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. 3211. 10. 345. 50. 239. 15. 338. 35. 285. 40. 276. 20. 254 . 60. 22 -the psychologist concluded exercise did not provide any additional information becausea.) R2 = .923b.) Adjusted R2 =.853c.) B= -.017d.) F change = .045; p>.05 -The linear equation resulting from this is (hint: let SPSS make the decision)a.) Ŷ= 1.670X1 + (-.017)X2 + 16.89b.) Ŷ= (1.775)X1 + (1.670)X2 + 16.89c.) Ŷ= .923X1 + (-.789)X2 + 16.89d.) 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. ### Correlations Table:- **Variables:** Health, Fat, Exercise.- **Pearson Correlation:** - Health & Fat: 0.923 - Health & Exercise: -0.789 - Fat & Exercise: -0.833- **Significance (1-tailed):** - Health with Fat: 0.001 - Health with Exercise: 0.010 - Fat with Exercise: 0.005- **Sample Size (N):** 8 for all variables.### Variables Entered/Removed:- **Model 1:** - Variables Entered: Fat- **Model 2:** - Variables Entered: Exercise- **Dependent Variable:** Health### Model Summary:- **Model 1:** - R: 0.923, R Square: 0.852, Adjusted R Square: 0.827 - Std. Error of Estimate: 1.914 - F Change: 34.410, Significance of F Change: 0.001- **Model 2:** - R: 0.923, R Square: 0.853, Adjusted R Square: 0.794 - Std. Error of Estimate: 2.087 - F Change: 0.045, Significance of F Change: 0.840- **Predictors:** Fat, Exercise### ANOVA Table:- **Model 1:** - Regression Sum of Squares: 126.025 - Mean Square: 126.025 - F: 34.410, Significance: 0.001- **Model 2:** - Regression Sum of Squares: 126.221 - Mean Square: 63.111 - F: 14.489, Significance: 0.008### Coefficients Table:- **Model 1:** - Constant: B = 15.575, Std. Error = 2.224 - Fat: B = 1.775, Std. Error = 0.303, Beta = 0.923, t = 5.866, Sig. = 0.001- **Model 2:** - Constant: B = 16.889, Std. Error =

The data is given as: Fat (grams) . Exercise (minutes). Health (BMI) 8.…

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.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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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.

### Correlations Table:

- **Variables:** Health, Fat, Exercise.
- **Pearson Correlation:**
- Health & Fat: 0.923
- Health & Exercise: -0.789
- Fat & Exercise: -0.833
- **Significance (1-tailed):**
- Health with Fat: 0.001
- Health with Exercise: 0.010
- Fat with Exercise: 0.005
- **Sample Size (N):** 8 for all variables.

### Variables Entered/Removed:

- **Model 1:**
- Variables Entered: Fat
- **Model 2:**
- Variables Entered: Exercise
- **Dependent Variable:** Health

### Model Summary:

- **Model 1:**
- R: 0.923, R Square: 0.852, Adjusted R Square: 0.827
- Std. Error of Estimate: 1.914
- F Change: 34.410, Significance of F Change: 0.001
- **Model 2:**
- R: 0.923, R Square: 0.853, Adjusted R Square: 0.794
- Std. Error of Estimate: 2.087
- F Change: 0.045, Significance of F Change: 0.840
- **Predictors:** Fat, Exercise

### ANOVA Table:

- **Model 1:**
- Regression Sum of Squares: 126.025
- Mean Square: 126.025
- F: 34.410, Significance: 0.001
- **Model 2:**
- Regression Sum of Squares: 126.221
- Mean Square: 63.111
- F: 14.489, Significance: 0.008

### Coefficients Table:

- **Model 1:**
- Constant: B = 15.575, Std. Error = 2.224
- Fat: B = 1.775, Std. Error = 0.303, Beta = 0.923, t = 5.866, Sig. = 0.001
- **Model 2:**
- Constant: B = 16.889, Std. Error =

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