Week 11 Discussion - Auto MPG as function of horsepower

https://archive.ics.uci.edu/ml/datasets/Auto+MPG

1. mpg:           continuous
2. cylinders:     multi-valued discrete
3. displacement:  continuous
4. horsepower:    continuous
5. weight:        continuous
6. acceleration:  continuous
7. model year:    multi-valued discrete
8. origin:        multi-valued discrete
9. car name:      string (unique for each instance)
library(tidyr)

#retrieve auto mpg data from UCI website
car_mpg <- read.csv(file="https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", header=FALSE, sep="\t")


#collapse multiple-spaces into single space for columns in V1 with subset of numbers
car_mpg$V1 <- (gsub("[[:space:]]+", " ", car_mpg$V1))

#seperate V1 into multiple columns with headers
car_mpg <- car_mpg %>% separate(V1, into=c('mpg','cylinders','displacement','horsepower','weight','acceleration','model_year','origin'), sep=" ")

It seems there is negative correlation between mpg and horsepower

# mpg as function of horsepower

car_horsepower_vs_mpg <- lm(mpg~horsepower, car_mpg, na.action = na.exclude)

options(warn=-1)

plot(x = car_mpg$horsepower, y= car_mpg$mpg )

from the qqplot and residual fitted charts, the residual values are pretty normal and random distributed.

plot(car_horsepower_vs_mpg)