MechaCarChallenge.RScript.R

#load dplyr package
library(dplyr)
library(ggplot2)
library(tidyr)
library(jsonlite)

#read file
MechaCar <- read.csv(file='MechaCar_mpg.csv',check.names=F,stringsAsFactors = F)

#perform linear regression
lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD,data= MechaCar)

#perform summary
summary(lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD,data= MechaCar))


#read file
Suspension <- read.csv(file='Suspension_coil.csv',check.names=F,stringsAsFactors = F)

#get a total summary
total_summery <- Suspension %>% summarize(Mean=mean(PSI),Median=median(PSI),Variance=var(PSI),SD=sd(PSI)) 

#create a lot summary
lot_summery <- Suspension %>% group_by(Manufacturing_Lot) %>% summarize(Mean=mean(PSI),Median=median(PSI),Variance=var(PSI),SD=sd(PSI),.groups='keep')

#perform t-test to determine if the PSI across 
# Peform t-test across all Lots
t.test(Suspension$PSI,mu = 1500)

# Peform t-test on Lot 1
t.test(subset(Suspension,Manufacturing_Lot=="Lot1")$PSI,mu = 1500)

# Peform t-test on Lot 2
t.test(subset(Suspension,Manufacturing_Lot=="Lot2")$PSI,mu = 1500)

# Peform t-test on Lot 3
t.test(subset(Suspension,Manufacturing_Lot=="Lot3")$PSI,mu = 1500)