Logistic Regression - Tire Ratings

##Project Objective

To help drivers with their tire experience

Question 1 & 2

Step 1: Install and Load Required libraries

library(readxl)
library(Hmisc)

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

library(pscl)

## Classes and Methods for R originally developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University (2002-2015),
## by and under the direction of Simon Jackman.
## hurdle and zeroinfl functions by Achim Zeileis.

Step 2: Import & clean the data

tire_df <- read_excel("Class Exercise 15_TireRatings.xlsx")
ti_df <- subset(tire_df,select = -c(Tire))

Step 3: Summarize the Data

head(ti_df)

## # A tibble: 6 × 4
##     Wet Noise Buy_Again Purchase
##   <dbl> <dbl>     <dbl>    <dbl>
## 1   8     7.2       6.1        0
## 2   8     7.2       6.6        1
## 3   7.6   7.5       6.9        1
## 4   6.6   5.4       6.6        0
## 5   5.8   6.3       4          0
## 6   6.3   5.7       4.5        0

summary(ti_df)

##       Wet            Noise         Buy_Again        Purchase     
##  Min.   :4.300   Min.   :3.600   Min.   :1.400   Min.   :0.0000  
##  1st Qu.:6.450   1st Qu.:6.000   1st Qu.:3.850   1st Qu.:0.0000  
##  Median :7.750   Median :7.100   Median :6.150   Median :0.0000  
##  Mean   :7.315   Mean   :6.903   Mean   :5.657   Mean   :0.4412  
##  3rd Qu.:8.225   3rd Qu.:7.925   3rd Qu.:7.400   3rd Qu.:1.0000  
##  Max.   :9.200   Max.   :8.900   Max.   :8.900   Max.   :1.0000

Step 4: Correlation Analysis

corr <- rcorr(as.matrix(ti_df))
corr

##            Wet Noise Buy_Again Purchase
## Wet       1.00  0.76      0.91     0.74
## Noise     0.76  1.00      0.83     0.72
## Buy_Again 0.91  0.83      1.00     0.83
## Purchase  0.74  0.72      0.83     1.00
## 
## n= 68 
## 
## 
## P
##           Wet Noise Buy_Again Purchase
## Wet            0     0         0      
## Noise      0         0         0      
## Buy_Again  0   0               0      
## Purchase   0   0     0

Step 5: Build the Logistic Regression Model

model <- glm(Purchase ~ Wet + Noise, data = ti_df, family = binomial)
summary(model)

## 
## Call:
## glm(formula = Purchase ~ Wet + Noise, family = binomial, data = ti_df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.90595  -0.07829  -0.00213   0.21082   2.25564  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -39.4982    12.4779  -3.165  0.00155 **
## Wet           3.3745     1.2641   2.670  0.00760 **
## Noise         1.8163     0.8312   2.185  0.02887 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 93.325  on 67  degrees of freedom
## Residual deviance: 27.530  on 65  degrees of freedom
## AIC: 33.53
## 
## Number of Fisher Scoring iterations: 8

Step 6: Pseudo-R Squared

pR2(model)

## fitting null model for pseudo-r2

##         llh     llhNull          G2    McFadden        r2ML        r2CU 
## -13.7649516 -46.6623284  65.7947536   0.7050093   0.6199946   0.8305269

Interpretation: A McFadden R-squared of .705 means that our LR model explains about 37.3% of the variability in the outcome relative to a model with no predictors.This is considered a very good fit

Step 7: Predicting with New Information

new_data1 <- data.frame(Wet = 8, Noise = 8)
prob1 <- predict(model, newdata = new_data1, type = "response")
round((prob1 * 100),2)

##     1 
## 88.37

Interpretation: There is an 88.37% likelihood that the driver will purchase the tire again if it was rated 8 for wet traction performance and it's noise level.

new_data2 <- data.frame(Wet = 7, Noise = 7)
prob2 <- predict(model, newdata = new_data2, type = "response")
round((prob2 * 100),2)

##    1 
## 4.06

Interpretation: There is an 4.06% likelihood that the driver will purchase the tire again if it was rated 7 for wet traction performance and it's noise level.