The accompanying values are a set of data for y, the amount of money (in thousands of dollars) contributed to the alumni association at a certain university from a certain graduate class, and x, the number of years following graduation for that class. Complete parts (a) through (c) below. Click the icon to view the contribution data. (a) Fit a regression model of the type Hylx = ẞo +ẞ1×1. y= 587 + (428) ×₁ (Round to the nearest integer as needed.) (b) Fit a quadratic model of the type µyx = 0 +ß₁×₁ +B₁₁ײ. ŷ= 1180 + (192) ×₁ + (35.2) × (Round the x-coefficient to one decimal place as needed. Round all other constants and coefficients to the nearest integer as needed.) (c) Determine which of the models in (a) or (b) is preferable. Use s², R², and the PRESS residuals to support the decision. Compute s² for each model. Model s² Hylx =Bo+B₁x₁ Hylx = Po +B₁x₁ +B₁₁x² 1104727 (Round to the nearest integer as needed.) Compute R² for each model. 430563 Model R² Hylx =Bo+B₁x1 Hypx = Bo+B₁x₁ + B₁₁x² (Round to three decimal places as needed.) Contribution Data y x y 812.48 1 2752.00 11 824.50 2 4391.50 12 1210.50 3 5580.50 13 1348.00 4 5549.00 1301.00 8 6089.00 2566.50 9 5763.00 2529.50 10 8901.00 1567 14

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Question

I need to compute R^2 for each model and compute the press for each model too in part c please

The accompanying values are a set of data for y, the amount of money (in thousands of dollars) contributed to the alumni association at a certain university from a certain graduate class, and x, the number of years following graduation for that class. Complete parts (a) through (c)
below.
Click the icon to view the contribution data.
(a) Fit a regression model of the type Hylx = ẞo +ẞ1×1.
y= 587 + (428) ×₁
(Round to the nearest integer as needed.)
(b) Fit a quadratic model of the type µyx = 0 +ß₁×₁ +B₁₁ײ.
ŷ= 1180 + (192) ×₁ + (35.2) ×
(Round the x-coefficient to one decimal place as needed. Round all other constants and coefficients to the nearest integer as needed.)
(c) Determine which of the models in (a) or (b) is preferable. Use s², R², and the PRESS residuals to support the decision.
Compute s² for each model.
Model
s²
Hylx =Bo+B₁x₁
Hylx = Po +B₁x₁ +B₁₁x²
1104727
(Round to the nearest integer as needed.)
Compute R² for each model.
430563
Model
R²
Hylx =Bo+B₁x1
Hypx = Bo+B₁x₁ + B₁₁x²
(Round to three decimal places as needed.)
Transcribed Image Text:The accompanying values are a set of data for y, the amount of money (in thousands of dollars) contributed to the alumni association at a certain university from a certain graduate class, and x, the number of years following graduation for that class. Complete parts (a) through (c) below. Click the icon to view the contribution data. (a) Fit a regression model of the type Hylx = ẞo +ẞ1×1. y= 587 + (428) ×₁ (Round to the nearest integer as needed.) (b) Fit a quadratic model of the type µyx = 0 +ß₁×₁ +B₁₁ײ. ŷ= 1180 + (192) ×₁ + (35.2) × (Round the x-coefficient to one decimal place as needed. Round all other constants and coefficients to the nearest integer as needed.) (c) Determine which of the models in (a) or (b) is preferable. Use s², R², and the PRESS residuals to support the decision. Compute s² for each model. Model s² Hylx =Bo+B₁x₁ Hylx = Po +B₁x₁ +B₁₁x² 1104727 (Round to the nearest integer as needed.) Compute R² for each model. 430563 Model R² Hylx =Bo+B₁x1 Hypx = Bo+B₁x₁ + B₁₁x² (Round to three decimal places as needed.)
Contribution Data
y
x
y
812.48
1
2752.00
11
824.50
2
4391.50
12
1210.50
3
5580.50 13
1348.00
4
5549.00
1301.00
8 6089.00
2566.50
9
5763.00
2529.50
10 8901.00
1567
14
Transcribed Image Text:Contribution Data y x y 812.48 1 2752.00 11 824.50 2 4391.50 12 1210.50 3 5580.50 13 1348.00 4 5549.00 1301.00 8 6089.00 2566.50 9 5763.00 2529.50 10 8901.00 1567 14
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