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Below, we see a snippet of a dataset that consists of 128 observations and 15 variables. We present two models describing the linear relationship between the BodyFat and other variables. 2 58.7 37.3 23.4 Density BodyFat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 1.0708 12.3 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 6.1 22 173.25 72.25 38.5 93.6 83.0 98.7 25.3 22 154.00 66.25 34.0 95.8 87.9 99.2 10.4 26 184.75 72.25 37.4 101.8 86.4 101.2 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 59.6 38.9 1.0853 3 1.0414 4 1.0751 5 1.0340 6 1.0502 24.0 60.1 37.3 22.8 63.2 42.2 24.0 100.0 101.9 94.4 107.8 66.0 42.0 25.6 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 20.0 25.2 16.6 32.4 29.4 18.2 5 32.2 27.7 17.7 30.6 18.8 35.7 Analysis of Variance Table Model 1: x$BodyFat x$Neck Model 2: x$BodyFat Res. Df x$Neck + xSWeight + x$Height F Pr (>F) RSS Df Sum of Sq 1 250 13348.1 2 248 9461.4 2 3886.7 50.939 < 2.2e-16 *** Signif. codes: 0*** 0.001 **0.01 * 0.05 0.11 Consider the two models Salary = 6366 +9.3 Age-329.56 Male, R² = 0.135, ² = 1099 (1) and log(Salary) = 5.342 +0.012 Age -0.321 Male, R² = 0.178, 8²=1.231. (2) Consider another model log(Salary) = 3.54 +0.127 Age -0.321 Male, R²=0.880, 820.757. (3) (iii) Is model (3) better than model (2)? Why?

Below, we see a snippet of a dataset that consists of 128 observations and 15 variables. We present two models describing the linear relationship between the BodyFat and other variables. 2 58.7 37.3 23.4 Density BodyFat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 1.0708 12.3 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 6.1 22 173.25 72.25 38.5 93.6 83.0 98.7 25.3 22 154.00 66.25 34.0 95.8 87.9 99.2 10.4 26 184.75 72.25 37.4 101.8 86.4 101.2 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 59.6 38.9 1.0853 3 1.0414 4 1.0751 5 1.0340 6 1.0502 24.0 60.1 37.3 22.8 63.2 42.2 24.0 100.0 101.9 94.4 107.8 66.0 42.0 25.6 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 20.0 25.2 16.6 32.4 29.4 18.2 5 32.2 27.7 17.7 30.6 18.8 35.7 Analysis of Variance Table Model 1: x$BodyFat x$Neck Model 2: x$BodyFat Res. Df x$Neck + xSWeight + x$Height F Pr (>F) RSS Df Sum of Sq 1 250 13348.1 2 248 9461.4 2 3886.7 50.939 < 2.2e-16 *** Signif. codes: 0*** 0.001 **0.01 * 0.05 0.11 Consider the two models Salary = 6366 +9.3 Age-329.56 Male, R² = 0.135, ² = 1099 (1) and log(Salary) = 5.342 +0.012 Age -0.321 Male, R² = 0.178, 8²=1.231. (2) Consider another model log(Salary) = 3.54 +0.127 Age -0.321 Male, R²=0.880, 820.757. (3) (iii) Is model (3) better than model (2)? Why?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Below, we see a snippet of a dataset that consists of 128 observations and 15 variables. We present two models describing the linear relationship between the BodyFat and other variables. Density BodyFat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 1.0708 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 2 1.0853 3 1.0414 4 1.0751 5 1.0340 6 1.0502 83.0 98.7 87.9 99.2 12.3 23 154.25 6.1 22 173.25 25.3 22 154.00 10.4 26 184.75 28.7 24 184.25 20.9 24 210.25 Biceps Forearm Wrist 1 32.0 27.4 17.1 72.25 38.5 93.6 66.25 34.0 95.8 72.25 37.4 101.8 71.25 34.4 97.3 74.75 39.0 104.5 58.7 37.3 23.4 59.6 38.9 24.0 60.1 37.3 22.8 86.4 101.2 100.0 101.9 63.2 42.2 24.0 94.4 107.8 66.0 42.0 25.6 2 30.5 28.9 18.2 3 28.8 25.2 16.6 32.4 29.4 18.2 5 32.2 27.7 17.7 35.7 30.6 18.8 Analysis of Variance Table Model 1: x$BodyFat x$Neck Model 2: x$BodyFat Res. Df x$Neck+xSWeight + x$Height F Pr (>F) RSS Df Sum of Sq 1 250 13348.1 2 248 9461.4 2 3886.7 50.939 < 2.2e-16 *** Signif. codes: 0*** 0.001 '**' 0.01 0.05 0.11 Consider the two models Salary = 6366 +9.3 Age - 329.56 Male, R² = 0.135, ² = 1099 (1) and log(Salary) = 5.342 +0.012 Age-0.321 Male, R² = 0.178, ²= 1.231. (2) Consider another model log(Salary) = 3.54 +0.127 Age -0.321 Male, R² = 0.880, ² = 0.757. (3) (iii) Is model (3) better than model (2)? Why? 4
Transcribed Image Text:Below, we see a snippet of a dataset that consists of 128 observations and 15 variables. We present two models describing the linear relationship between the BodyFat and other variables. Density BodyFat Age Weight Height Neck Chest Abdomen Hip Thigh Knee Ankle 1 1.0708 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 2 1.0853 3 1.0414 4 1.0751 5 1.0340 6 1.0502 83.0 98.7 87.9 99.2 12.3 23 154.25 6.1 22 173.25 25.3 22 154.00 10.4 26 184.75 28.7 24 184.25 20.9 24 210.25 Biceps Forearm Wrist 1 32.0 27.4 17.1 72.25 38.5 93.6 66.25 34.0 95.8 72.25 37.4 101.8 71.25 34.4 97.3 74.75 39.0 104.5 58.7 37.3 23.4 59.6 38.9 24.0 60.1 37.3 22.8 86.4 101.2 100.0 101.9 63.2 42.2 24.0 94.4 107.8 66.0 42.0 25.6 2 30.5 28.9 18.2 3 28.8 25.2 16.6 32.4 29.4 18.2 5 32.2 27.7 17.7 35.7 30.6 18.8 Analysis of Variance Table Model 1: x$BodyFat x$Neck Model 2: x$BodyFat Res. Df x$Neck+xSWeight + x$Height F Pr (>F) RSS Df Sum of Sq 1 250 13348.1 2 248 9461.4 2 3886.7 50.939 < 2.2e-16 *** Signif. codes: 0*** 0.001 '**' 0.01 0.05 0.11 Consider the two models Salary = 6366 +9.3 Age - 329.56 Male, R² = 0.135, ² = 1099 (1) and log(Salary) = 5.342 +0.012 Age-0.321 Male, R² = 0.178, ²= 1.231. (2) Consider another model log(Salary) = 3.54 +0.127 Age -0.321 Male, R² = 0.880, ² = 0.757. (3) (iii) Is model (3) better than model (2)? Why? 4
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