Could you please explain what the standard residuals in the given data imply, in context to the topic (strengths dependency on body weight). Also what the Shapiro test implies in context. 100 110 120Bench press standards per class of body weight (kg) - AverageSample Quantiles0680700.0 0.5 1.0-1.0-2.0OO60Scatterplot for averageO80-1.5 -1.00100-0.5Body Weight (kg)OQ-Q PlotO1200.0 0.5Theoretical QuantilesOO1.0140O1.5Ostdres0.0 0.5 1.0stdres-1.0-2.01.00.50.0-0.5-1.5 -1.0-2.0Std residuals versus explanatory variableOT09o80OO8090OOBody Weight (kg)OStd residuals versus fitsO100100OfitsO120110O120140130O Residuals:Min-10.531 -4.989Coefficients:10 Median3Q1.869 5.267(Intercept)`Body Weight (kg) `Signif. codes: 0 *** 0.001`Body Weight (kg) `ResidualsEstimate Std. Error t value Pr (>|t|)42.627556.209 0.0001570.626928.402 1.49e-05 ***7Residual standard error: 7.077 on 9 degrees of freedomMultiple R-squared: 0.8869, Adjusted R-squared:F-statistic: 70.6 on 1 and 9 DF, p-value: 1.492e-058> abline (Regression.model, col=4, lwd=3)> anova (Regression.model)Analysis of Variance Table6.865170.074611**/Response: Bench press standards per class of body weight (kg) - AverageSum Sq Mean Sq F valuePr (>F)2Max7.950Signif. codes:1**/0 *** 0.001> stdres<- rstandard (Regression.model)> print (stdres)19Df1 3536.0 3536.0 70.601 1.492e-05 ***9 450.850.10.01 * 0.05 ¹.0.1 13data: stdresW = 0.91857, p-value = 0.306910Shapiro-Wilk normality test-1.4864868 -1.0645996 -0.5043759 0.2843252 0.6513863 1.10832911.17860180.87440.01 1*1 0.05. 0.1 140.9248828 0.5985440 -0.3245869 -2.0230776115> plot (Body Weight (kg)`, stdres, main="Std residuals versusexplanatory variable")> fits<- fitted (Regression.model)> plot (fits, stdres, main="Std residuals versus fits")> qqnorm (stdres, main="Q-Q Plot")>qqline (stdres)> shapiro.test (stdres)6

120 110 100 90 80 70 0.0 0.5 1.0 Sample -1.0 -2.0 O O 60 -1.5 O O Scatterplot for average O 80 -1.0 O 100 -0.5 Body Weight (kg) O Q-Q Plot O 120 0.0 0.5 Theoretical Quantiles O 1.0 T 140 1.5 O stdres 0.0 0.5 1.0 -1.0 -2.0 stdres 1.0 0.5 -0.5 0.0 -1.0 -2.0 -1.5 Std residuals versus explanatory variable O 60 O 80 O 80 O O 90 Body Weight (kg) Std residuals versus fits O 100 O T 100 fits O 120 110 120 140 O T 130

120 110 100 90 80 70 0.0 0.5 1.0 Sample -1.0 -2.0 O O 60 -1.5 O O Scatterplot for average O 80 -1.0 O 100 -0.5 Body Weight (kg) O Q-Q Plot O 120 0.0 0.5 Theoretical Quantiles O 1.0 T 140 1.5 O stdres 0.0 0.5 1.0 -1.0 -2.0 stdres 1.0 0.5 -0.5 0.0 -1.0 -2.0 -1.5 Std residuals versus explanatory variable O 60 O 80 O 80 O O 90 Body Weight (kg) Std residuals versus fits O 100 O T 100 fits O 120 110 120 140 O T 130

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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