regression analysis in r/rstudio

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Predicting Miles per Gallon from Auto Specifications

Using dataset on mpg (miles per gallon) on UCI Machine Learning Repository

##Read data in to variable auto

auto <- na.omit(read.table("auto-mpg.data"))

##Apply meaningful columnmanes from data source

colnames(auto) <- c("mpg","cylinders","displacement","horsepower","weight","acceleration","model_year","

origin","car_name")

##Change horsepower to factor data type

auto$horsepower <- as.numeric(levels(auto$horsepower))[auto$horsepower]

##Remove N/As

auto <- na.omit(auto)

##Inspect scatterplots for relationships among variables

pairs(~mpg + cylinders + displacement + horsepower + weight + acceleration + model_year+origin)

Let’s start analysis in RStudio (download from here if you don’t already have it).

auto.fit <- lm(mpg~. -car_name,data=auto)

summary(auto.fit)

auto.fit1 <- lm(mpg ~ displacement + horsepower + weight , data=auto)

summary(auto.fit1)

auto.fit2 <- lm(mpg ~ acceleration + horsepower + weight, data = auto)

summary(auto.fit2)

auto.fit3 <- lm(mpg ~ model_year + horsepower + weight, data = auto)

summary(auto.fit3)

auto.fit4 <- lm(mpg ~ model_year + horsepower + weight + origin, data = auto)

summary(auto.fit4)

auto.fit5 <- lm(mpg ~ model_year + acceleration + weight + origin, data = auto)

summary(auto.fit5)

auto.fit6 <- lm(mpg ~ model_year + weight + origin, data = auto)

summary(auto.fit6)

auto.fit7 <- lm(mpg ~ I(displacement^2) + model_year + weight + origin, data = auto)

summary(auto.fit7)

auto.fit8 <- lm(mpg ~ I(horsepower^1) + I(horsepower^2) + I(horsepower^3) + model_year + weight + origin, data = auto)

summary(auto.fit8)

auto.fit9 <- lm(mpg ~ horsepower + model_year + weight + origin, data = auto)

summary(auto.fit9)

auto.fit10 <- lm(mpg ~ model_year + weight + origin + poly(horsepower,2) , data=auto)

summary(auto.fit10)