Contribution Data y x y x 812.48 1 2752.00 11 824.50 2 4391.50 12 1210.50 3 5580.50 13 1348.00 4 5549.00 14 1301.00 8 6089.00 15 2566.50 9 5763.00 16 2529.50 10 8901.00 17 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 μyx = ẞo + B₁×1. ŷ= -587 + (428) ×₁ (Round to the nearest integer as needed.) (b) Fit a quadratic model of the type μyx = 0 +ẞ₁×₁ +ß₁₁ײ₁. ŷ = 1180 + ( − 192 ) ×₁ + ( 35.2 ) × (Round the x²-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 Prix = P₂+B+x+B₁₁x 1104727 430563 (Round to the nearest integer as needed.) Compute R² for each model. Model Hylx = Bo+B1x1 R² (Round to three decimal places as needed.) ☐
Contribution Data y x y x 812.48 1 2752.00 11 824.50 2 4391.50 12 1210.50 3 5580.50 13 1348.00 4 5549.00 14 1301.00 8 6089.00 15 2566.50 9 5763.00 16 2529.50 10 8901.00 17 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 μyx = ẞo + B₁×1. ŷ= -587 + (428) ×₁ (Round to the nearest integer as needed.) (b) Fit a quadratic model of the type μyx = 0 +ẞ₁×₁ +ß₁₁ײ₁. ŷ = 1180 + ( − 192 ) ×₁ + ( 35.2 ) × (Round the x²-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 Prix = P₂+B+x+B₁₁x 1104727 430563 (Round to the nearest integer as needed.) Compute R² for each model. Model Hylx = Bo+B1x1 R² (Round to three decimal places as needed.) ☐
Chapter4: Linear Functions
Section4.3: Fitting Linear Models To Data
Problem 34SE: For the following exercises, consider this scenario: The profit of a company decreased steadily...
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I need final answers for R^2 and press residuals. Round to three decimals in R^2
Transcribed Image Text:Contribution Data
y
x
y
x
812.48
1
2752.00
11
824.50
2
4391.50
12
1210.50
3
5580.50
13
1348.00
4
5549.00
14
1301.00
8
6089.00
15
2566.50
9
5763.00
16
2529.50
10
8901.00
17
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 μyx = ẞo + B₁×1.
ŷ= -587 + (428) ×₁
(Round to the nearest integer as needed.)
(b) Fit a quadratic model of the type μyx = 0 +ẞ₁×₁ +ß₁₁ײ₁.
ŷ = 1180 + ( − 192 ) ×₁ + ( 35.2 ) ×
(Round the x²-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
Prix = P₂+B+x+B₁₁x
1104727
430563
(Round to the nearest integer as needed.)
Compute R² for each model.
Model
Hylx = Bo+B1x1
R²
(Round to three decimal places as needed.)
☐
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