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meddcor = read.table("meddcor-4.txt", sep="", header = FALSE)
n = dim(meddcor)[1] # number of observations
colnames(meddcor) = c("sales", "advertising", "bonuses", "marketshare", "largestcomp", "region")
meddcor$region = as.factor(meddcor$region)
model = lm(sales ~ advertising, data = meddcor)
summary(model)
## 
## Call:
## lm(formula = sales ~ advertising, data = meddcor)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -137.56  -92.63  -19.54   97.51  170.12 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -157.3301   145.1912  -1.084     0.29    
## advertising    2.7721     0.2794   9.921 8.87e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 101.4 on 23 degrees of freedom
## Multiple R-squared:  0.8106, Adjusted R-squared:  0.8024 
## F-statistic: 98.43 on 1 and 23 DF,  p-value: 8.873e-10
plot(model$fitted, model$residuals, xlab = "Response Variable", ylab="Residuals", pty=2, lwd=2)

plot(meddcor$advertising, meddcor$sales, xlab="Advertising Expenditure",ylab="Sales",pty=2,lwd=2)

library(car);  qqPlot(model$residuals,ylab="Residuals")
## Warning: package 'car' was built under R version 4.3.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.3.3

## [1] 19  5
hist(model$residuals, xlab="Residuals",main="")

summary(model)$r.squared
## [1] 0.8105919
summary(model)$adjr.squared
## NULL
1- pt(9.921, 23)
## [1] 4.438411e-10
confint(model, level=0.99)
##                   0.5 %    99.5 %
## (Intercept) -564.930546 250.27032
## advertising    1.987712   3.55652
summary(model)$r.squared
## [1] 0.8105919
summary(model)$adj.r.squared
## [1] 0.8023567