# On the two potential model based on aic are ecost

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On the two potential model based on AIC are ecost, fsize, shelves features, brand and shelves:brand. 3a. > lm_frig<-lm(price ~ ecost + rsize + fsize + shelves + + shelfsqft+ features + brand) > Recidual<-lm_frig\$residuals > fited<-lm_frig\$fitted.values

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> plot(fited,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "Fitted_Values", + main = "Residual Vs Fitted_Values") > abline(0,0) Interpretation The above residual vs fitted_values shows it reasonable to assume a homoscedastic because there is no regular pat tern in the scattered plot. > plot(ecost,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "ecost", + main = "Residual Vs ecost") > abline(0,0) Interpretation The above residual vs ecost shows it reasonable to assume a homoscedastic because there is no regular pattern in the scattered plot
> plot(rsize,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "rsize", + main = "Residual Vs rsize") > abline(0,0) Interpretation The above residual vs rsize shows it reasonable to assume a homoscedastic because there is no regular pattern in t he scattered plot. > plot(fsize,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "fsize", + main = "Residual Vs fsize") > abline(0,0) Interpretation The above residual vs fsize shows it reasonable to assume a homoscedastic because there is no regular pattern in t he scattered plot > plot(shelves,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "shelves", + main = "Residual Vs shelves") > abline(0,0)

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Interpretation The above residual vs shelves shows it reasonable to assume a homoscedastic because there is no regular pattern i n the scattered plot. > plot(shelfsqft,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "shelfsqft", + main = "Residual Vs shelfsqft") > abline(0,0) Interpretation The above residual vs shelfsquft shows it reasonable to assume a homoscedastic because there is no regular patter n in the scattered plot > plot(features,Recidual,col="blue", + pch=19,ylab="Residual",xlab = "features", + main = "Residual Vs features") > abline(0,0) Interpretation The above residual vs features shows it reasonable to assume a homoscedastic because there is no regular pattern in the scattered plot
> plot(brand,Recidual,col="yellow", + pch=19,ylab="Residual",xlab = "brand", + main = "Residual Vs brand",horizontal = TRUE) Interpretation Boxplot above shows that there is no outliers which is normally distributed. > hist(Recidual, col= "green", + xlab="Residual", main = "Histogram of Residual") The histogram above shows the residuals are left-skewed, which is problematic to make inference on this model.

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> boxplot(Recidual, col= "green", horizontal = TRUE, + main= "Residual Boxplot", xlab="Residual") Interpretation The boxplot above shows that price are roughly normally distributed with no outliers.
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