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ow, we see a snippet of a dataset that consists of 128 observations and 15 variables. We o models describing the linear relationship between the BodyFat and other variables. Density BodyFat Age Weight Height Neck Chest Abdomen 11.0708 12.3 23 154.25 67.75 36.2 93.1 85.2 2 1.0853 6.1 22 173.25 72.25 38.5 93.6 3 1.0414 25.3 22 154.00 66.25 34.0 95.8 4 1.0751 10.4 26 184.75 72.25 37.4 101.8 5 1.0340 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 Hip Thigh Knee A 94.5 59.0 37.3 83.0 98.7 58.7 37.3 87.9 99.2 59.6 38.9 86.4 101.2 60.1 37.3 61.0502 100.0 101.9 63.2 42.2 94.4 107.8 66.0 42.0 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 432.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 (a) By looking at the R output, state whether one should include extra param the model. Analysis of Variance Table Model 1: x$BodyFat x$Neck lodal 2. *$RodyFat **Nock Hois

ow, we see a snippet of a dataset that consists of 128 observations and 15 variables. We o models describing the linear relationship between the BodyFat and other variables. Density BodyFat Age Weight Height Neck Chest Abdomen 11.0708 12.3 23 154.25 67.75 36.2 93.1 85.2 2 1.0853 6.1 22 173.25 72.25 38.5 93.6 3 1.0414 25.3 22 154.00 66.25 34.0 95.8 4 1.0751 10.4 26 184.75 72.25 37.4 101.8 5 1.0340 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 Hip Thigh Knee A 94.5 59.0 37.3 83.0 98.7 58.7 37.3 87.9 99.2 59.6 38.9 86.4 101.2 60.1 37.3 61.0502 100.0 101.9 63.2 42.2 94.4 107.8 66.0 42.0 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 432.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 (a) By looking at the R output, state whether one should include extra param the model. Analysis of Variance Table Model 1: x$BodyFat x$Neck lodal 2. *$RodyFat **Nock Hois

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 12.3 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 2 1.0853 6.1 22 173.25 72.25 38.5 93.6 25.3 22 154.00 66.25 34.0 95.8 10.4 26 184.75 72.25 37.4 101.8 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 3 58.7 37.3 23.4 59.6 38.9 24.0 22.8 4 83.0 98.7 87.9 99.2 86.4 101.2 100.0 101.9 63.2 42.2 94.4 107.8 66.0 42.0 25.6 1.0414 1.0751 5 1.0340 6 1.0502 60.1 37.3 24.0 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 4 32.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 (a) By looking at the R output, state whether one should include extra parameters in the model. Analysis of Variance Table Model 1: x$BodyFat x$Neck Model 2: x$BodyFat x$Neck+x$Weight + x$Height Res. Df F Pr (>F) 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 '.' 0.1 1 RSS Df Sum of Sq
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 12.3 23 154.25 67.75 36.2 93.1 85.2 94.5 59.0 37.3 21.9 2 1.0853 6.1 22 173.25 72.25 38.5 93.6 25.3 22 154.00 66.25 34.0 95.8 10.4 26 184.75 72.25 37.4 101.8 28.7 24 184.25 71.25 34.4 97.3 20.9 24 210.25 74.75 39.0 104.5 3 58.7 37.3 23.4 59.6 38.9 24.0 22.8 4 83.0 98.7 87.9 99.2 86.4 101.2 100.0 101.9 63.2 42.2 94.4 107.8 66.0 42.0 25.6 1.0414 1.0751 5 1.0340 6 1.0502 60.1 37.3 24.0 Biceps Forearm Wrist 1 32.0 27.4 17.1 2 30.5 28.9 18.2 3 28.8 25.2 16.6 4 32.4 29.4 18.2 5 32.2 27.7 17.7 6 35.7 30.6 18.8 (a) By looking at the R output, state whether one should include extra parameters in the model. Analysis of Variance Table Model 1: x$BodyFat x$Neck Model 2: x$BodyFat x$Neck+x$Weight + x$Height Res. Df F Pr (>F) 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 '.' 0.1 1 RSS Df Sum of Sq
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