MechaCarChallenge.R

###Deliverable 1###########################################

# import csv file
# example code from 15.2.3
# demo_table <- read.csv(file='demo.csv',check.names=F,stringsAsFactors = F)
MechaCar_mpg <- read.csv(file='Assignment Resources/MechaCar_mpg.csv',check.names=F,stringsAsFactors = F)

# Perform linear regression using the lm() function
Mecha_lm <- lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD, data=MechaCar_mpg)
Mecha_lm

# view using summary
summary(Mecha_lm)

# view R-squared
summary(Mecha_lm)$r.squared

################# Start Deliverable 2#################################################

# Import CSV file:
Suspension_Coil <- read.csv(file='Assignment Resources/Suspension_Coil.csv',check.names=F,stringsAsFactors = F)

# create a summary table for suspension_coils
total_summary <- Suspension_Coil %>%
  summarize(Mean=mean(PSI), Median=median(PSI), Variance=var(PSI), std_Dev=sd(PSI))
total_summary

# view by lots using grouby:
lot_summary <- Suspension_Coil %>% group_by(Manufacturing_Lot) %>%
  summarize(Mean=mean(PSI), Median=median(PSI), Variance=var(PSI), std_Dev=sd(PSI))

#######Deliverable 3##################
# note level of significance is .05

# Ho: mu = 1500
# Ha: mu <> 1500

t.test(Suspension_Coil$PSI, mu=1500)

# Analysis- p-value(0.06) greater than 0.05 therefore we fail to reject Ho

# t.test by lots

# Lot 1
t.test(subset(Suspension_Coil, Manufacturing_Lot=="Lot1")$PSI, mu=1500)

# Lot 2
t.test(subset(Suspension_Coil, Manufacturing_Lot=="Lot2")$PSI, mu=1500)

# Lot 3
t.test(subset(Suspension_Coil, Manufacturing_Lot=="Lot3")$PSI, mu=1500)