Introduction to R for Business Intelligence

Refining data for simple linear regression

As discussed earlier, there may be times when your diagnostic plots indicate that the data does not meet all the assumptions specified by the LINE approach (Linearity, Independence, Normality, and Equal variance).

Consider the following simple dataset. Run a SLR and generate its diagnostic plots:

x0 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) 
y0 <- c(1.00, 1.41, 1.73, 2.00, 2.24, 
        2.45, 2.65, 2.83, 3.00, 3.16) 
fit0 <- lm(y0 ~ x0) 
 
par(mfrow = c(1, 3)) 
plot(x0, y0, pch = 19, main = "Linearity?"); abline(fit0) 
hist(fit0$residuals, main = "Normality?", col = "gray") 
plot(fit0$fitted.values, fit0$residuals,  
     main = "Equal Variance?", pch = 19); abline(h = 0) 

The diagnostic plots generated are as follows:

The first plot ...