13 30 1.02 14 36.9 1.664 8 15 41.9 1.488 7 16 40.5 1.376 6 17 43.9 1.5 7 18 37.5 1.256 6 19 37.9 1.69 6. 20 44.5 1.82 8. 21 37.9 1.652 6. 22 38.9 1.777 8 23 36.9 1.504 7 24 45.8 1.831 8 25 25.9 0.998 26 27 28 29 30 Re 31 32 33 34 35 house(1) (1) 76 6. 1. 12 345 67
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