Motor Trend is a magazine about the automobile industry. The data set of a collection of cars is exploring in order to derive the relationship between a set of variables and miles per gallon (MPG).

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

A data frame with 32 observations on 11 (numeric) variables.

Load data

data("mtcars")
mydata = mtcars

Summary of data

mydata$am <- factor(mydata$am, labels = c("Automatic", "Manual"))
head(mydata)

##                    mpg cyl disp  hp drat    wt  qsec vs        am gear
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0    Manual    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0    Manual    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1    Manual    4
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1 Automatic    3
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0 Automatic    3
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1 Automatic    3
##                   carb
## Mazda RX4            4
## Mazda RX4 Wag        4
## Datsun 710           1
## Hornet 4 Drive       1
## Hornet Sportabout    2
## Valiant              1

Plotting Histogram and Box plot for answering question

library("ggplot2")
ggplot(mydata,aes(x=mpg,, fill=am, color=am)) +
  geom_histogram( binwidth=5, aes(y=..density..), position="identity", alpha=0.5) +
  labs(title="",x="MPG", y = "Density") +
  geom_density(alpha=0.6) + theme_dark() + theme(legend.position = "right")

ggplot(mydata, aes(x=factor(am), y=mpg, fill=am)) +
  geom_boxplot(position=position_dodge(1),alpha=.7) + 
  scale_fill_manual(values=c("yellow", "blue")) +
  labs(title="",x="Transmisson", y = "MPG") + theme_dark() + 
  theme(legend.position = "right")

The conclusion drawn is that manual car gives more milege than automatic car.

As initial step lets find correlation of MPG with other factor and pick strong relations in multi variable linear regression to justify Transmission with MPG

library("GGally")

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

# Correlation plot
ggcorr(mydata, palette = "RdBu", label = TRUE) + theme_dark() + theme(legend.position = "none")

From result above cylinder, Displacement, Horse power and weight are considered for regression

reg <- lm(mydata$mpg ~ mydata$cyl+mydata$disp+mydata$disp+mydata$hp+mydata$wt+mydata$am,data = mydata)
pred <- predict(reg)
residuals <- residuals(reg)
summary(reg)

## 
## Call:
## lm(formula = mydata$mpg ~ mydata$cyl + mydata$disp + mydata$disp + 
##     mydata$hp + mydata$wt + mydata$am, data = mydata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5952 -1.5864 -0.7157  1.2821  5.5725 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     38.20280    3.66910  10.412 9.08e-11 ***
## mydata$cyl      -1.10638    0.67636  -1.636  0.11393    
## mydata$disp      0.01226    0.01171   1.047  0.30472    
## mydata$hp       -0.02796    0.01392  -2.008  0.05510 .  
## mydata$wt       -3.30262    1.13364  -2.913  0.00726 ** 
## mydata$amManual  1.55649    1.44054   1.080  0.28984    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.505 on 26 degrees of freedom
## Multiple R-squared:  0.8551, Adjusted R-squared:  0.8273 
## F-statistic:  30.7 on 5 and 26 DF,  p-value: 4.029e-10