Project Objective
To investigate the relationship between the Wet and Noise performance ratings and their impact on Purchase
Step 1: Load the required libraries
library(readxl) #allows us to import excel file
## Warning: 套件 'readxl' 是用 R 版本 4.3.3 來建造的
library(Hmisc) #allows us to call the correlation function
## Warning: 套件 'Hmisc' 是用 R 版本 4.3.3 來建造的
##
## 載入套件：'Hmisc'
## 下列物件被遮斷自 'package:base':
##
## format.pval, units
library(pscl) #allows us to call the pseudo R-square package to evaluate our model
## Warning: 套件 'pscl' 是用 R 版本 4.3.3 來建造的
## 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.
library(pROC)
## Warning: 套件 'pROC' 是用 R 版本 4.3.3 來建造的
## Type 'citation("pROC")' for a citation.
##
## 載入套件：'pROC'
## 下列物件被遮斷自 'package:stats':
##
## cov, smooth, var
Step 2: Import and clean the data
df<-read_excel("Class Exercise 15_TireRatings.xlsx")
coll_df<- subset(df, select= -c(Buy_Again,Tire)) #drop irrelevant column
Step 3: Summarize the data
head(coll_df)
## # A tibble: 6 × 3
## Wet Noise Purchase
## <dbl> <dbl> <dbl>
## 1 8 7.2 0
## 2 8 7.2 1
## 3 7.6 7.5 1
## 4 6.6 5.4 0
## 5 5.8 6.3 0
## 6 6.3 5.7 0
summary(coll_df)
## Wet Noise Purchase
## Min. :4.300 Min. :3.600 Min. :0.0000
## 1st Qu.:6.450 1st Qu.:6.000 1st Qu.:0.0000
## Median :7.750 Median :7.100 Median :0.0000
## Mean :7.315 Mean :6.903 Mean :0.4412
## 3rd Qu.:8.225 3rd Qu.:7.925 3rd Qu.:1.0000
## Max. :9.200 Max. :8.900 Max. :1.0000
Step 4: Feature selection (correlation analysis)
corr<- rcorr(as.matrix(coll_df))
corr
## Wet Noise Purchase
## Wet 1.00 0.76 0.74
## Noise 0.76 1.00 0.72
## Purchase 0.74 0.72 1.00
##
## n= 68
##
##
## P
## Wet Noise Purchase
## Wet 0 0
## Noise 0 0
## Purchase 0 0
Interpretation: All the predictors are significant with the target variable
Step 5: Logistic Regression Model
model<-glm(Purchase~ Wet+Noise, family=binomial, data=df)
summary(model)
##
## Call:
## glm(formula = Purchase ~ Wet + Noise, family = binomial, data = df)
##
## 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 Overall Model Significance