patient how many minutes per week (on average) he or she exercised. He also measured their cholesterol levels. The independent variable is Exercise Time (as in weekly exercise time in minutes). The response variable is Change (as in change in cholesterol level before and after exercise program). a. Use information in Figure 2 and choose the correct regression equation i. y = −23.669 + 29.609x

Algebra & Trigonometry with Analytic Geometry
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ISBN:9781133382119
Author:Swokowski
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Chapter4: Polynomial And Rational Functions
Section4.6: Variation
Problem 37E
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1. Physicians have been recommending more exercise for their patients, particularly those who are overweight. One benefit of regular exercise appears to be a reduction in choles- terol, a substance associated with heart disease. In order to study the relationship more carefully, a physician took a random sample of 50 patients who do not exercise. He measured their cholesterol levels. He then started these patients on regular exercise programs. After four months, the physician asked each patient how many minutes per week (on average) he or she exercised. He also measured their cholesterol levels. The independent variable is Exercise Time (as in weekly exercise time in minutes). The response variable is Change (as in change in cholesterol level before and after exercise program).

a. Use information in Figure 2 and choose the correct regression equation

i. y = −23.669 + 29.609x

ii. y= 29.609 − 23.669x

iii. y = −239.772 + 0.981x

iv. y = 0.981 − 239.772x

b. Based on your answer in part (a), if the weekly exercise time is in- creased by one minute, the average change in cholesterol level will increase by

i. 29.609

ii. 0.981

iii. 23.669

iv. 239.772

c. Use Figure 3 to find value of s

i. 733.357

ii. 27.081

iii. 876.722

iv. 29.609

d. Use Figure 3 to find value of r2

i. 1.054

ii. 0.948

iii. 0.974 

iv. 1.026

e. Based on your answer in part (d), the weekly exercise time in minutes is an excellent predictor of change in cholesterol level before and after exercise program

i. True

ii. False

ANOVAª
Sum of
Squares
Model
df
Mean Square
F
Sig.
Regression 642950.535
1 642950.535 876.722
.000b
1
Residual
35201.145
48
733.357
Total
678151.680
49
a. Dependent Variable: Change
b. Predictors: (Constant), ExerciseTime
Figure 3: Analysis of Variance
Transcribed Image Text:ANOVAª Sum of Squares Model df Mean Square F Sig. Regression 642950.535 1 642950.535 876.722 .000b 1 Residual 35201.145 48 733.357 Total 678151.680 49 a. Dependent Variable: Change b. Predictors: (Constant), ExerciseTime Figure 3: Analysis of Variance
300
200
100
-100
-200
100
200
300
400
500
Exercise Time
Figure 1: Scatter plot and fitted regression line
Coefficients"
Standardized
Coefficients
Unstandardized Coefficients
Model
B
Std. Error
Beta
Sig.
1
(Constant)
-239.772
10.130
-23.669
.000
ExerciseTime
.981
.033
.974
29.609
.000
a. Dependent Variable: Change
Figure 2: Estimated regression coefficients
Change
Transcribed Image Text:300 200 100 -100 -200 100 200 300 400 500 Exercise Time Figure 1: Scatter plot and fitted regression line Coefficients" Standardized Coefficients Unstandardized Coefficients Model B Std. Error Beta Sig. 1 (Constant) -239.772 10.130 -23.669 .000 ExerciseTime .981 .033 .974 29.609 .000 a. Dependent Variable: Change Figure 2: Estimated regression coefficients Change
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