MPG prediction based on a car weight
Moh Hassan
05/21/2015
mtcars data set
This R presentation is for a shiny app that predicts MPG usage for a car using mtcars dataset. The user enters the weight of the car. Then the user selects whether the weight is in pounds or tonnes. A linear regression line is drawn to predicts the MPG of the car.
Slide With Code
summary(mtcars)
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
Exploring relationship between a car weight and its mpg
plot(mtcars$wt, mtcars$mpg)
abline(lm(mpg~wt, data=mtcars), col="red")
Exploring relation between number of cylinder and mpg
Looking at correlation between mpg and car weight
cor(mtcars$wt, mtcars$mpg)
[1] -0.8676594
- We can see that the relationship between mpg and wt is strong and negative. As the car weight increases by one unit of wt, the mpg decreases by 0.867
Correlation between mpg and number of cylinder
cor(mtcars$cyl, mtcars$mpg)
[1] -0.852162
- We can see that the relationship between mpg and cyl is strong and negative. As the number of cylinder increases by 2 unit, the mpg decreases by 0.852
Using a linear regression to fit in the dataset
model <- glm(mpg~wt, data=mtcars)
summary(model)
Call:
glm(formula = mpg ~ wt, data = mtcars)
Deviance Residuals:
Min 1Q Median 3Q Max
-4.5432 -2.3647 -0.1252 1.4096 6.8727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
wt -5.3445 0.5591 -9.559 1.29e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 9.277398)
Null deviance: 1126.05 on 31 degrees of freedom
Residual deviance: 278.32 on 30 degrees of freedom
AIC: 166.03
Number of Fisher Scoring iterations: 2