R Markdown

cereal<-read.csv("https://raw.githubusercontent.com/maliat-hossain/FileProcessing/main/cereal.csv")
library(ggplot2)
ggplot(cereal, aes(x=sugars, y=calories)) +
geom_point() +
ggtitle("Calories vs. Sugars")

model1 <- lm(calories ~ sugars, data = cereal)
ggplot(cereal, aes(x=sugars, y=calories)) +
geom_point() +
ggtitle("Calories vs. Sugars") +
geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

### Residual Analysis

plot(fitted(model1), resid(model1), xlab = "Fitted Values", ylab = "Residual Values", main = "Residuals")

QQ Plot

qqnorm(resid(model1))
qqline(resid(model1))

hist(model1$residuals,col="orange")

### X and Y value is not normally distributed.