A sport psychologist wanted to test the extent to which daily intake of fat in grams, and amount of exercise (in minutes) can predict health m=(measured using a Body mass index {BMI} scale in which higher scores indicate poorer health. Using the da1ta below: Fat (grams) . Exercise (minutes). Health (BMI) 8. 34. 32 11. 10. 34 5. 50. 23 9. 15. 33 8. 35. 28 5. 40. 27 6. 20. 25 4 . 60. 22 -the psychologist concluded exercise did not provide any additional information because a.) R2 = .923 b.) Adjusted R2 =.853 c.) B= -.017 d.) F change = .045; p>.05 -The linear equation resulting from this is (hint: let SPSS make the decision) a.) Ŷ= 1.670X1 + (-.017)X2 + 16.89 b.) Ŷ= (1.775)X1 + (1.670)X2 + 16.89 c.) Ŷ= .923X1 + (-.789)X2 + 16.89 d.) Ŷ= 16.89X1 + (-.017+1.670)X2 -Did doing exercise provide significantly to the health of the athletes? Provide the regression statistics that show the effect of exercise beyond the effect of body fat.
A sport psychologist wanted to test the extent to which daily intake of fat in grams, and amount of exercise (in minutes) can predict health m=(measured using a Body mass index {BMI} scale in which higher scores indicate poorer health. Using the da1ta below: Fat (grams) . Exercise (minutes). Health (BMI) 8. 34. 32 11. 10. 34 5. 50. 23 9. 15. 33 8. 35. 28 5. 40. 27 6. 20. 25 4 . 60. 22 -the psychologist concluded exercise did not provide any additional information because a.) R2 = .923 b.) Adjusted R2 =.853 c.) B= -.017 d.) F change = .045; p>.05 -The linear equation resulting from this is (hint: let SPSS make the decision) a.) Ŷ= 1.670X1 + (-.017)X2 + 16.89 b.) Ŷ= (1.775)X1 + (1.670)X2 + 16.89 c.) Ŷ= .923X1 + (-.789)X2 + 16.89 d.) Ŷ= 16.89X1 + (-.017+1.670)X2 -Did doing exercise provide significantly to the health of the athletes? Provide the regression statistics that show the effect of exercise beyond the effect of body fat.
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A sport psychologist wanted to test the extent to which daily intake of fat in grams, and amount of exercise (in minutes) can predict health m=(measured using a Body mass index {BMI} scale in which higher scores indicate poorer health. Using the da1ta below:
Fat (grams) . Exercise (minutes). Health (BMI)
8. 34. 32
11. 10. 34
5. 50. 23
9. 15. 33
8. 35. 28
5. 40. 27
6. 20. 25
4 . 60. 22
-the psychologist concluded exercise did not provide any additional information because
a.) R2 = .923
b.) Adjusted R2 =.853
c.) B= -.017
d.) F change = .045; p>.05
-The linear equation resulting from this is (hint: let SPSS make the decision)
a.) Ŷ= 1.670X1 + (-.017)X2 + 16.89
b.) Ŷ= (1.775)X1 + (1.670)X2 + 16.89
c.) Ŷ= .923X1 + (-.789)X2 + 16.89
d.) Ŷ= 16.89X1 + (-.017+1.670)X2
-Did doing exercise provide significantly to the health of the athletes? Provide the regression statistics that show the effect of exercise beyond the effect of body fat.
Transcribed Image Text:Correlations
Health
Fat
Eexercise
Pearson Correlation Health
1.000
923
-.789
Fat
.923 1.000
-.833
Exercise
-.789
-.833
1.000
Sig. (1-tailed)
Health
.001
.010
Fat
.001
.005
Exercise
.010
.005
Health
8
8
8.
Fat
8
8
8
Exercise
8
8
Variables Entered/Removed
Variables
Removed
Model Variables Entered
Fat
Exercise
a. Dependent Variable: Health
Method
1
Enter
2
Enter
b. All requested variables entered.
Model Summary
Std.
Error of
the
Estimate
Mode
R
Square
Adjusted R
Square
Change Statistics
R Square
Change
Sig. F
Change
F
Change
df1
df2
1
.923
.852
.827
1.914
.852
34.410
1
6
.001
2
.923"
.853
794
2.087
.001
.045
.840
a. Predictors: (Constant), Fat
b. Predictors: (Constant), Fat, Exercise
ANOVA
Sum of Squares
Mean Square
Sig.
.001
Model
df
F
1
Regression
126.025
1
126.025
34.410
Residual
21.975
3.662
Total
148.000
2
Regression
126.221
2
63.111
14.489
.008
Residual
21.779
5
4.356
Total
148.000
a. Dependent Variable: Health
b. Predictors: (Constant), Fat
c. Predictors: (Constant), Fat, Exercise
Coefficients
Standardized
Coefficients
Unstandardized
Model
Coefficients
Sig.
Correlations
Std.
Zero-
B
Error
Beta
order
Partial
Part
(Constant
15.575
2.224
7.004
.000
Fat
1.775
.303
.923
5.866
.001
.923
.923
.923
|(Constant
16.889
6.650
2.540
.052
Fat
1.670
.596
.868
2.802
.038
.923
.782
.481
Eexercise
-.017
.082
-.066
-.212
840
-.789
-.095
-.036
a. Dependent Variable: Health
Excluded Variables
Collinearity
Statistics
Partial
Model
Beta In
Sig.
Correlation
Tolerance
1 Exercise
a. Dependent Variable: Health
-.066°|-212 |.840
-.095
.307
b. Predictors in the Model: (Constant), Fat
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