Make a full regression predicting quality from density for all interesting variables.
Spoiler: lm(y~x1+x2+x4+...)
Then, explore a few more promising candidates, using lm and graphs.
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R Code is given below:mydata = read.table("C:/Users/GCE Employee/Desktop/WORK/DOWNLOADS/data.txt", header= TRUE, sep = ";")
model1 <- lm(quality ~ ., data = mydata)
model1
summary (model1)
anova(model1)
resid(model1) #List of residuals
plot(density(resid(model1))) #A density plot
qqnorm(resid(model1)) # A quantile normal plot - good for checking normality
qqline(resid(model1))
model2 <- step(lm(quality ~ ., data = mydata), direction="both")
summary (model2)
anova(model2)...
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