Question

What appear to be the 3-4 most important car specifications for predicting the car’s price? using the toyotacorolla file

Answer 1

Use the following code in R to fit the model

a <- read.csv("C:\\Users\\Vibhor\\Desktop\\ToyotaCorolla.csv")  ## according to your location of file
attach(a)
corolla <- a[c("Price","Age_08_04","KM","HP","cc","Doors","Gears","Quarterly_Tax","Weight")]
View(corolla)
summary(corolla)
model <- lm(Price~., data = corolla)
summary(model)

OUTPUT OF SUMMARY OF THE MODEL
## 
## Call:
## lm(formula = Price ~ ., data = corolla)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9366.4  -793.3   -21.3   799.7  6444.0 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -5.573e+03  1.411e+03  -3.949 8.24e-05 ***
## Age_08_04     -1.217e+02  2.616e+00 -46.512  < 2e-16 ***
## KM            -2.082e-02  1.252e-03 -16.622  < 2e-16 ***
## HP             3.168e+01  2.818e+00  11.241  < 2e-16 ***
## cc            -1.211e-01  9.009e-02  -1.344  0.17909    
## Doors         -1.617e+00  4.001e+01  -0.040  0.96777    
## Gears          5.943e+02  1.971e+02   3.016  0.00261 ** 
## Quarterly_Tax  3.949e+00  1.310e+00   3.015  0.00262 ** 
## Weight         1.696e+01  1.068e+00  15.880  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1342 on 1427 degrees of freedom
## Multiple R-squared:  0.8638, Adjusted R-squared:  0.863 
## F-statistic:  1131 on 8 and 1427 DF,  p-value: < 2.2e-16

We can see that which variables are significant from their p values (have star in them),

the variables are: Age_08_04, KM, HP, Gears, Quarterly_Tax , Weight