Project 2 MKTG 3P98
Aderinsola Sola-Oladokun
2024-11-12
Team 3 - Aderinsola Sola, Vanessa Cecchini, Robert Hoffman, Fausat Ismail
Set up working directory
getwd()## [1] "/Users/user/Desktop/Rstudio Work"setwd("/Users/user/Desktop/Rstudio Work")Load necessary libraries
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
library(visreg)
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodeRead Data file Car_Total
Car_Total<-read.csv("/Users/user/Car_Total.csv")
names(Car_Total) ## [1] "X.1" "Resp" "Att_1" "Att_2" "Enj_1"
## [6] "Enj_2" "Perform_1" "Perform_2" "Perform_3" "WOM_1"
## [11] "WOM_2" "Futu_Pur_1" "Futu_Pur_2" "Valu_Percp_1" "Valu_Percp_2"
## [16] "Pur_Proces_1" "Pur_Proces_2" "Residence" "Pay_Meth" "Insur_Type"
## [21] "Gender" "Age" "Education" "X" "Region"
## [26] "Model" "MPG" "Cyl" "acc1" "C_cost."
## [31] "H_Cost" "Post.Satis" "Att_Mean" "Make" "Model_v1"
## [36] "Parent"ct<-Car_Total Step 1
Choose the target car brand for statistical analyses
select the subset data on the target brand Dodge
stringr::str_detect(ct$Model, "Dodge") # Select the text values wiht "Dodge" ## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [1045] FALSE FALSE FALSE FALSE FALSEct[str_detect(ct$Model, "Dodge"),] #just see data frame ## X.1 Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1
## 196 196 Res23 7 7 7 6 6 6 2 6
## 207 207 Res24 6 6 6 5 5 6 3 4
## 218 218 Res25 7 7 7 5 5 7 2 7
## 229 229 Res26 2 3 3 2 2 4 5 5
## 240 240 Res27 7 7 7 6 6 6 6 6
## 251 251 Res28 2 2 2 2 2 2 2 2
## 262 262 Res29 5 5 5 4 4 5 5 4
## 274 274 Res30 5 3 3 2 2 3 4 3
## 285 285 Res31 7 7 7 6 6 6 5 4
## 296 296 Res32 7 7 7 6 6 6 2 6
## 307 307 Res33 4 2 3 2 2 3 2 6
## 318 318 Res34 6 6 7 4 5 6 6 4
## 329 329 Res35 5 5 5 5 5 1 1 7
## 340 340 Res36 6 6 5 5 5 5 1 6
## 351 351 Res37 2 2 2 2 2 2 2 2
## 362 362 Res38 6 6 6 5 5 6 2 5
## 373 373 Res39 6 6 6 5 5 5 5 4
## 385 385 Res40 1 1 1 1 1 1 1 1
## 396 396 Res41 1 1 1 1 1 NA 1 1
## 543 543 Res542 6 6 6 5 5 4 4 3
## 544 544 Res543 2 1 1 1 1 2 1 4
## 545 545 Res544 3 4 4 3 2 3 2 4
## 546 546 Res545 1 1 2 1 2 2 1 2
## 547 547 Res546 4 4 3 4 4 4 4 4
## 548 548 Res547 2 2 2 2 1 5 5 4
## 549 549 Res548 1 1 1 2 2 4 2 1
## 550 550 Res549 2 2 2 1 1 1 1 1
## 552 552 Res550 6 6 2 4 4 4 4 5
## 553 553 Res551 2 1 1 2 3 3 3 5
## 554 554 Res552 5 5 5 5 5 3 4 2
## 555 555 Res553 6 6 6 4 4 4 4 5
## 556 556 Res554 7 7 7 6 6 2 4 5
## 557 557 Res555 6 6 6 5 5 5 2 5
## 558 558 Res556 7 7 6 6 7 6 7 7
## 559 559 Res557 7 7 7 7 6 3 6 7
## 560 560 Res558 6 6 6 5 5 2 2 2
## 561 561 Res559 6 4 5 5 3 2 4 5
## 563 563 Res560 6 7 7 2 5 5 5 5
## 587 587 Res582 5 6 6 5 5 5 5 7
## 588 588 Res583 3 3 4 3 4 3 4 7
## 589 589 Res584 6 4 3 2 2 5 2 7
## WOM_2 Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1
## 196 6 3 6 4 3 2
## 207 4 2 2 6 3 3
## 218 6 3 3 5 5 6
## 229 5 2 2 4 7 7
## 240 5 4 4 5 4 5
## 251 2 2 2 7 7 7
## 262 4 5 7 4 7 7
## 274 3 6 5 6 6 6
## 285 2 4 5 4 4 4
## 296 6 5 4 5 4 4
## 307 6 3 3 5 6 6
## 318 5 3 3 3 5 4
## 329 7 3 3 6 1 5
## 340 2 3 3 6 3 4
## 351 2 2 2 2 2 2
## 362 6 5 5 4 5 5
## 373 4 3 3 6 4 4
## 385 1 1 2 2 2 2
## 396 1 3 3 2 2 2
## 543 4 6 6 6 6 6
## 544 6 3 3 6 6 6
## 545 6 7 7 7 6 6
## 546 4 7 6 6 7 7
## 547 4 5 5 5 3 3
## 548 4 4 4 6 7 7
## 549 2 6 6 6 5 5
## 550 1 6 6 6 6 6
## 552 6 6 6 6 5 5
## 553 3 6 6 6 5 5
## 554 5 6 6 6 4 5
## 555 6 5 5 5 5 4
## 556 5 5 5 5 5 5
## 557 5 7 7 6 6 6
## 558 6 6 6 6 6 6
## 559 7 5 5 6 7 6
## 560 6 4 4 4 4 4
## 561 4 3 3 6 7 7
## 563 6 5 5 5 5 5
## 587 5 6 5 6 6 6
## 588 6 6 6 6 6 6
## 589 6 6 6 6 5 5
## Pur_Proces_2 Residence Pay_Meth Insur_Type Gender Age Education X
## 196 4 2 2 Comprehensive Male 26 2 NA
## 207 6 2 1 Female 26 3 NA
## 218 4 1 2 Comprehensive Male 27 3 NA
## 229 7 2 1 Comprehensive Female 27 2 NA
## 240 5 2 1 Comprehensive Male 27 2 NA
## 251 3 1 2 Comprehensive Male 29 1 NA
## 262 2 2 1 Comprehensive Male 32 2 NA
## 274 6 1 2 Comprehensive Male 32 2 NA
## 285 4 1 2 Comprehensive Male 32 2 NA
## 296 4 2 1 Comprehensive Male 32 2 NA
## 307 6 1 2 Collision Male 34 2 NA
## 318 4 1 2 Collision Male 34 1 NA
## 329 1 2 1 Female 34 1 NA
## 340 4 1 2 Collision Male 34 3 NA
## 351 6 2 2 Collision Male 35 2 NA
## 362 4 1 2 Collision Male 35 2 NA
## 373 5 2 1 Collision Male 36 2 NA
## 385 5 2 1 Collision Male 36 2 NA
## 396 5 2 2 Collision Male 36 2 NA
## 543 6 1 1 Comprehensive Female 49 3 NA
## 544 6 2 3 Comprehensive Male 49 2 NA
## 545 6 2 1 Comprehensive Female 50 2 NA
## 546 4 2 2 Comprehensive Male 52 2 NA
## 547 4 1 1 Comprehensive Female 53 2 NA
## 548 7 1 1 Comprehensive Male 53 1 NA
## 549 4 2 3 Comprehensive Male 53 2 NA
## 550 6 2 1 Comprehensive Male 54 2 NA
## 552 6 2 3 Comprehensive Male 55 2 NA
## 553 5 2 2 Comprehensive Male 55 1 NA
## 554 6 2 3 Comprehensive Male 55 2 NA
## 555 6 2 2 Comprehensive Male 56 2 NA
## 556 5 1 3 Comprehensive Male 57 2 NA
## 557 7 1 2 Collision Female 57 2 NA
## 558 6 1 2 Collision Male 57 2 NA
## 559 3 2 2 Collision Male 57 2 NA
## 560 4 1 3 Collision Male 60 2 NA
## 561 6 2 2 Collision Male 60 2 NA
## 563 5 2 2 Collision Male 18 2 NA
## 587 6 2 1 Comprehensive Female 25 1 NA
## 588 5 1 1 Comprehensive Male 26 2 NA
## 589 5 2 1 Comprehensive Male 26 2 NA
## Region Model MPG Cyl acc1 C_cost. H_Cost Post.Satis
## 196 American Dodge Journey 16 6 5.75 12 11 5
## 207 American Dodge Journey 16 6 5.75 12 11 4
## 218 American Dodge Journey 16 6 5.75 12 11 4
## 229 American Dodge Journey 16 6 5.75 12 11 5
## 240 American Dodge Journey 16 6 5.75 12 11 4
## 251 American Dodge Journey 16 6 5.75 12 11 3
## 262 American Dodge Journey 16 6 5.75 12 11 6
## 274 American Dodge Journey 16 6 5.75 12 11 5
## 285 American Dodge Journey 16 6 5.75 12 11 5
## 296 American Dodge Journey 16 6 5.75 12 11 4
## 307 American Dodge Journey 16 6 5.75 12 11 5
## 318 American Dodge Journey 16 6 5.75 12 11 5
## 329 American Dodge Journey 16 6 5.75 12 11 6
## 340 American Dodge Journey 16 6 5.75 12 11 4
## 351 American Dodge Journey 16 6 5.75 12 11 4
## 362 American Dodge Journey 16 6 5.75 12 11 3
## 373 American Dodge Journey 16 6 5.75 12 11 4
## 385 American Dodge Journey 16 6 5.75 12 11 3
## 396 American Dodge Journey 16 6 5.75 12 11 4
## 543 Middle Eastern Dodge Journey 16 6 5.75 12 11 6
## 544 Middle Eastern Dodge Journey 16 6 5.75 12 11 3
## 545 Middle Eastern Dodge Journey 16 6 5.75 12 11 4
## 546 Middle Eastern Dodge Journey 16 6 5.75 12 11 4
## 547 Middle Eastern Dodge Journey 16 6 5.75 12 11 3
## 548 Middle Eastern Dodge Journey 16 6 5.75 12 11 3
## 549 Middle Eastern Dodge Journey 16 6 5.75 12 11 6
## 550 Middle Eastern Dodge Journey 16 6 5.75 12 11 7
## 552 Middle Eastern Dodge Journey 16 6 5.75 12 11 3
## 553 Middle Eastern Dodge Journey 16 6 5.75 12 11 6
## 554 Middle Eastern Dodge Journey 16 6 5.75 12 11 7
## 555 Middle Eastern Dodge Journey 16 6 5.75 12 11 6
## 556 Middle Eastern Dodge Journey 16 6 5.75 12 11 5
## 557 Middle Eastern Dodge Journey 16 6 5.75 12 11 3
## 558 Middle Eastern Dodge Journey 16 6 5.75 12 11 5
## 559 Middle Eastern Dodge Journey 16 6 5.75 12 11 5
## 560 Middle Eastern Dodge Journey 16 6 5.75 12 11 3
## 561 Middle Eastern Dodge Journey 16 6 5.75 12 11 5
## 563 Middle Eastern Dodge Journey 16 6 5.75 12 11 6
## 587 American Dodge Journey 16 6 5.75 12 11 6
## 588 American Dodge Journey 16 6 5.75 12 11 5
## 589 American Dodge Journey 16 6 5.75 12 11 6
## Att_Mean Make Model_v1 Parent
## 196 7.0 Dodge Journey Chrysler
## 207 6.0 Dodge Journey Chrysler
## 218 7.0 Dodge Journey Chrysler
## 229 2.5 Dodge Journey Chrysler
## 240 7.0 Dodge Journey Chrysler
## 251 2.0 Dodge Journey Chrysler
## 262 5.0 Dodge Journey Chrysler
## 274 4.0 Dodge Journey Chrysler
## 285 7.0 Dodge Journey Chrysler
## 296 7.0 Dodge Journey Chrysler
## 307 3.0 Dodge Journey Chrysler
## 318 6.0 Dodge Journey Chrysler
## 329 5.0 Dodge Journey Chrysler
## 340 6.0 Dodge Journey Chrysler
## 351 2.0 Dodge Journey Chrysler
## 362 6.0 Dodge Journey Chrysler
## 373 6.0 Dodge Journey Chrysler
## 385 1.0 Dodge Journey Chrysler
## 396 1.0 Dodge Journey Chrysler
## 543 6.0 Dodge Journey Chrysler
## 544 1.5 Dodge Journey Chrysler
## 545 3.5 Dodge Journey Chrysler
## 546 1.0 Dodge Journey Chrysler
## 547 4.0 Dodge Journey Chrysler
## 548 2.0 Dodge Journey Chrysler
## 549 1.0 Dodge Journey Chrysler
## 550 2.0 Dodge Journey Chrysler
## 552 6.0 Dodge Journey Chrysler
## 553 1.5 Dodge Journey Chrysler
## 554 5.0 Dodge Journey Chrysler
## 555 6.0 Dodge Journey Chrysler
## 556 7.0 Dodge Journey Chrysler
## 557 6.0 Dodge Journey Chrysler
## 558 7.0 Dodge Journey Chrysler
## 559 7.0 Dodge Journey Chrysler
## 560 6.0 Dodge Journey Chrysler
## 561 5.0 Dodge Journey Chrysler
## 563 6.5 Dodge Journey Chrysler
## 587 5.5 Dodge Journey Chrysler
## 588 3.0 Dodge Journey Chrysler
## 589 5.0 Dodge Journey Chryslerct_Dodge<-str_detect(ct$Model, "Dodge") #assign into a new data frame
ct_Dodge #See new data frame ## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [1045] FALSE FALSE FALSE FALSE FALSEsubdodge<-ct[ct_Dodge,] ## assign subset data into a data frame
table(subdodge$Make) ## double check on the models selected ##
## Dodge
## 41Divide age categories
subdodge$AgeGrp<-cut(subdodge$Age,
breaks = c(0, 40, Inf),
Labels = c("Young Adults", "Matured Adults"),
right=FALSE)
names(subdodge)## [1] "X.1" "Resp" "Att_1" "Att_2" "Enj_1"
## [6] "Enj_2" "Perform_1" "Perform_2" "Perform_3" "WOM_1"
## [11] "WOM_2" "Futu_Pur_1" "Futu_Pur_2" "Valu_Percp_1" "Valu_Percp_2"
## [16] "Pur_Proces_1" "Pur_Proces_2" "Residence" "Pay_Meth" "Insur_Type"
## [21] "Gender" "Age" "Education" "X" "Region"
## [26] "Model" "MPG" "Cyl" "acc1" "C_cost."
## [31] "H_Cost" "Post.Satis" "Att_Mean" "Make" "Model_v1"
## [36] "Parent" "AgeGrp"head(subdodge$AgeGrp)## [1] [0,40) [0,40) [0,40) [0,40) [0,40) [0,40)
## Levels: [0,40) [40,Inf)Independent sample t-test (Age Group by Post Purchase Satisfaction)
boxplot(subdodge$Post.Satis ~ subdodge$AgeGrp, col=c(5,7)) #visually check normality of residuals 
shapiro.test(subdodge$Post.Satis) #Pvalue < 0.05 means is significance ##
## Shapiro-Wilk normality test
##
## data: subdodge$Post.Satis
## W = 0.90319, p-value = 0.002063res_aov<-aov(Post.Satis ~ AgeGrp, data=subdodge)
res_aov## Call:
## aov(formula = Post.Satis ~ AgeGrp, data = subdodge)
##
## Terms:
## AgeGrp Residuals
## Sum of Squares 0.03393 57.47826
## Deg. of Freedom 1 39
##
## Residual standard error: 1.214002
## Estimated effects may be unbalancedHistogram
hist(res_aov$residuals)
#### Create QQ Plot (visually check normaliity of the data)
qqnorm(res_aov$residuals, pch=1, frame=FALSE)
qqline(res_aov$residuals, col="blue", lwd=4)
#### Test normality of the residuals
shapiro.test(res_aov$residuals)##
## Shapiro-Wilk normality test
##
## data: res_aov$residuals
## W = 0.91142, p-value = 0.003668Barlett test of homogeneity of variances
bartlett.test(subdodge$Post.Satis, subdodge$AgeGrp)##
## Bartlett test of homogeneity of variances
##
## data: subdodge$Post.Satis and subdodge$AgeGrp
## Bartlett's K-squared = 2.8247, df = 1, p-value = 0.09282Research Question:
- Null Hypothesis (H₀): There is no difference in the mean Post Purchase satisfaction scores between the two age groups ([0,40) and [40,Inf)).
- Alternative Hypothesis (H₁): There is a difference in the mean Post purchase satisfaction scores between the two age group
t.test(Post.Satis~AgeGrp, data=subdodge, var.eq=TRUE)##
## Two Sample t-test
##
## data: Post.Satis by AgeGrp
## t = -0.15174, df = 39, p-value = 0.8802
## alternative hypothesis: true difference in means between group [0,40) and group [40,Inf) is not equal to 0
## 95 percent confidence interval:
## -0.8307242 0.7147821
## sample estimates:
## mean in group [0,40) mean in group [40,Inf)
## 4.608696 4.666667One way anova
Independent variable: Age Grp
Dependent variable: Perform_1
- H0: There is no significant difference for the usefulness of the vehicle across Age Groups
- H1 There is significant difference for the usefulness of the vehicle across Age Groups
tapply(subdodge$Perform_1,subdodge$AgeGrp,shapiro.test)## $`[0,40)`
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.82963, p-value = 0.001188
##
##
## $`[40,Inf)`
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.93601, p-value = 0.2473Check homogeneity
bartlett.test(subdodge$Perform_1, subdodge$AgeGrp) #Bartlett test of homogeneity of variances ##
## Bartlett test of homogeneity of variances
##
## data: subdodge$Perform_1 and subdodge$AgeGrp
## Bartlett's K-squared = 0.10308, df = 1, p-value = 0.7482ANOVA
aov_perform_equal <- aov(Perform_1 ~ as.factor(AgeGrp), var.equal = FALSE, data=subdodge)## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'var.equal' will be disregardedsummary(aov_perform_equal)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(AgeGrp) 1 0.85 0.848 0.261 0.613
## Residuals 39 126.96 3.255#Pairwise t-tests With no assumption ot equal variances
pairwise.t.test(subdodge$Att_1, subdodge$AgeGrp, p.adjust.methods = "BH" , pool.sd = FALSE)##
## Pairwise comparisons using t tests with non-pooled SD
##
## data: subdodge$Att_1 and subdodge$AgeGrp
##
## [0,40)
## [40,Inf) 0.48
##
## P value adjustment method: holmStep 2
ggplot(subdodge,aes(x=Age, fill = Region,)) +
theme_bw()+
geom_bar()+
geom_text(stat="count", aes(label=..count..), vjust=0) +
labs(y="number of Participants",
x = "Age",
title ="Age")## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggplot(subdodge,aes(x=Pay_Meth, fill = Region,)) +
theme_bw()+
geom_bar()+
geom_text(stat="count", aes(label=..count..), vjust=0) +
labs(y="number of Participants",
x = "Payment Method",
title ="Payment Method")
ggplot(subdodge,aes(x=Insur_Type, fill = Region,)) +
theme_bw()+
geom_bar()+
geom_text(stat="count", aes(label=..count..), vjust=0) +
labs(y="number of Participants",
x = "Insur_Type",
title ="Insur_Type")
## Step 3
data_lb<-subdodgeMultiple Linear Regression
model3<-lm(Age ~ Futu_Pur_1+Post.Satis+Enj_1, data = data_lb)
summary(model3)##
## Call:
## lm(formula = Age ~ Futu_Pur_1 + Post.Satis + Enj_1, data = data_lb)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.5982 -7.0996 -0.6879 6.6725 25.6311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.7874 8.3568 4.522 6.11e-05 ***
## Futu_Pur_1 3.7224 1.1985 3.106 0.00363 **
## Post.Satis -1.8550 1.6449 -1.128 0.26671
## Enj_1 -1.0621 0.8602 -1.235 0.22470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.66 on 37 degrees of freedom
## Multiple R-squared: 0.2343, Adjusted R-squared: 0.1722
## F-statistic: 3.773 on 3 and 37 DF, p-value: 0.01852Visual inspection of Linearity
visreg(model3)
#### visualizing their relationships
ggplot(data_lb)+aes(x=Futu_Pur_1, y= Post.Satis, size = Enj_1)+
geom_point()+ scale_color_gradient()+
labs (y = "Future Purchase", x= "Post Purchase Satisfaction", color = "Enjoyment")+
theme_minimal()
vif(model3) # check if variance inflation factor (vif) <10## Futu_Pur_1 Post.Satis Enj_1
## 1.120217 1.143813 1.023011Check linearity of residuels
plot(model3, which = 1)
#### Use a qq plot
plot(model3, which = 2)
#### Use shapiro-wilk test
shapiro.test(residuals(model3))##
## Shapiro-Wilk normality test
##
## data: residuals(model3)
## W = 0.98162, p-value = 0.7366Check HOmoscedacity (Contant Variance )
plot(model3, which=3)