Project 1 A
Elyse
2025-01-29
Introduction
I am analyzing the data as a member of the Chryler corporation. I am completing a current consumers car purchasing trends analysis across different countries. Working closely with the Sales and Marketing teams, we will use the key data analytics and visualizations to then inform the strategic planning process at the company to improve knowledge and sales.
setwd("~/Documents/year 3/semester 2/MKTG 3P98/Rstudio/assignment 1")Data Files
The following included the data being compared, merged together, and cleaning of data in order to get a more comprehensive overall view of the survey responses.
Car_Survey_1<-read.csv("Car_Survey_1.csv")
str(Car_Survey_1)## 'data.frame': 1180 obs. of 23 variables:
## $ Resp : chr "Res1" "Res2" "Res3" "Res4" ...
## $ Att_1 : int 6 7 7 4 6 6 1 6 3 6 ...
## $ Att_2 : int 6 5 7 1 6 6 1 5 2 6 ...
## $ Enj_1 : int 6 5 7 1 6 6 1 5 3 4 ...
## $ Enj_2 : int 6 2 5 1 5 5 1 3 2 4 ...
## $ Perform_1 : int 5 2 5 1 5 5 2 5 2 4 ...
## $ Perform_2 : int 6 6 5 1 2 5 2 5 3 4 ...
## $ Perform_3 : int 3 7 3 1 1 7 2 2 1 1 ...
## $ WOM_1 : int 3 5 6 7 7 5 2 4 6 5 ...
## $ WOM_2 : int 3 5 6 7 7 5 3 6 6 6 ...
## $ Futu_Pur_1 : int 3 6 7 3 7 7 5 4 7 6 ...
## $ Futu_Pur_2 : int 3 6 7 3 6 7 2 4 7 6 ...
## $ Valu_Percp_1: int 5 6 5 6 6 7 2 4 6 6 ...
## $ Valu_Percp_2: int 2 7 7 5 5 7 2 4 6 6 ...
## $ Pur_Proces_1: int 6 7 7 5 6 7 2 4 6 6 ...
## $ Pur_Proces_2: int 4 6 7 4 7 7 6 4 6 6 ...
## $ Residence : int 2 2 1 2 1 2 2 1 2 1 ...
## $ Pay_Meth : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Insur_Type : chr "Collision" "Collision" "Collision" "Collision" ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Age : int 18 18 19 19 19 19 19 21 21 21 ...
## $ Education : int 2 2 2 2 2 2 2 2 2 2 ...
## $ X : logi NA NA NA NA NA NA ...head(Car_Survey_1, n=5)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res2 7 5 5 2 2 6 7 5 5
## 3 Res3 7 7 7 5 5 5 3 6 6
## 4 Res4 4 1 1 1 1 1 1 7 7
## 5 Res5 6 6 6 5 5 2 1 7 7
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 7 7 6
## 3 7 7 5 7 7 7
## 4 3 3 6 5 5 4
## 5 7 6 6 5 6 7
## Residence Pay_Meth Insur_Type Gender Age Education X
## 1 2 2 Collision Male 18 2 NA
## 2 2 2 Collision Male 18 2 NA
## 3 1 2 Collision Male 19 2 NA
## 4 2 2 Collision Male 19 2 NA
## 5 1 2 Collision Female 19 2 NAView(Car_Survey_1)
Car_Survey_2<-read.csv("Car_Survey_2.csv")
str(Car_Survey_2)## 'data.frame': 1049 obs. of 9 variables:
## $ Respondents: chr "Res1" "Res2" "Res3" "Res4" ...
## $ Region : chr "European" "European" "European" "European" ...
## $ Model : chr "Ford Expedition" "Ford Expedition" "Ford Expedition" "Ford Expedition" ...
## $ MPG : int 15 15 15 15 15 15 15 15 15 15 ...
## $ Cyl : int 8 8 8 8 8 8 8 8 8 8 ...
## $ acc1 : num 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 ...
## $ C_cost. : num 16 16 16 16 16 16 16 16 16 16 ...
## $ H_Cost : num 14 14 14 14 14 14 14 14 14 14 ...
## $ Post.Satis : int 4 3 5 5 5 3 3 6 3 5 ...head(Car_Survey_2, n=5)## Respondents Region Model MPG Cyl acc1 C_cost. H_Cost Post.Satis
## 1 Res1 European Ford Expedition 15 8 5.5 16 14 4
## 2 Res2 European Ford Expedition 15 8 5.5 16 14 3
## 3 Res3 European Ford Expedition 15 8 5.5 16 14 5
## 4 Res4 European Ford Expedition 15 8 5.5 16 14 5
## 5 Res5 European Ford Expedition 15 8 5.5 16 14 5View(Car_Survey_2)
names(Car_Survey_2)[1]<-c("Resp")
head(Car_Survey_2, n=1)## Resp Region Model MPG Cyl acc1 C_cost. H_Cost Post.Satis
## 1 Res1 European Ford Expedition 15 8 5.5 16 14 4#merge
Car_Total<-merge(Car_Survey_1, Car_Survey_2, by="Resp")
str(Car_Total)## 'data.frame': 1049 obs. of 31 variables:
## $ Resp : chr "Res1" "Res10" "Res100" "Res1000" ...
## $ Att_1 : int 6 6 6 6 6 3 2 7 2 6 ...
## $ Att_2 : int 6 6 7 6 6 1 2 7 1 6 ...
## $ Enj_1 : int 6 4 7 7 7 4 1 7 2 6 ...
## $ Enj_2 : int 6 4 3 6 6 3 2 6 1 5 ...
## $ Perform_1 : int 5 4 5 6 6 5 2 5 2 5 ...
## $ Perform_2 : int 6 4 6 6 6 6 2 6 2 5 ...
## $ Perform_3 : int 3 1 6 6 6 6 1 5 2 5 ...
## $ WOM_1 : int 3 5 3 6 4 2 6 6 7 3 ...
## $ WOM_2 : int 3 6 5 6 4 6 7 6 7 3 ...
## $ Futu_Pur_1 : int 3 6 6 6 4 6 6 6 7 6 ...
## $ Futu_Pur_2 : int 3 6 6 6 6 6 5 7 7 6 ...
## $ Valu_Percp_1: int 5 6 7 4 5 5 4 6 4 5 ...
## $ Valu_Percp_2: int 2 6 6 6 6 4 4 5 6 6 ...
## $ Pur_Proces_1: int 6 6 5 6 6 5 4 5 6 6 ...
## $ Pur_Proces_2: int 4 6 5 3 7 5 5 5 7 5 ...
## $ Residence : int 2 1 2 2 1 1 1 2 1 2 ...
## $ Pay_Meth : int 2 2 1 3 3 3 3 3 3 3 ...
## $ Insur_Type : chr "Collision" "Collision" "Collision" "Liability" ...
## $ Gender : chr "Male" "Male" "Female" "Female" ...
## $ Age : int 18 21 32 24 24 25 26 26 27 27 ...
## $ Education : int 2 2 1 2 2 2 2 2 2 2 ...
## $ X : logi NA NA NA NA NA NA ...
## $ Region : chr "European" "European" "American" "Asian" ...
## $ Model : chr "Ford Expedition" "Ford Expedition" "Toyota Rav4" "Toyota Corolla" ...
## $ MPG : int 15 15 24 26 26 26 26 26 26 26 ...
## $ Cyl : int 8 8 4 4 4 4 4 4 4 4 ...
## $ acc1 : num 5.5 5.5 8.2 8 8 8 8 8 8 8 ...
## $ C_cost. : num 16 16 10 7 7 7 7 7 7 7 ...
## $ H_Cost : num 14 14 8 6 6 6 6 6 6 6 ...
## $ Post.Satis : int 4 5 4 6 5 6 5 6 7 6 ...write.csv(Car_Total, "Car_Total", row.names = FALSE)
View(Car_Total)Created a new attribute in order to get rid of inconclusive data. Once the mean of the two attribute columns were found, the data was analyzed and displayed to determine the disprution of cars across different regions.
summary(Car_Total)## Resp Att_1 Att_2 Enj_1
## Length:1049 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Mode :character Median :6.000 Median :6.000 Median :6.000
## Mean :4.882 Mean :5.287 Mean :5.378
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000
## NA's :4 NA's :4
## Enj_2 Perform_1 Perform_2 Perform_3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.575 Mean :4.947 Mean :4.831 Mean :4.217
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## NA's :4 NA's :2 NA's :4 NA's :1
## WOM_1 WOM_2 Futu_Pur_1 Futu_Pur_2 Valu_Percp_1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.00 Median :6.000 Median :6.000 Median :6.000
## Mean :5.286 Mean :5.35 Mean :5.321 Mean :5.371 Mean :5.411
## 3rd Qu.:7.000 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.00 Max. :9.000 Max. :7.000 Max. :7.000
## NA's :1 NA's :3 NA's :5 NA's :2 NA's :4
## Valu_Percp_2 Pur_Proces_1 Pur_Proces_2 Residence
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:1.000
## Median :5.000 Median :6.000 Median :5.000 Median :1.000
## Mean :5.114 Mean :5.256 Mean :4.923 Mean :1.474
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:2.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :5.000
## NA's :1 NA's :3 NA's :4 NA's :5
## Pay_Meth Insur_Type Gender Age
## Min. :1.000 Length:1049 Length:1049 Min. :18.00
## 1st Qu.:1.000 Class :character Class :character 1st Qu.:23.00
## Median :2.000 Mode :character Mode :character Median :34.00
## Mean :2.153 Mean :35.22
## 3rd Qu.:3.000 3rd Qu.:48.00
## Max. :3.000 Max. :60.00
##
## Education X Region Model
## Min. :1.000 Mode:logical Length:1049 Length:1049
## 1st Qu.:2.000 NA's:1049 Class :character Class :character
## Median :2.000 Mode :character Mode :character
## Mean :1.989
## 3rd Qu.:2.000
## Max. :3.000
##
## MPG Cyl acc1 C_cost. H_Cost
## Min. :14.00 Min. :4.0 Min. :3.600 Min. : 7.00 Min. : 6.000
## 1st Qu.:17.00 1st Qu.:4.0 1st Qu.:5.100 1st Qu.:10.00 1st Qu.: 8.000
## Median :19.00 Median :6.0 Median :6.500 Median :12.00 Median :10.000
## Mean :19.58 Mean :5.8 Mean :6.202 Mean :11.35 Mean : 9.634
## 3rd Qu.:22.00 3rd Qu.:6.0 3rd Qu.:7.500 3rd Qu.:13.00 3rd Qu.:11.000
## Max. :26.00 Max. :8.0 Max. :8.500 Max. :16.00 Max. :14.000
##
## Post.Satis
## Min. :2.00
## 1st Qu.:5.00
## Median :6.00
## Mean :5.28
## 3rd Qu.:6.00
## Max. :7.00
## mean(Car_Total$Att_1)## [1] NAmeanATT1<-mean(Car_Total$Att_1,na.rm=TRUE)
Car_Total[is.na(Car_Total$Att_1),"Att_1"]<-meanATT1
Car_Total[is.na(Car_Total)] <-mean(Car_Total, na.rm = TRUE)## Warning in mean.default(Car_Total, na.rm = TRUE): argument is not numeric or
## logical: returning NAsummary(Car_Total)## Resp Att_1 Att_2 Enj_1
## Length:1049 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Mode :character Median :5.000 Median :6.000 Median :6.000
## Mean :4.882 Mean :5.287 Mean :5.378
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000
## NA's :4
## Enj_2 Perform_1 Perform_2 Perform_3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.575 Mean :4.947 Mean :4.831 Mean :4.217
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## NA's :4 NA's :2 NA's :4 NA's :1
## WOM_1 WOM_2 Futu_Pur_1 Futu_Pur_2 Valu_Percp_1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.00 Median :6.000 Median :6.000 Median :6.000
## Mean :5.286 Mean :5.35 Mean :5.321 Mean :5.371 Mean :5.411
## 3rd Qu.:7.000 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.00 Max. :9.000 Max. :7.000 Max. :7.000
## NA's :1 NA's :3 NA's :5 NA's :2 NA's :4
## Valu_Percp_2 Pur_Proces_1 Pur_Proces_2 Residence
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:1.000
## Median :5.000 Median :6.000 Median :5.000 Median :1.000
## Mean :5.114 Mean :5.256 Mean :4.923 Mean :1.474
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:2.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :5.000
## NA's :1 NA's :3 NA's :4 NA's :5
## Pay_Meth Insur_Type Gender Age
## Min. :1.000 Length:1049 Length:1049 Min. :18.00
## 1st Qu.:1.000 Class :character Class :character 1st Qu.:23.00
## Median :2.000 Mode :character Mode :character Median :34.00
## Mean :2.153 Mean :35.22
## 3rd Qu.:3.000 3rd Qu.:48.00
## Max. :3.000 Max. :60.00
##
## Education X Region Model
## Min. :1.000 Min. : NA Length:1049 Length:1049
## 1st Qu.:2.000 1st Qu.: NA Class :character Class :character
## Median :2.000 Median : NA Mode :character Mode :character
## Mean :1.989 Mean :NaN
## 3rd Qu.:2.000 3rd Qu.: NA
## Max. :3.000 Max. : NA
## NA's :1049
## MPG Cyl acc1 C_cost. H_Cost
## Min. :14.00 Min. :4.0 Min. :3.600 Min. : 7.00 Min. : 6.000
## 1st Qu.:17.00 1st Qu.:4.0 1st Qu.:5.100 1st Qu.:10.00 1st Qu.: 8.000
## Median :19.00 Median :6.0 Median :6.500 Median :12.00 Median :10.000
## Mean :19.58 Mean :5.8 Mean :6.202 Mean :11.35 Mean : 9.634
## 3rd Qu.:22.00 3rd Qu.:6.0 3rd Qu.:7.500 3rd Qu.:13.00 3rd Qu.:11.000
## Max. :26.00 Max. :8.0 Max. :8.500 Max. :16.00 Max. :14.000
##
## Post.Satis
## Min. :2.00
## 1st Qu.:5.00
## Median :6.00
## Mean :5.28
## 3rd Qu.:6.00
## Max. :7.00
## 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, unionCar_Total<-Car_Total%>%
mutate(across(everything(),~ ifelse(is.na(.), mean(., na.rm=TRUE), .)))
summary(Car_Total)## Resp Att_1 Att_2 Enj_1
## Length:1049 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:5.000
## Mode :character Median :5.000 Median :6.000 Median :6.000
## Mean :4.882 Mean :5.287 Mean :5.378
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000
##
## Enj_2 Perform_1 Perform_2 Perform_3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.575 Mean :4.947 Mean :4.831 Mean :4.217
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## WOM_1 WOM_2 Futu_Pur_1 Futu_Pur_2 Valu_Percp_1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.00 Median :6.000 Median :6.000 Median :6.000
## Mean :5.286 Mean :5.35 Mean :5.321 Mean :5.371 Mean :5.411
## 3rd Qu.:7.000 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.00 Max. :9.000 Max. :7.000 Max. :7.000
##
## Valu_Percp_2 Pur_Proces_1 Pur_Proces_2 Residence
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:1.000
## Median :5.000 Median :6.000 Median :5.000 Median :1.000
## Mean :5.114 Mean :5.256 Mean :4.923 Mean :1.474
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:2.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :5.000
##
## Pay_Meth Insur_Type Gender Age
## Min. :1.000 Length:1049 Length:1049 Min. :18.00
## 1st Qu.:1.000 Class :character Class :character 1st Qu.:23.00
## Median :2.000 Mode :character Mode :character Median :34.00
## Mean :2.153 Mean :35.22
## 3rd Qu.:3.000 3rd Qu.:48.00
## Max. :3.000 Max. :60.00
##
## Education X Region Model
## Min. :1.000 Min. : NA Length:1049 Length:1049
## 1st Qu.:2.000 1st Qu.: NA Class :character Class :character
## Median :2.000 Median : NA Mode :character Mode :character
## Mean :1.989 Mean :NaN
## 3rd Qu.:2.000 3rd Qu.: NA
## Max. :3.000 Max. : NA
## NA's :1049
## MPG Cyl acc1 C_cost. H_Cost
## Min. :14.00 Min. :4.0 Min. :3.600 Min. : 7.00 Min. : 6.000
## 1st Qu.:17.00 1st Qu.:4.0 1st Qu.:5.100 1st Qu.:10.00 1st Qu.: 8.000
## Median :19.00 Median :6.0 Median :6.500 Median :12.00 Median :10.000
## Mean :19.58 Mean :5.8 Mean :6.202 Mean :11.35 Mean : 9.634
## 3rd Qu.:22.00 3rd Qu.:6.0 3rd Qu.:7.500 3rd Qu.:13.00 3rd Qu.:11.000
## Max. :26.00 Max. :8.0 Max. :8.500 Max. :16.00 Max. :14.000
##
## Post.Satis
## Min. :2.00
## 1st Qu.:5.00
## Median :6.00
## Mean :5.28
## 3rd Qu.:6.00
## Max. :7.00
## Car_Total$Att_Mean=(Car_Total$Att_1+Car_Total$Att_2)/2
View(Car_Total)
Car_Total<-Car_Total %>% mutate(across(where(is.numeric), round))
print(Car_Total) ## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res10 6 6 4 4 4 4 1 5 6
## 3 Res100 6 7 7 3 5 6 6 3 5
## 4 Res1000 6 6 7 6 6 6 6 6 6
## 5 Res1001 6 6 7 6 6 6 6 4 4
## 6 Res1002 3 1 4 3 5 6 6 2 6
## 7 Res1003 2 2 1 2 2 2 1 6 7
## 8 Res1004 7 7 7 6 5 6 5 6 6
## 9 Res1005 2 1 2 1 2 2 2 7 7
## 10 Res1006 6 6 6 5 5 5 5 3 3
## 11 Res1007 4 4 4 2 3 5 3 7 7
## 12 Res1008 5 5 5 3 3 5 5 5 5
## 13 Res1009 4 4 7 7 7 7 7 7 7
## 14 Res101 6 6 7 6 5 6 3 5 6
## 15 Res1010 7 7 7 4 7 4 4 5 5
## 16 Res1011 7 7 7 2 6 2 5 6 6
## 17 Res1012 6 6 6 6 5 3 4 5 5
## 18 Res1013 4 4 5 1 1 3 4 7 6
## 19 Res1014 5 5 5 4 3 5 5 5 5
## 20 Res1015 4 5 5 5 4 7 5 7 7
## 21 Res1016 6 6 7 7 6 7 6 7 7
## 22 Res1017 6 6 7 5 5 6 5 7 7
## 23 Res1018 4 3 4 2 3 5 4 7 6
## 24 Res1019 6 6 6 5 5 4 4 4 4
## 25 Res102 7 7 7 6 6 6 1 7 7
## 26 Res1020 6 7 6 6 6 5 5 7 7
## 27 Res1021 6 6 6 3 5 6 5 6 6
## 28 Res1022 6 6 6 4 4 7 7 5 5
## 29 Res1023 7 7 6 6 6 5 5 6 6
## 30 Res1024 6 5 4 5 4 6 5 6 7
## 31 Res1025 7 7 7 6 6 6 6 7 7
## 32 Res1026 6 7 7 7 7 6 6 7 7
## 33 Res1027 7 7 7 6 6 7 7 7 7
## 34 Res1028 4 7 7 6 7 6 7 7 6
## 35 Res1029 7 7 7 7 7 7 7 7 7
## 36 Res103 7 7 7 6 6 7 2 6 6
## 37 Res1030 7 7 7 7 7 7 7 7 7
## 38 Res1031 6 6 6 5 5 4 4 6 6
## 39 Res1032 7 6 6 5 6 6 6 7 7
## 40 Res1033 7 6 5 6 6 6 6 7 7
## 41 Res1034 7 7 7 7 7 6 5 7 6
## 42 Res1035 7 7 7 7 7 7 7 7 7
## 43 Res1036 6 5 6 5 5 6 6 7 7
## 44 Res1037 6 7 6 6 7 5 5 5 5
## 45 Res1038 6 5 5 5 5 5 5 5 5
## 46 Res1039 6 6 7 5 5 4 5 5 5
## 47 Res104 7 7 7 7 7 6 6 3 4
## 48 Res1040 5 6 5 6 6 4 4 7 7
## 49 Res1041 4 4 4 3 5 6 6 2 6
## 50 Res1042 5 5 1 2 2 2 1 6 7
## 51 Res1043 5 4 7 6 5 6 5 6 6
## 52 Res1044 4 4 2 1 2 2 2 7 7
## 53 Res1045 3 3 6 5 5 5 5 3 3
## 54 Res1046 3 4 4 2 3 5 3 7 7
## 55 Res1047 4 3 3 2 2 2 2 3 4
## 56 Res1048 3 4 4 3 4 4 4 4 5
## 57 Res1049 3 4 4 5 5 5 5 4 5
## 58 Res105 7 6 6 5 5 5 1 6 7
## 59 Res106 6 7 6 7 7 5 6 7 7
## 60 Res107 7 7 7 7 7 7 2 5 5
## 61 Res108 6 7 7 6 6 7 5 5 4
## 62 Res109 6 6 6 5 5 6 4 5 4
## 63 Res11 4 4 4 2 1 4 2 6 6
## 64 Res110 7 7 7 7 7 6 2 6 6
## 65 Res111 6 7 7 7 7 6 3 5 5
## 66 Res112 6 6 6 5 5 6 6 7 7
## 67 Res113 5 4 4 6 7 5 6 3 4
## 68 Res114 6 5 5 5 5 5 5 5 5
## 69 Res115 5 5 5 5 5 5 5 6 6
## 70 Res116 6 7 7 6 6 4 3 6 6
## 71 Res117 6 6 7 5 6 3 5 6 6
## 72 Res118 6 6 5 6 6 6 6 6 7
## 73 Res119 6 6 5 5 4 4 4 4 4
## 74 Res12 3 2 3 2 2 3 7 5 5
## 75 Res120 6 6 6 5 5 5 3 6 6
## 76 Res121 7 7 7 6 6 6 1 7 7
## 77 Res122 6 7 6 3 5 5 3 5 6
## 78 Res123 7 7 7 7 7 7 5 4 4
## 79 Res124 7 7 7 7 7 7 3 6 5
## 80 Res125 6 6 5 4 4 5 6 6 6
## 81 Res126 6 6 6 5 5 4 3 7 7
## 82 Res127 6 6 6 5 5 5 3 6 5
## 83 Res128 7 5 6 2 2 7 6 6 6
## 84 Res129 7 7 7 7 7 5 4 6 6
## 85 Res13 6 6 6 6 6 6 1 7 7
## 86 Res130 5 5 5 3 2 5 5 5 5
## 87 Res131 6 6 6 5 5 5 1 7 7
## 88 Res132 7 7 7 7 7 7 5 3 3
## 89 Res133 6 5 5 5 5 4 4 6 5
## 90 Res134 6 5 5 3 3 2 3 5 6
## 91 Res135 6 6 5 6 5 5 4 5 5
## 92 Res136 3 3 3 3 4 4 2 6 7
## 93 Res137 6 6 6 6 6 6 5 3 3
## 94 Res138 7 7 7 6 6 7 1 7 7
## 95 Res139 2 3 4 2 2 5 3 6 6
## 96 Res14 6 6 6 5 5 7 3 6 6
## 97 Res140 6 6 7 4 5 6 6 2 2
## 98 Res141 7 6 7 6 6 6 3 5 6
## 99 Res142 6 6 6 5 5 4 5 3 3
## 100 Res143 2 2 2 2 1 1 1 1 2
## 101 Res144 6 6 6 2 2 2 1 4 3
## 102 Res145 6 6 6 5 5 6 2 7 7
## 103 Res146 7 6 7 5 5 5 5 5 5
## 104 Res147 5 6 5 4 5 6 3 5 6
## 105 Res148 5 5 6 6 5 5 3 5 5
## 106 Res149 5 6 6 6 5 1 3 5 6
## 107 Res15 1 1 1 1 2 2 2 2 3
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## 819 Res791 3 4 4 5 4 3 3 4 1
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## 826 Res798 6 6 7 1 2 5 5 4 3
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## 851 Res82 6 6 7 6 7 6 1 6 6
## 852 Res820 7 6 7 5 6 6 5 4 4
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## 882 Res848 7 7 7 6 5 5 5 4 3
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## 937 Res898 3 4 4 4 6 7 6 7 6
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## 941 Res900 6 5 5 2 4 3 5 7 5
## 942 Res901 5 5 4 3 2 5 5 5 5
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## 959 Res917 2 2 1 1 1 6 3 7 7
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## 977 Res933 4 4 5 1 4 4 4 4 4
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## 979 Res935 5 5 4 5 3 3 5 5 5
## 980 Res936 3 2 3 1 1 4 4 7 4
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## 984 Res94 6 6 5 3 4 5 1 6 6
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## 986 Res941 5 5 6 3 4 5 5 7 6
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## 988 Res943 4 4 4 2 2 4 2 7 6
## 989 Res944 5 5 4 4 5 5 5 6 6
## 990 Res945 6 7 6 6 6 5 6 7 7
## 991 Res946 4 4 4 1 2 4 4 7 7
## 992 Res947 5 5 5 5 5 3 4 5 3
## 993 Res948 5 5 5 5 4 6 6 5 6
## 994 Res949 7 7 7 6 6 6 6 3 3
## 995 Res95 6 7 7 6 5 6 2 6 7
## 996 Res950 6 7 7 5 7 7 7 3 3
## 997 Res951 7 7 7 6 6 6 6 4 4
## 998 Res952 6 4 4 3 3 4 4 4 5
## 999 Res953 7 6 7 5 5 5 5 6 6
## 1000 Res954 7 7 7 3 5 5 5 5 6
## 1001 Res955 4 4 3 4 4 5 7 4 4
## 1002 Res956 7 6 7 6 7 6 6 6 7
## 1003 Res957 7 7 7 6 6 7 7 3 3
## 1004 Res958 6 6 5 1 2 6 6 4 4
## 1005 Res959 5 4 4 2 4 2 2 4 3
## 1006 Res96 7 7 6 5 6 6 6 7 6
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## 1008 Res961 2 1 2 1 2 3 3 6 6
## 1009 Res962 6 2 1 1 1 5 6 4 4
## 1010 Res963 3 3 3 3 3 2 2 4 4
## 1011 Res964 4 4 5 4 4 5 5 6 6
## 1012 Res965 6 6 7 4 4 4 4 4 4
## 1013 Res966 4 4 4 4 4 2 2 6 3
## 1014 Res967 6 6 6 5 5 4 4 3 5
## 1015 Res968 6 6 6 5 5 5 5 6 5
## 1016 Res969 5 5 5 3 3 4 4 4 5
## 1017 Res97 6 5 6 5 5 6 3 5 6
## 1018 Res970 2 2 2 1 2 2 1 4 3
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## 1020 Res972 7 7 7 5 5 4 4 6 5
## 1021 Res973 5 4 5 3 4 4 4 5 5
## 1022 Res974 7 7 7 1 7 6 7 4 4
## 1023 Res975 7 7 7 6 6 4 3 5 6
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## 1025 Res977 6 6 5 3 4 2 3 5 5
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## 1032 Res983 5 5 5 4 5 2 3 5 5
## 1033 Res984 6 6 7 6 6 4 6 6 6
## 1034 Res985 4 3 1 1 1 4 1 4 4
## 1035 Res986 4 5 4 4 4 3 4 4 3
## 1036 Res987 6 6 7 5 5 5 5 3 3
## 1037 Res988 6 6 7 6 5 6 6 6 6
## 1038 Res989 4 4 4 1 2 4 2 3 3
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## 1048 Res998 5 5 5 5 5 4 4 6 5
## 1049 Res999 4 5 6 5 5 4 4 4 4
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 6 6 6
## 3 6 6 7 6 5 5
## 4 6 6 4 6 6 3
## 5 4 6 5 6 6 7
## 6 6 6 5 4 5 5
## 7 6 5 4 4 4 5
## 8 6 7 6 5 5 5
## 9 7 7 4 6 6 7
## 10 6 6 5 6 6 5
## 11 5 6 6 2 2 5
## 12 6 6 6 5 6 6
## 13 5 5 5 4 4 3
## 14 4 3 3 2 2 2
## 15 6 5 6 6 6 6
## 16 6 5 5 6 7 7
## 17 4 4 4 4 4 4
## 18 6 6 6 5 4 4
## 19 5 5 4 6 6 6
## 20 5 5 5 3 3 6
## 21 6 5 5 4 5 6
## 22 5 5 5 4 4 4
## 23 6 7 6 6 6 6
## 24 6 6 6 4 5 5
## 25 6 6 6 7 7 7
## 26 7 7 7 3 3 7
## 27 5 6 6 5 5 6
## 28 6 6 6 5 5 6
## 29 5 5 5 5 5 5
## 30 9 5 4 3 3 6
## 31 3 4 6 6 6 6
## 32 6 6 5 4 6 6
## 33 3 2 6 4 3 6
## 34 7 6 6 4 5 4
## 35 6 5 6 6 6 6
## 36 6 7 6 5 6 5
## 37 6 6 6 5 5 6
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## 39 5 5 5 5 5 4
## 40 6 6 4 6 4 5
## 41 4 5 4 5 5 6
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## 44 5 6 3 7 6 6
## 45 5 6 3 5 6 4
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## 47 4 4 4 2 3 2
## 48 7 7 7 5 6 6
## 49 6 7 4 6 6 3
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## 51 7 7 6 4 4 5
## 52 5 6 6 5 5 5
## 53 6 6 6 6 7 6
## 54 5 5 6 3 3 6
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## 59 6 7 7 5 7 4
## 60 6 5 6 3 6 2
## 61 6 6 5 5 7 1
## 62 5 5 5 5 5 5
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## 64 4 4 5 5 5 1
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## 70 5 6 4 5 5 2
## 71 6 6 6 5 5 2
## 72 6 6 6 7 6 2
## 73 4 5 5 4 3 2
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## 76 2 2 1 5 4 1
## 77 7 7 6 5 6 5
## 78 5 6 5 5 5 4
## 79 5 5 5 4 5 3
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## 82 4 4 4 4 4 4
## 83 4 5 5 6 7 2
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## 85 7 7 7 5 7 3
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## 90 5 6 3 2 5 2
## 91 4 6 6 7 5 6
## 92 6 5 6 4 5 2
## 93 6 6 6 5 6 2
## 94 6 7 5 2 6 2
## 95 6 6 5 7 5 1
## 96 7 7 7 5 7 3
## 97 6 7 6 7 7 1
## 98 2 5 4 6 7 1
## 99 5 7 6 6 6 1
## 100 2 2 2 2 2 6
## 101 7 7 6 7 6 6
## 102 6 6 5 6 6 5
## 103 6 6 5 6 4 3
## 104 6 6 6 6 6 6
## 105 6 6 6 6 6 6
## 106 3 5 6 3 5 6
## 107 5 2 2 2 2 6
## 108 3 3 3 3 3 6
## 109 2 2 5 2 2 3
## 110 6 6 6 2 4 5
## 111 7 7 3 3 2 7
## 112 5 6 5 6 5 5
## 113 5 5 5 5 5 4
## 114 2 2 2 3 2 4
## 115 2 2 2 2 2 5
## 116 6 5 5 6 6 6
## 117 2 2 5 6 6 6
## 118 5 2 2 2 2 6
## 119 3 5 3 6 6 2
## 120 5 5 5 6 5 5
## 121 6 6 6 5 5 5
## 122 6 5 4 6 6 4
## 123 6 6 6 6 6 1
## 124 2 2 2 2 2 6
## 125 6 6 6 6 6 1
## 126 5 6 6 5 6 5
## 127 6 7 5 3 5 4
## 128 7 7 6 7 2 3
## 129 5 2 2 2 2 6
## 130 5 5 5 4 2 2
## 131 5 6 4 5 5 5
## 132 7 7 7 5 2 2
## 133 6 6 6 7 2 2
## 134 4 4 4 3 2 2
## 135 5 4 3 2 2 2
## 136 4 4 4 1 5 5
## 137 7 7 7 7 2 2
## 138 6 6 4 5 4 2
## 139 5 6 4 5 4 2
## 140 5 2 2 2 2 6
## 141 6 6 7 6 6 2
## 142 6 6 5 4 6 2
## 143 6 6 5 6 6 2
## 144 7 7 6 7 6 7
## 145 3 3 4 4 4 4
## 146 6 6 5 3 5 4
## 147 6 6 4 5 6 4
## 148 7 7 7 7 7 7
## 149 2 2 4 4 4 5
## 150 6 6 6 6 6 6
## 151 5 2 2 2 2 6
## 152 4 6 5 5 3 3
## 153 6 6 5 7 6 2
## 154 6 6 6 7 4 2
## 155 6 6 4 6 6 3
## 156 7 7 6 6 5 2
## 157 6 6 6 5 4 4
## 158 7 6 7 7 5 7
## 159 6 6 6 6 6 4
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## 947 1 1 Collision Male 57 1 NaN Middle Eastern
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## 949 1 1 Collision Male 60 1 NaN Middle Eastern
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## 1018 2 3 Collision Male 55 2 NaN Middle Eastern
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## 1020 2 3 Collision Female 55 1 NaN Middle Eastern
## 1021 2 3 Collision Male 56 1 NaN Middle Eastern
## 1022 2 3 Collision Male 57 2 NaN Middle Eastern
## 1023 2 3 Collision Male 57 1 NaN Middle Eastern
## 1024 2 3 Collision Female 57 2 NaN Middle Eastern
## 1025 2 3 Collision Male 57 1 NaN Middle Eastern
## 1026 2 3 Collision Female 60 3 NaN Middle Eastern
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## 1029 2 3 Collision Female 18 2 NaN Middle Eastern
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## 1031 2 3 Collision Female 18 1 NaN Middle Eastern
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## 1035 2 3 Collision Female 19 1 NaN Middle Eastern
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## Model MPG Cyl acc1 C_cost. H_Cost Post.Satis Att_Mean
## 1 Ford Expedition 15 8 6 16 14 4 6
## 2 Ford Expedition 15 8 6 16 14 5 6
## 3 Toyota Rav4 24 4 8 10 8 4 6
## 4 Toyota Corolla 26 4 8 7 6 6 6
## 5 Toyota Corolla 26 4 8 7 6 5 6
## 6 Toyota Corolla 26 4 8 7 6 6 2
## 7 Toyota Corolla 26 4 8 7 6 5 2
## 8 Toyota Corolla 26 4 8 7 6 6 7
## 9 Toyota Corolla 26 4 8 7 6 7 2
## 10 Toyota Corolla 26 4 8 7 6 6 6
## 11 Toyota Corolla 26 4 8 7 6 6 4
## 12 Honda Pilot 20 6 6 12 10 5 5
## 13 Honda Pilot 20 6 6 12 10 4 4
## 14 Toyota Rav4 24 4 8 10 8 5 6
## 15 Honda Pilot 20 6 6 12 10 4 7
## 16 Honda Pilot 20 6 6 12 10 6 7
## 17 Honda Pilot 20 6 6 12 10 4 6
## 18 Honda Pilot 20 6 6 12 10 5 4
## 19 Honda Pilot 20 6 6 12 10 5 5
## 20 Honda Pilot 20 6 6 12 10 5 4
## 21 Honda Pilot 20 6 6 12 10 4 6
## 22 Honda Pilot 20 6 6 12 10 6 6
## 23 Honda Pilot 20 6 6 12 10 6 4
## 24 Toyota Corolla 26 4 8 7 6 5 6
## 25 Toyota Rav4 24 4 8 10 8 7 7
## 26 Toyota Corolla 26 4 8 7 6 7 6
## 27 Toyota Corolla 26 4 8 7 6 6 6
## 28 Toyota Corolla 26 4 8 7 6 5 6
## 29 Toyota Corolla 26 4 8 7 6 6 7
## 30 Toyota Corolla 26 4 8 7 6 6 6
## 31 Toyota Corolla 26 4 8 7 6 5 7
## 32 Toyota Corolla 26 4 8 7 6 6 6
## 33 Toyota Corolla 26 4 8 7 6 5 7
## 34 Toyota Corolla 26 4 8 7 6 6 6
## 35 Toyota Corolla 26 4 8 7 6 6 7
## 36 Toyota Rav4 24 4 8 10 8 6 7
## 37 Toyota Corolla 26 4 8 7 6 7 7
## 38 Toyota Corolla 26 4 8 7 6 6 6
## 39 Toyota Corolla 26 4 8 7 6 6 6
## 40 Toyota Corolla 26 4 8 7 6 6 6
## 41 Toyota Corolla 26 4 8 7 6 6 7
## 42 Toyota Corolla 26 4 8 7 6 6 7
## 43 Toyota Corolla 26 4 8 7 6 6 6
## 44 Toyota Corolla 26 4 8 7 6 6 6
## 45 Toyota Corolla 26 4 8 7 6 6 6
## 46 Toyota Corolla 26 4 8 7 6 7 6
## 47 Toyota Rav4 24 4 8 10 8 6 7
## 48 Toyota Corolla 26 4 8 7 6 7 6
## 49 Fiat 500x 22 4 8 8 6 7 4
## 50 Fiat 500x 22 4 8 8 6 6 5
## 51 Fiat 500x 22 4 8 8 6 6 4
## 52 Fiat 500x 22 4 8 8 6 2 4
## 53 Fiat 500x 22 4 8 8 6 6 3
## 54 Fiat 500x 22 4 8 8 6 4 4
## 55 Fiat 500x 22 4 8 8 6 7 4
## 56 Fiat 500x 22 4 8 8 6 2 4
## 57 Fiat 500x 22 4 8 8 6 5 4
## 58 Toyota Rav4 24 4 8 10 8 4 6
## 59 Toyota Rav4 24 4 8 10 8 4 6
## 60 Toyota Rav4 24 4 8 10 8 6 7
## 61 Toyota Rav4 24 4 8 10 8 5 6
## 62 Toyota Rav4 24 4 8 10 8 3 6
## 63 Ford Expedition 15 8 6 16 14 5 4
## 64 Toyota Rav4 24 4 8 10 8 5 7
## 65 Toyota Rav4 24 4 8 10 8 5 6
## 66 Toyota Rav4 24 4 8 10 8 5 6
## 67 Toyota Rav4 24 4 8 10 8 6 4
## 68 Toyota Rav4 24 4 8 10 8 4 6
## 69 Toyota Rav4 24 4 8 10 8 6 5
## 70 Toyota Rav4 24 4 8 10 8 5 6
## 71 Toyota Rav4 24 4 8 10 8 6 6
## 72 Toyota Rav4 24 4 8 10 8 7 6
## 73 Toyota Rav4 24 4 8 10 8 5 6
## 74 Ford Expedition 15 8 6 16 14 5 2
## 75 Toyota Rav4 24 4 8 10 8 7 6
## 76 Honda CRV 26 4 8 8 7 5 7
## 77 Honda CRV 26 4 8 8 7 7 6
## 78 Honda CRV 26 4 8 8 7 6 7
## 79 Honda CRV 26 4 8 8 7 6 7
## 80 Honda CRV 26 4 8 8 7 5 6
## 81 Honda CRV 26 4 8 8 7 6 6
## 82 Honda CRV 26 4 8 8 7 5 6
## 83 Honda CRV 26 4 8 8 7 5 6
## 84 Honda CRV 26 4 8 8 7 5 7
## 85 Ford Expedition 15 8 6 16 14 4 6
## 86 Honda CRV 26 4 8 8 7 6 5
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## 133 Toyota Highlander 20 6 7 10 8 7 6
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## 188 Toyota Rav4 24 4 8 10 8 5 4
## 189 Toyota Rav4 24 4 8 10 8 5 4
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## 200 Toyota Rav4 24 4 8 10 8 5 5
## 201 Toyota Rav4 24 4 8 10 8 7 4
## 202 Toyota Rav4 24 4 8 10 8 6 4
## 203 Toyota Rav4 24 4 8 10 8 6 4
## 204 Toyota Rav4 24 4 8 10 8 6 4
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## 212 Kia Sorento 22 4 8 9 7 6 4
## 213 Kia Sorento 22 4 8 9 7 5 5
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## 216 Kia Sorento 22 4 8 9 7 5 4
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## 220 Kia Sorento 22 4 8 9 7 6 4
## 221 Kia Sorento 22 4 8 9 7 5 4
## 222 Kia Sorento 22 4 8 9 7 7 4
## 223 Kia Sorento 22 4 8 9 7 6 5
## 224 Kia Sorento 22 4 8 9 7 7 5
## 225 Kia Sorento 22 4 8 9 7 6 4
## 226 Kia Sorento 22 4 8 9 7 5 6
## 227 Kia Sorento 22 4 8 9 7 6 4
## 228 Kia Sorento 22 4 8 9 7 5 4
## 229 Dodge Journey 16 6 6 12 11 5 2
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## 232 Ford Explorer 19 6 6 13 11 6 4
## 233 Ford Explorer 19 6 6 13 11 4 4
## 234 Ford Explorer 19 6 6 13 11 6 4
## 235 Ford Explorer 19 6 6 13 11 6 6
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## 237 Ford Explorer 19 6 6 13 11 3 4
## 238 Ford Explorer 19 6 6 13 11 3 4
## 239 Ford Explorer 19 6 6 13 11 3 5
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## 241 Ford Explorer 19 6 6 13 11 7 4
## 242 Ford Explorer 19 6 6 13 11 3 4
## 243 Ford Explorer 19 6 6 13 11 6 4
## 244 Ford Explorer 19 6 6 13 11 6 6
## 245 Ford Explorer 19 6 6 13 11 7 4
## 246 Ford Explorer 19 6 6 13 11 7 5
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## 271 Ford Explorer 19 6 6 13 11 5 4
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## 276 Ford Explorer 19 6 6 13 11 3 4
## 277 Ford Explorer 19 6 6 13 11 5 4
## 278 Ford Explorer 19 6 6 13 11 7 4
## 279 Ford Explorer 19 6 6 13 11 6 6
## 280 Ford Explorer 19 6 6 13 11 5 2
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## 282 Ford Explorer 19 6 6 13 11 6 5
## 283 Ford Explorer 19 6 6 13 11 6 4
## 284 Ford Explorer 19 6 6 13 11 7 4
## 285 Dodge Journey 16 6 6 12 11 5 7
## 286 Ford Explorer 19 6 6 13 11 7 4
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## 304 Ford Explorer 19 6 6 13 11 7 4
## 305 Ford Explorer 19 6 6 13 11 5 4
## 306 Ford Explorer 19 6 6 13 11 7 4
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## 309 Ford Explorer 19 6 6 13 11 7 6
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## 313 Kia Sorento 22 4 8 9 7 5 4
## 314 Kia Sorento 22 4 8 9 7 6 4
## 315 Kia Sorento 22 4 8 9 7 7 6
## 316 Kia Sorento 22 4 8 9 7 6 6
## 317 Kia Sorento 22 4 8 9 7 6 3
## 318 Dodge Journey 16 6 6 12 11 5 6
## 319 Kia Sorento 22 4 8 9 7 5 2
## 320 Kia Sorento 22 4 8 9 7 5 4
## 321 Kia Sorento 22 4 8 9 7 6 4
## 322 Kia Sorento 22 4 8 9 7 6 4
## 323 Kia Sorento 22 4 8 9 7 5 7
## 324 Kia Sorento 22 4 8 9 7 6 4
## 325 Kia Sorento 22 4 8 9 7 7 4
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## 334 Honda CRV 26 4 8 8 7 5 6
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## 339 Honda CRV 26 4 8 8 7 6 4
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## 362 Dodge Journey 16 6 6 12 11 3 6
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## 368 Toyota Highlander 20 6 7 10 8 6 4
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## 385 Dodge Journey 16 6 6 12 11 3 1
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## 390 Ford Explorer 19 6 6 13 11 6 4
## 391 Ford Explorer 19 6 6 13 11 6 4
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## 396 Dodge Journey 16 6 6 12 11 4 1
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## 398 Ford Explorer 19 6 6 13 11 6 4
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## 400 Ford Explorer 19 6 6 13 11 5 4
## 401 Ford Explorer 19 6 6 13 11 6 4
## 402 Ford Explorer 19 6 6 13 11 5 4
## 403 Ford Explorer 19 6 6 13 11 6 4
## 404 Ford Explorer 19 6 6 13 11 7 6
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## 410 Ford Explorer 19 6 6 13 11 5 6
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## 556 Dodge Journey 16 6 6 12 11 5 7
## 557 Dodge Journey 16 6 6 12 11 3 6
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## 559 Dodge Journey 16 6 6 12 11 5 7
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## 611 Toyota Highlander 20 6 7 10 8 4 6
## 612 Toyota Highlander 20 6 7 10 8 4 6
## 613 Toyota Highlander 20 6 7 10 8 4 6
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## 619 Honda Pilot 20 6 6 12 10 6 5
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## 630 Honda Pilot 20 6 6 12 10 5 4
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## 670 Fiat 500x 22 4 8 8 6 5 4
## 671 Fiat 500x 22 4 8 8 6 5 7
## 672 Fiat 500x 22 4 8 8 6 5 3
## 673 Chrysler Jeep 18 6 4 12 10 6 6
## 674 Chevrolet Camaro 14 8 4 14 12 4 6
## 675 Chevrolet Camaro 14 8 4 14 12 6 7
## 676 Chevrolet Camaro 14 8 4 14 12 5 4
## 677 Chevrolet Camaro 14 8 4 14 12 3 4
## 678 Chevrolet Camaro 14 8 4 14 12 6 6
## 679 Chevrolet Camaro 14 8 4 14 12 3 5
## 680 Chevrolet Camaro 14 8 4 14 12 5 6
## 681 Chevrolet Camaro 14 8 4 14 12 6 6
## 682 Chevrolet Camaro 14 8 4 14 12 3 6
## 683 Chevrolet Camaro 14 8 4 14 12 5 6
## 684 Chrysler Jeep 18 6 4 12 10 6 7
## 685 Chevrolet Camaro 14 8 4 14 12 6 6
## 686 Chevrolet Camaro 14 8 4 14 12 5 6
## 687 Chevrolet Camaro 14 8 4 14 12 5 6
## 688 Chevrolet Camaro 14 8 4 14 12 4 2
## 689 Chevrolet Camaro 14 8 4 14 12 6 5
## 690 Chevrolet Camaro 14 8 4 14 12 5 6
## 691 Chevrolet Camaro 14 8 4 14 12 7 6
## 692 Chevrolet Camaro 14 8 4 14 12 7 5
## 693 Chevrolet Camaro 14 8 4 14 12 6 7
## 694 Chevrolet Camaro 14 8 4 14 12 6 6
## 695 Chrysler Jeep 18 6 4 12 10 5 6
## 696 Chevrolet Camaro 14 8 4 14 12 4 2
## 697 Chevrolet Camaro 14 8 4 14 12 2 6
## 698 Chevrolet Camaro 14 8 4 14 12 5 5
## 699 Chevrolet Camaro 14 8 4 14 12 2 4
## 700 Chevrolet Camaro 14 8 4 14 12 2 5
## 701 Chevrolet Camaro 14 8 4 14 12 5 2
## 702 Chevrolet Camaro 14 8 4 14 12 2 6
## 703 Chevrolet Camaro 14 8 4 14 12 5 4
## 704 Chevrolet Camaro 14 8 4 14 12 5 4
## 705 Chevrolet Camaro 14 8 4 14 12 2 2
## 706 Chrysler Jeep 18 6 4 12 10 3 5
## 707 Chevrolet Camaro 14 8 4 14 12 6 2
## 708 Chevrolet Camaro 14 8 4 14 12 6 2
## 709 Chevrolet Camaro 14 8 4 14 12 6 3
## 710 Chevrolet Camaro 14 8 4 14 12 6 5
## 711 Chevrolet Camaro 14 8 4 14 12 3 6
## 712 Chevrolet Camaro 14 8 4 14 12 2 6
## 713 Chevrolet Camaro 14 8 4 14 12 5 2
## 714 Chevrolet Camaro 14 8 4 14 12 7 6
## 715 Chevrolet Camaro 14 8 4 14 12 6 6
## 716 Chevrolet Camaro 14 8 4 14 12 7 5
## 717 Ford Expedition 15 8 6 16 14 3 1
## 718 Chrysler Jeep 18 6 4 12 10 6 6
## 719 Chevrolet Camaro 14 8 4 14 12 2 6
## 720 Chevrolet Camaro 14 8 4 14 12 4 6
## 721 Chevrolet Camaro 14 8 4 14 12 2 6
## 722 Chevrolet Camaro 14 8 4 14 12 7 6
## 723 Chevrolet Camaro 14 8 4 14 12 2 3
## 724 Chevrolet Camaro 14 8 4 14 12 2 6
## 725 Chevrolet Camaro 14 8 4 14 12 7 4
## 726 Chevrolet Camaro 14 8 4 14 12 5 6
## 727 Chevrolet Camaro 14 8 4 14 12 2 5
## 728 Chevrolet Camaro 14 8 4 14 12 5 4
## 729 Chrysler Jeep 18 6 4 12 10 3 6
## 730 Chevrolet Camaro 14 8 4 14 12 2 5
## 731 Chevrolet Camaro 14 8 4 14 12 6 4
## 732 Chevrolet Camaro 14 8 4 14 12 6 6
## 733 Chevrolet Camaro 14 8 4 14 12 5 7
## 734 Chevrolet Camaro 14 8 4 14 12 6 7
## 735 Chevrolet Camaro 14 8 4 14 12 6 7
## 736 Chevrolet Camaro 14 8 4 14 12 7 7
## 737 Chevrolet Camaro 14 8 4 14 12 5 7
## 738 Chevrolet Camaro 14 8 4 14 12 6 6
## 739 Chevrolet Camaro 14 8 4 14 12 6 6
## 740 Chrysler Jeep 18 6 4 12 10 3 6
## 741 Chevrolet Camaro 14 8 4 14 12 6 6
## 742 Chevrolet Camaro 14 8 4 14 12 5 7
## 743 Chevrolet Camaro 14 8 4 14 12 6 6
## 744 Chevrolet Camaro 14 8 4 14 12 7 6
## 745 Toyota Highlander 20 6 7 10 8 4 6
## 746 Toyota Highlander 20 6 7 10 8 5 7
## 747 Toyota Highlander 20 6 7 10 8 5 7
## 748 Toyota Highlander 20 6 7 10 8 6 7
## 749 Toyota Highlander 20 6 7 10 8 6 7
## 750 Toyota Highlander 20 6 7 10 8 5 5
## 751 Chrysler Jeep 18 6 4 12 10 5 6
## 752 Toyota Highlander 20 6 7 10 8 5 6
## 753 Toyota Highlander 20 6 7 10 8 6 7
## 754 Toyota Highlander 20 6 7 10 8 6 4
## 755 Toyota Highlander 20 6 7 10 8 6 7
## 756 Toyota Highlander 20 6 7 10 8 6 6
## 757 Toyota Highlander 20 6 7 10 8 5 6
## 758 Toyota Highlander 20 6 7 10 8 4 6
## 759 Toyota Highlander 20 6 7 10 8 5 7
## 760 Toyota Highlander 20 6 7 10 8 4 7
## 761 Toyota Highlander 20 6 7 10 8 3 6
## 762 Honda CRV 26 4 8 8 7 6 4
## 763 Toyota Highlander 20 6 7 10 8 4 6
## 764 Toyota Highlander 20 6 7 10 8 4 6
## 765 Toyota Highlander 20 6 7 10 8 4 5
## 766 Toyota Highlander 20 6 7 10 8 4 7
## 767 Toyota Highlander 20 6 7 10 8 6 6
## 768 Toyota Highlander 20 6 7 10 8 5 5
## 769 Toyota Highlander 20 6 7 10 8 5 6
## 770 Toyota Highlander 20 6 7 10 8 5 5
## 771 Toyota Highlander 20 6 7 10 8 6 7
## 772 Toyota Highlander 20 6 7 10 8 4 7
## 773 Honda CRV 26 4 8 8 7 7 2
## 774 Toyota Highlander 20 6 7 10 8 6 7
## 775 Toyota Highlander 20 6 7 10 8 4 4
## 776 Toyota Highlander 20 6 7 10 8 5 6
## 777 Toyota Highlander 20 6 7 10 8 5 7
## 778 Toyota Highlander 20 6 7 10 8 5 6
## 779 Toyota Highlander 20 6 7 10 8 3 3
## 780 Toyota Highlander 20 6 7 10 8 6 6
## 781 Toyota Highlander 20 6 7 10 8 5 6
## 782 Toyota Highlander 20 6 7 10 8 6 2
## 783 Toyota Highlander 20 6 7 10 8 5 6
## 784 Honda CRV 26 4 8 8 7 6 4
## 785 Toyota Highlander 20 6 7 10 8 5 7
## 786 Toyota Highlander 20 6 7 10 8 6 6
## 787 Toyota Highlander 20 6 7 10 8 5 6
## 788 Honda Pilot 20 6 6 12 10 5 7
## 789 Honda Pilot 20 6 6 12 10 6 5
## 790 Honda Pilot 20 6 6 12 10 6 6
## 791 Honda Pilot 20 6 6 12 10 5 4
## 792 Honda Pilot 20 6 6 12 10 6 6
## 793 Honda Pilot 20 6 6 12 10 4 4
## 794 Honda Pilot 20 6 6 12 10 5 6
## 795 Honda CRV 26 4 8 8 7 6 6
## 796 Honda Pilot 20 6 6 12 10 6 6
## 797 Honda Pilot 20 6 6 12 10 6 6
## 798 Honda Pilot 20 6 6 12 10 7 5
## 799 Honda Pilot 20 6 6 12 10 5 7
## 800 Honda Pilot 20 6 6 12 10 5 7
## 801 Honda Pilot 20 6 6 12 10 6 5
## 802 Honda Pilot 20 6 6 12 10 6 4
## 803 Honda Pilot 20 6 6 12 10 6 6
## 804 Honda Pilot 20 6 6 12 10 5 6
## 805 Honda Pilot 20 6 6 12 10 6 4
## 806 Honda CRV 26 4 8 8 7 6 6
## 807 Honda Pilot 20 6 6 12 10 5 6
## 808 Buick Encore 17 4 7 8 7 5 6
## 809 Buick Encore 17 4 7 8 7 4 6
## 810 Buick Encore 17 4 7 8 7 4 6
## 811 Buick Encore 17 4 7 8 7 6 6
## 812 Buick Encore 17 4 7 8 7 7 1
## 813 Buick Encore 17 4 7 8 7 7 6
## 814 Buick Encore 17 4 7 8 7 4 6
## 815 Buick Encore 17 4 7 8 7 5 6
## 816 Buick Encore 17 4 7 8 7 3 5
## 817 Honda CRV 26 4 8 8 7 6 5
## 818 Buick Encore 17 4 7 8 7 6 4
## 819 Buick Encore 17 4 7 8 7 2 4
## 820 Buick Encore 17 4 7 8 7 2 7
## 821 Buick Encore 17 4 7 8 7 6 5
## 822 Buick Encore 17 4 7 8 7 6 7
## 823 Buick Encore 17 4 7 8 7 7 6
## 824 Buick Encore 17 4 7 8 7 6 6
## 825 Buick Encore 17 4 7 8 7 3 6
## 826 Buick Encore 17 4 7 8 7 5 6
## 827 Buick Encore 17 4 7 8 7 6 6
## 828 Ford Expedition 15 8 6 16 14 6 6
## 829 Honda CRV 26 4 8 8 7 7 6
## 830 Buick Encore 17 4 7 8 7 7 6
## 831 Buick Encore 17 4 7 8 7 6 6
## 832 Buick Encore 17 4 7 8 7 6 6
## 833 Buick Encore 17 4 7 8 7 5 2
## 834 Buick Encore 17 4 7 8 7 5 3
## 835 Buick Encore 17 4 7 8 7 6 6
## 836 Buick Encore 17 4 7 8 7 6 4
## 837 Buick Encore 17 4 7 8 7 5 6
## 838 Buick Encore 17 4 7 8 7 5 6
## 839 Buick Encore 17 4 7 8 7 6 5
## 840 Honda CRV 26 4 8 8 7 4 6
## 841 Buick Encore 17 4 7 8 7 7 6
## 842 Buick Encore 17 4 7 8 7 5 6
## 843 Lincoln Navigator 15 8 5 16 13 6 5
## 844 Lincoln Navigator 15 8 5 16 13 5 2
## 845 Lincoln Navigator 15 8 5 16 13 7 2
## 846 Lincoln Navigator 15 8 5 16 13 5 7
## 847 Lincoln Navigator 15 8 5 16 13 6 6
## 848 Lincoln Navigator 15 8 5 16 13 4 6
## 849 Lincoln Navigator 15 8 5 16 13 4 5
## 850 Lincoln Navigator 15 8 5 16 13 5 6
## 851 Honda CRV 26 4 8 8 7 6 6
## 852 Lincoln Navigator 15 8 5 16 13 5 6
## 853 Lincoln Navigator 15 8 5 16 13 6 7
## 854 Lincoln Navigator 15 8 5 16 13 7 6
## 855 Lincoln Navigator 15 8 5 16 13 7 6
## 856 Lincoln Navigator 15 8 5 16 13 6 6
## 857 Lincoln Navigator 15 8 5 16 13 7 6
## 858 Lincoln Navigator 15 8 5 16 13 5 6
## 859 Lincoln Navigator 15 8 5 16 13 6 6
## 860 Lincoln Navigator 15 8 5 16 13 7 6
## 861 Lincoln Navigator 15 8 5 16 13 7 5
## 862 Honda CRV 26 4 8 8 7 6 6
## 863 Lincoln Navigator 15 8 5 16 13 6 6
## 864 Lincoln Navigator 15 8 5 16 13 7 6
## 865 Lincoln Navigator 15 8 5 16 13 5 7
## 866 Lincoln Navigator 15 8 5 16 13 3 4
## 867 Lincoln Navigator 15 8 5 16 13 6 7
## 868 Lincoln Navigator 15 8 5 16 13 7 6
## 869 Ford Expedition 15 8 6 16 14 7 7
## 870 Ford Expedition 15 8 6 16 14 4 6
## 871 Ford Expedition 15 8 6 16 14 5 4
## 872 Ford Expedition 15 8 6 16 14 6 7
## 873 Honda CRV 26 4 8 8 7 4 6
## 874 Ford Expedition 15 8 6 16 14 7 7
## 875 Ford Expedition 15 8 6 16 14 2 7
## 876 Ford Expedition 15 8 6 16 14 4 7
## 877 Ford Expedition 15 8 6 16 14 3 7
## 878 Ford Expedition 15 8 6 16 14 3 7
## 879 Ford Expedition 15 8 6 16 14 7 6
## 880 Ford Expedition 15 8 6 16 14 3 4
## 881 Ford Expedition 15 8 6 16 14 3 5
## 882 Ford Expedition 15 8 6 16 14 2 7
## 883 Ford Expedition 15 8 6 16 14 5 6
## 884 Honda CRV 26 4 8 8 7 6 6
## 885 Ford Expedition 15 8 6 16 14 2 7
## 886 Ford Expedition 15 8 6 16 14 6 7
## 887 Ford Expedition 15 8 6 16 14 2 7
## 888 Ford Expedition 15 8 6 16 14 6 6
## 889 Toyota Corolla 26 4 8 7 6 7 6
## 890 Toyota Corolla 26 4 8 7 6 5 6
## 891 Toyota Corolla 26 4 8 7 6 6 7
## 892 Toyota Corolla 26 4 8 7 6 7 4
## 893 Toyota Corolla 26 4 8 7 6 7 7
## 894 Toyota Corolla 26 4 8 7 6 6 4
## 895 Honda CRV 26 4 8 8 7 3 7
## 896 Toyota Corolla 26 4 8 7 6 5 7
## 897 Toyota Corolla 26 4 8 7 6 5 7
## 898 Toyota Corolla 26 4 8 7 6 6 6
## 899 Toyota Corolla 26 4 8 7 6 6 7
## 900 Toyota Corolla 26 4 8 7 6 5 6
## 901 Toyota Corolla 26 4 8 7 6 6 7
## 902 Toyota Corolla 26 4 8 7 6 7 4
## 903 Toyota Corolla 26 4 8 7 6 6 6
## 904 Toyota Corolla 26 4 8 7 6 6 6
## 905 Toyota Corolla 26 4 8 7 6 5 5
## 906 Honda CRV 26 4 8 8 7 6 4
## 907 Toyota Corolla 26 4 8 7 6 7 6
## 908 Toyota Corolla 26 4 8 7 6 7 6
## 909 Toyota Corolla 26 4 8 7 6 7 4
## 910 Toyota Corolla 26 4 8 7 6 7 6
## 911 Toyota Corolla 26 4 8 7 6 7 7
## 912 Toyota Corolla 26 4 8 7 6 5 6
## 913 Toyota Corolla 26 4 8 7 6 6 5
## 914 Toyota Corolla 26 4 8 7 6 6 6
## 915 Toyota Corolla 26 4 8 7 6 6 4
## 916 Toyota Corolla 26 4 8 7 6 6 6
## 917 Honda CRV 26 4 8 8 7 4 7
## 918 Toyota Corolla 26 4 8 7 6 6 7
## 919 Toyota Corolla 26 4 8 7 6 7 6
## 920 Toyota Corolla 26 4 8 7 6 6 6
## 921 Toyota Corolla 26 4 8 7 6 6 6
## 922 Toyota Corolla 26 4 8 7 6 5 4
## 923 Toyota Corolla 26 4 8 7 6 6 4
## 924 Toyota Corolla 26 4 8 7 6 7 6
## 925 Toyota Corolla 26 4 8 7 6 7 5
## 926 Toyota Corolla 26 4 8 7 6 7 4
## 927 Toyota Corolla 26 4 8 7 6 7 6
## 928 Honda CRV 26 4 8 8 7 4 7
## 929 Toyota Corolla 26 4 8 7 6 6 6
## 930 Toyota Corolla 26 4 8 7 6 7 7
## 931 Toyota Corolla 26 4 8 7 6 6 6
## 932 Toyota Corolla 26 4 8 7 6 5 6
## 933 Toyota Corolla 26 4 8 7 6 7 6
## 934 Honda Pilot 20 6 6 12 10 7 6
## 935 Honda Pilot 20 6 6 12 10 5 4
## 936 Honda Pilot 20 6 6 12 10 6 7
## 937 Honda Pilot 20 6 6 12 10 5 4
## 938 Honda Pilot 20 6 6 12 10 5 6
## 939 Ford Expedition 15 8 6 16 14 3 2
## 940 Honda CRV 26 4 8 8 7 5 6
## 941 Honda Pilot 20 6 6 12 10 5 6
## 942 Honda Pilot 20 6 6 12 10 5 5
## 943 Honda Pilot 20 6 6 12 10 6 4
## 944 Honda Pilot 20 6 6 12 10 6 7
## 945 Honda Pilot 20 6 6 12 10 5 6
## 946 Honda Pilot 20 6 6 12 10 6 7
## 947 Honda Pilot 20 6 6 12 10 5 5
## 948 Honda Pilot 20 6 6 12 10 4 6
## 949 Honda Pilot 20 6 6 12 10 5 6
## 950 Honda Pilot 20 6 6 12 10 6 6
## 951 Honda CRV 26 4 8 8 7 5 7
## 952 Honda Pilot 20 6 6 12 10 5 7
## 953 Ford Expedition 15 8 6 16 14 4 4
## 954 Ford Expedition 15 8 6 16 14 5 6
## 955 Ford Expedition 15 8 6 16 14 6 6
## 956 Ford Expedition 15 8 6 16 14 5 7
## 957 Ford Expedition 15 8 6 16 14 2 4
## 958 Ford Expedition 15 8 6 16 14 5 6
## 959 Ford Expedition 15 8 6 16 14 2 2
## 960 Ford Expedition 15 8 6 16 14 6 7
## 961 Ford Expedition 15 8 6 16 14 2 4
## 962 Honda CRV 26 4 8 8 7 4 6
## 963 Ford Expedition 15 8 6 16 14 6 4
## 964 Ford Expedition 15 8 6 16 14 2 6
## 965 Ford Expedition 15 8 6 16 14 2 6
## 966 Ford Expedition 15 8 6 16 14 5 6
## 967 Ford Expedition 15 8 6 16 14 6 6
## 968 Ford Expedition 15 8 6 16 14 2 7
## 969 Ford Expedition 15 8 6 16 14 6 5
## 970 Ford Expedition 15 8 6 16 14 2 6
## 971 Ford Expedition 15 8 6 16 14 2 4
## 972 Ford Expedition 15 8 6 16 14 5 5
## 973 Honda CRV 26 4 8 8 7 5 6
## 974 Ford Expedition 15 8 6 16 14 2 4
## 975 Ford Expedition 15 8 6 16 14 6 4
## 976 Ford Expedition 15 8 6 16 14 2 2
## 977 Ford Expedition 15 8 6 16 14 2 4
## 978 Ford Expedition 15 8 6 16 14 4 7
## 979 Ford Expedition 15 8 6 16 14 4 5
## 980 Ford Expedition 15 8 6 16 14 3 2
## 981 Ford Expedition 15 8 6 16 14 6 7
## 982 Ford Expedition 15 8 6 16 14 5 2
## 983 Ford Expedition 15 8 6 16 14 5 7
## 984 Honda CRV 26 4 8 8 7 7 6
## 985 Ford Expedition 15 8 6 16 14 3 3
## 986 Ford Expedition 15 8 6 16 14 6 5
## 987 Ford Expedition 15 8 6 16 14 5 7
## 988 Ford Expedition 15 8 6 16 14 2 4
## 989 Ford Expedition 15 8 6 16 14 6 5
## 990 Ford Expedition 15 8 6 16 14 4 6
## 991 Ford Expedition 15 8 6 16 14 2 4
## 992 Ford Expedition 15 8 6 16 14 6 5
## 993 Ford Expedition 15 8 6 16 14 6 5
## 994 Ford Expedition 15 8 6 16 14 6 7
## 995 Honda CRV 26 4 8 8 7 5 6
## 996 Ford Expedition 15 8 6 16 14 5 6
## 997 Toyota Corolla 26 4 8 7 6 5 7
## 998 Toyota Corolla 26 4 8 7 6 6 5
## 999 Toyota Corolla 26 4 8 7 6 6 6
## 1000 Toyota Corolla 26 4 8 7 6 6 7
## 1001 Toyota Corolla 26 4 8 7 6 5 4
## 1002 Toyota Corolla 26 4 8 7 6 5 6
## 1003 Toyota Corolla 26 4 8 7 6 6 7
## 1004 Toyota Corolla 26 4 8 7 6 6 6
## 1005 Toyota Corolla 26 4 8 7 6 6 4
## 1006 Toyota Rav4 24 4 8 10 8 6 7
## 1007 Toyota Corolla 26 4 8 7 6 6 6
## 1008 Toyota Corolla 26 4 8 7 6 5 2
## 1009 Toyota Corolla 26 4 8 7 6 6 4
## 1010 Ford Expedition 15 8 6 16 14 2 3
## 1011 Ford Expedition 15 8 6 16 14 6 4
## 1012 Ford Expedition 15 8 6 16 14 2 6
## 1013 Ford Expedition 15 8 6 16 14 6 4
## 1014 Ford Expedition 15 8 6 16 14 2 6
## 1015 Ford Expedition 15 8 6 16 14 5 6
## 1016 Lincoln Navigator 15 8 5 16 13 7 5
## 1017 Toyota Rav4 24 4 8 10 8 6 6
## 1018 Lincoln Navigator 15 8 5 16 13 5 2
## 1019 Lincoln Navigator 15 8 5 16 13 6 6
## 1020 Lincoln Navigator 15 8 5 16 13 6 7
## 1021 Lincoln Navigator 15 8 5 16 13 6 4
## 1022 Lincoln Navigator 15 8 5 16 13 6 7
## 1023 Lincoln Navigator 15 8 5 16 13 7 7
## 1024 Lincoln Navigator 15 8 5 16 13 2 4
## 1025 Lincoln Navigator 15 8 5 16 13 6 6
## 1026 Lincoln Navigator 15 8 5 16 13 7 5
## 1027 Lincoln Navigator 15 8 5 16 13 3 4
## 1028 Toyota Rav4 24 4 8 10 8 6 6
## 1029 Lincoln Navigator 15 8 5 16 13 6 7
## 1030 Lincoln Navigator 15 8 5 16 13 6 6
## 1031 Lincoln Navigator 15 8 5 16 13 6 2
## 1032 Lincoln Navigator 15 8 5 16 13 6 5
## 1033 Honda Pilot 20 6 6 12 10 7 6
## 1034 Honda Pilot 20 6 6 12 10 6 4
## 1035 Honda Pilot 20 6 6 12 10 6 4
## 1036 Honda Pilot 20 6 6 12 10 5 6
## 1037 Honda Pilot 20 6 6 12 10 6 6
## 1038 Honda Pilot 20 6 6 12 10 6 4
## 1039 Toyota Rav4 24 4 8 10 8 4 7
## 1040 Honda Pilot 20 6 6 12 10 5 6
## 1041 Toyota Corolla 26 4 8 7 6 6 2
## 1042 Toyota Corolla 26 4 8 7 6 5 2
## 1043 Toyota Corolla 26 4 8 7 6 5 3
## 1044 Toyota Corolla 26 4 8 7 6 6 4
## 1045 Toyota Corolla 26 4 8 7 6 6 6
## 1046 Toyota Corolla 26 4 8 7 6 6 6
## 1047 Toyota Corolla 26 4 8 7 6 5 2
## 1048 Toyota Corolla 26 4 8 7 6 6 5
## 1049 Toyota Corolla 26 4 8 7 6 4 4View(Car_Total)
library(ggplot2)
ggplot(Car_Total, aes(x=Region, fill = Region))+
theme_bw()+
geom_bar()+
geom_text(stat="count", aes(label = ..count..), vjust=0)+
labs(y="Number of Cars",
x = "Region",
title = "Number of Cars by Region")## 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.
Data Manipulation
Once finding the car region percentages, there were variables made for model and regions. This then went into the creation of the graph to determine the number of cars by model and region. This enabled viewers to see how the models of a vehicle related to the regions they were sold in, and if the region contributed to the sales of a paticular vehicle model.
car_region_percentage <-prop.table(table(Car_Total$Region))
print(car_region_percentage)##
## American Asian European Middle Eastern
## 0.3431840 0.1982841 0.2001907 0.2583413View(car_region_percentage)
Car_Total$Model<-as.factor(Car_Total$Model)
Car_Total$Region<-as.factor(Car_Total$Region)
ggplot(Car_Total, aes(x=Region,fill=Model))+
theme_bw()+
geom_bar()+
labs(y="Number of Cars",
title = "Number of Cars by Model and Region")
Gained a deeper insight when seperating the model column into two more specific and focused columns of brand and model. Once this was completed, I was able to see a data visulations of the numbers of cars by brands across regions.
library(tidyr)
cars_separated <-Car_Total %>% separate(Model, into = c("Brand", "Model"), sep = " ", extra = "merge")
print(cars_separated)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res10 6 6 4 4 4 4 1 5 6
## 3 Res100 6 7 7 3 5 6 6 3 5
## 4 Res1000 6 6 7 6 6 6 6 6 6
## 5 Res1001 6 6 7 6 6 6 6 4 4
## 6 Res1002 3 1 4 3 5 6 6 2 6
## 7 Res1003 2 2 1 2 2 2 1 6 7
## 8 Res1004 7 7 7 6 5 6 5 6 6
## 9 Res1005 2 1 2 1 2 2 2 7 7
## 10 Res1006 6 6 6 5 5 5 5 3 3
## 11 Res1007 4 4 4 2 3 5 3 7 7
## 12 Res1008 5 5 5 3 3 5 5 5 5
## 13 Res1009 4 4 7 7 7 7 7 7 7
## 14 Res101 6 6 7 6 5 6 3 5 6
## 15 Res1010 7 7 7 4 7 4 4 5 5
## 16 Res1011 7 7 7 2 6 2 5 6 6
## 17 Res1012 6 6 6 6 5 3 4 5 5
## 18 Res1013 4 4 5 1 1 3 4 7 6
## 19 Res1014 5 5 5 4 3 5 5 5 5
## 20 Res1015 4 5 5 5 4 7 5 7 7
## 21 Res1016 6 6 7 7 6 7 6 7 7
## 22 Res1017 6 6 7 5 5 6 5 7 7
## 23 Res1018 4 3 4 2 3 5 4 7 6
## 24 Res1019 6 6 6 5 5 4 4 4 4
## 25 Res102 7 7 7 6 6 6 1 7 7
## 26 Res1020 6 7 6 6 6 5 5 7 7
## 27 Res1021 6 6 6 3 5 6 5 6 6
## 28 Res1022 6 6 6 4 4 7 7 5 5
## 29 Res1023 7 7 6 6 6 5 5 6 6
## 30 Res1024 6 5 4 5 4 6 5 6 7
## 31 Res1025 7 7 7 6 6 6 6 7 7
## 32 Res1026 6 7 7 7 7 6 6 7 7
## 33 Res1027 7 7 7 6 6 7 7 7 7
## 34 Res1028 4 7 7 6 7 6 7 7 6
## 35 Res1029 7 7 7 7 7 7 7 7 7
## 36 Res103 7 7 7 6 6 7 2 6 6
## 37 Res1030 7 7 7 7 7 7 7 7 7
## 38 Res1031 6 6 6 5 5 4 4 6 6
## 39 Res1032 7 6 6 5 6 6 6 7 7
## 40 Res1033 7 6 5 6 6 6 6 7 7
## 41 Res1034 7 7 7 7 7 6 5 7 6
## 42 Res1035 7 7 7 7 7 7 7 7 7
## 43 Res1036 6 5 6 5 5 6 6 7 7
## 44 Res1037 6 7 6 6 7 5 5 5 5
## 45 Res1038 6 5 5 5 5 5 5 5 5
## 46 Res1039 6 6 7 5 5 4 5 5 5
## 47 Res104 7 7 7 7 7 6 6 3 4
## 48 Res1040 5 6 5 6 6 4 4 7 7
## 49 Res1041 4 4 4 3 5 6 6 2 6
## 50 Res1042 5 5 1 2 2 2 1 6 7
## 51 Res1043 5 4 7 6 5 6 5 6 6
## 52 Res1044 4 4 2 1 2 2 2 7 7
## 53 Res1045 3 3 6 5 5 5 5 3 3
## 54 Res1046 3 4 4 2 3 5 3 7 7
## 55 Res1047 4 3 3 2 2 2 2 3 4
## 56 Res1048 3 4 4 3 4 4 4 4 5
## 57 Res1049 3 4 4 5 5 5 5 4 5
## 58 Res105 7 6 6 5 5 5 1 6 7
## 59 Res106 6 7 6 7 7 5 6 7 7
## 60 Res107 7 7 7 7 7 7 2 5 5
## 61 Res108 6 7 7 6 6 7 5 5 4
## 62 Res109 6 6 6 5 5 6 4 5 4
## 63 Res11 4 4 4 2 1 4 2 6 6
## 64 Res110 7 7 7 7 7 6 2 6 6
## 65 Res111 6 7 7 7 7 6 3 5 5
## 66 Res112 6 6 6 5 5 6 6 7 7
## 67 Res113 5 4 4 6 7 5 6 3 4
## 68 Res114 6 5 5 5 5 5 5 5 5
## 69 Res115 5 5 5 5 5 5 5 6 6
## 70 Res116 6 7 7 6 6 4 3 6 6
## 71 Res117 6 6 7 5 6 3 5 6 6
## 72 Res118 6 6 5 6 6 6 6 6 7
## 73 Res119 6 6 5 5 4 4 4 4 4
## 74 Res12 3 2 3 2 2 3 7 5 5
## 75 Res120 6 6 6 5 5 5 3 6 6
## 76 Res121 7 7 7 6 6 6 1 7 7
## 77 Res122 6 7 6 3 5 5 3 5 6
## 78 Res123 7 7 7 7 7 7 5 4 4
## 79 Res124 7 7 7 7 7 7 3 6 5
## 80 Res125 6 6 5 4 4 5 6 6 6
## 81 Res126 6 6 6 5 5 4 3 7 7
## 82 Res127 6 6 6 5 5 5 3 6 5
## 83 Res128 7 5 6 2 2 7 6 6 6
## 84 Res129 7 7 7 7 7 5 4 6 6
## 85 Res13 6 6 6 6 6 6 1 7 7
## 86 Res130 5 5 5 3 2 5 5 5 5
## 87 Res131 6 6 6 5 5 5 1 7 7
## 88 Res132 7 7 7 7 7 7 5 3 3
## 89 Res133 6 5 5 5 5 4 4 6 5
## 90 Res134 6 5 5 3 3 2 3 5 6
## 91 Res135 6 6 5 6 5 5 4 5 5
## 92 Res136 3 3 3 3 4 4 2 6 7
## 93 Res137 6 6 6 6 6 6 5 3 3
## 94 Res138 7 7 7 6 6 7 1 7 7
## 95 Res139 2 3 4 2 2 5 3 6 6
## 96 Res14 6 6 6 5 5 7 3 6 6
## 97 Res140 6 6 7 4 5 6 6 2 2
## 98 Res141 7 6 7 6 6 6 3 5 6
## 99 Res142 6 6 6 5 5 4 5 3 3
## 100 Res143 2 2 2 2 1 1 1 1 2
## 101 Res144 6 6 6 2 2 2 1 4 3
## 102 Res145 6 6 6 5 5 6 2 7 7
## 103 Res146 7 6 7 5 5 5 5 5 5
## 104 Res147 5 6 5 4 5 6 3 5 6
## 105 Res148 5 5 6 6 5 5 3 5 5
## 106 Res149 5 6 6 6 5 1 3 5 6
## 107 Res15 1 1 1 1 2 2 2 2 3
## 108 Res150 3 3 3 3 3 3 3 3 3
## 109 Res151 5 2 5 2 3 5 2 2 5
## 110 Res152 5 5 5 4 5 6 4 5 6
## 111 Res153 4 3 4 3 4 4 1 7 7
## 112 Res154 5 6 6 6 5 4 5 6 6
## 113 Res155 5 5 5 5 4 4 1 7 7
## 114 Res156 2 2 2 2 3 2 2 2 2
## 115 Res157 2 2 2 2 3 2 2 2 2
## 116 Res158 4 4 3 1 2 2 2 7 7
## 117 Res159 6 6 6 5 5 5 1 4 3
## 118 Res16 1 1 1 1 2 2 2 2 3
## 119 Res160 4 5 4 6 5 6 3 6 7
## 120 Res161 6 6 6 3 5 1 4 5 5
## 121 Res162 3 3 3 3 5 5 3 3 5
## 122 Res163 7 5 5 6 6 5 5 3 4
## 123 Res164 3 6 6 5 5 6 6 6 6
## 124 Res165 1 2 2 2 2 2 2 2 2
## 125 Res166 6 6 6 6 6 6 6 6 6
## 126 Res167 7 5 5 4 6 5 3 5 5
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## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 6 6 6
## 3 6 6 7 6 5 5
## 4 6 6 4 6 6 3
## 5 4 6 5 6 6 7
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## 130 5 5 5 4 2 2
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## 134 4 4 4 3 2 2
## 135 5 4 3 2 2 2
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## 144 7 7 6 7 6 7
## 145 3 3 4 4 4 4
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## 147 6 6 4 5 6 4
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## 151 5 2 2 2 2 6
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## 176 6 6 6 4 3 2
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## 191 6 6 6 6 6 6
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## 745 1 2 Collision Male 26 3 NaN European
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## Brand Model MPG Cyl acc1 C_cost. H_Cost Post.Satis Att_Mean
## 1 Ford Expedition 15 8 6 16 14 4 6
## 2 Ford Expedition 15 8 6 16 14 5 6
## 3 Toyota Rav4 24 4 8 10 8 4 6
## 4 Toyota Corolla 26 4 8 7 6 6 6
## 5 Toyota Corolla 26 4 8 7 6 5 6
## 6 Toyota Corolla 26 4 8 7 6 6 2
## 7 Toyota Corolla 26 4 8 7 6 5 2
## 8 Toyota Corolla 26 4 8 7 6 6 7
## 9 Toyota Corolla 26 4 8 7 6 7 2
## 10 Toyota Corolla 26 4 8 7 6 6 6
## 11 Toyota Corolla 26 4 8 7 6 6 4
## 12 Honda Pilot 20 6 6 12 10 5 5
## 13 Honda Pilot 20 6 6 12 10 4 4
## 14 Toyota Rav4 24 4 8 10 8 5 6
## 15 Honda Pilot 20 6 6 12 10 4 7
## 16 Honda Pilot 20 6 6 12 10 6 7
## 17 Honda Pilot 20 6 6 12 10 4 6
## 18 Honda Pilot 20 6 6 12 10 5 4
## 19 Honda Pilot 20 6 6 12 10 5 5
## 20 Honda Pilot 20 6 6 12 10 5 4
## 21 Honda Pilot 20 6 6 12 10 4 6
## 22 Honda Pilot 20 6 6 12 10 6 6
## 23 Honda Pilot 20 6 6 12 10 6 4
## 24 Toyota Corolla 26 4 8 7 6 5 6
## 25 Toyota Rav4 24 4 8 10 8 7 7
## 26 Toyota Corolla 26 4 8 7 6 7 6
## 27 Toyota Corolla 26 4 8 7 6 6 6
## 28 Toyota Corolla 26 4 8 7 6 5 6
## 29 Toyota Corolla 26 4 8 7 6 6 7
## 30 Toyota Corolla 26 4 8 7 6 6 6
## 31 Toyota Corolla 26 4 8 7 6 5 7
## 32 Toyota Corolla 26 4 8 7 6 6 6
## 33 Toyota Corolla 26 4 8 7 6 5 7
## 34 Toyota Corolla 26 4 8 7 6 6 6
## 35 Toyota Corolla 26 4 8 7 6 6 7
## 36 Toyota Rav4 24 4 8 10 8 6 7
## 37 Toyota Corolla 26 4 8 7 6 7 7
## 38 Toyota Corolla 26 4 8 7 6 6 6
## 39 Toyota Corolla 26 4 8 7 6 6 6
## 40 Toyota Corolla 26 4 8 7 6 6 6
## 41 Toyota Corolla 26 4 8 7 6 6 7
## 42 Toyota Corolla 26 4 8 7 6 6 7
## 43 Toyota Corolla 26 4 8 7 6 6 6
## 44 Toyota Corolla 26 4 8 7 6 6 6
## 45 Toyota Corolla 26 4 8 7 6 6 6
## 46 Toyota Corolla 26 4 8 7 6 7 6
## 47 Toyota Rav4 24 4 8 10 8 6 7
## 48 Toyota Corolla 26 4 8 7 6 7 6
## 49 Fiat 500x 22 4 8 8 6 7 4
## 50 Fiat 500x 22 4 8 8 6 6 5
## 51 Fiat 500x 22 4 8 8 6 6 4
## 52 Fiat 500x 22 4 8 8 6 2 4
## 53 Fiat 500x 22 4 8 8 6 6 3
## 54 Fiat 500x 22 4 8 8 6 4 4
## 55 Fiat 500x 22 4 8 8 6 7 4
## 56 Fiat 500x 22 4 8 8 6 2 4
## 57 Fiat 500x 22 4 8 8 6 5 4
## 58 Toyota Rav4 24 4 8 10 8 4 6
## 59 Toyota Rav4 24 4 8 10 8 4 6
## 60 Toyota Rav4 24 4 8 10 8 6 7
## 61 Toyota Rav4 24 4 8 10 8 5 6
## 62 Toyota Rav4 24 4 8 10 8 3 6
## 63 Ford Expedition 15 8 6 16 14 5 4
## 64 Toyota Rav4 24 4 8 10 8 5 7
## 65 Toyota Rav4 24 4 8 10 8 5 6
## 66 Toyota Rav4 24 4 8 10 8 5 6
## 67 Toyota Rav4 24 4 8 10 8 6 4
## 68 Toyota Rav4 24 4 8 10 8 4 6
## 69 Toyota Rav4 24 4 8 10 8 6 5
## 70 Toyota Rav4 24 4 8 10 8 5 6
## 71 Toyota Rav4 24 4 8 10 8 6 6
## 72 Toyota Rav4 24 4 8 10 8 7 6
## 73 Toyota Rav4 24 4 8 10 8 5 6
## 74 Ford Expedition 15 8 6 16 14 5 2
## 75 Toyota Rav4 24 4 8 10 8 7 6
## 76 Honda CRV 26 4 8 8 7 5 7
## 77 Honda CRV 26 4 8 8 7 7 6
## 78 Honda CRV 26 4 8 8 7 6 7
## 79 Honda CRV 26 4 8 8 7 6 7
## 80 Honda CRV 26 4 8 8 7 5 6
## 81 Honda CRV 26 4 8 8 7 6 6
## 82 Honda CRV 26 4 8 8 7 5 6
## 83 Honda CRV 26 4 8 8 7 5 6
## 84 Honda CRV 26 4 8 8 7 5 7
## 85 Ford Expedition 15 8 6 16 14 4 6
## 86 Honda CRV 26 4 8 8 7 6 5
## 87 Ford Explorer 19 6 6 13 11 6 6
## 88 Ford Explorer 19 6 6 13 11 3 7
## 89 Ford Explorer 19 6 6 13 11 2 6
## 90 Ford Explorer 19 6 6 13 11 6 6
## 91 Chrysler Jeep 18 6 4 12 10 5 6
## 92 Chrysler Jeep 18 6 4 12 10 6 3
## 93 Chrysler Jeep 18 6 4 12 10 7 6
## 94 Chrysler Jeep 18 6 4 12 10 4 7
## 95 Chrysler Jeep 18 6 4 12 10 4 2
## 96 Ford Expedition 15 8 6 16 14 4 6
## 97 Chrysler Jeep 18 6 4 12 10 6 6
## 98 Chrysler Jeep 18 6 4 12 10 5 6
## 99 Chrysler Jeep 18 6 4 12 10 7 6
## 100 Chrysler Jeep 18 6 4 12 10 3 2
## 101 Chrysler Jeep 18 6 4 12 10 4 6
## 102 Chrysler Jeep 18 6 4 12 10 6 6
## 103 Chrysler Jeep 18 6 4 12 10 4 6
## 104 Chrysler Jeep 18 6 4 12 10 6 6
## 105 Chrysler Jeep 18 6 4 12 10 6 5
## 106 Chrysler Jeep 18 6 4 12 10 5 6
## 107 Ford Expedition 15 8 6 16 14 4 1
## 108 Chrysler Jeep 18 6 4 12 10 5 3
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## 133 Toyota Highlander 20 6 7 10 8 7 6
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## 189 Toyota Rav4 24 4 8 10 8 5 4
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## 193 Toyota Rav4 24 4 8 10 8 4 4
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## 195 Toyota Rav4 24 4 8 10 8 5 6
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## 200 Toyota Rav4 24 4 8 10 8 5 5
## 201 Toyota Rav4 24 4 8 10 8 7 4
## 202 Toyota Rav4 24 4 8 10 8 6 4
## 203 Toyota Rav4 24 4 8 10 8 6 4
## 204 Toyota Rav4 24 4 8 10 8 6 4
## 205 Toyota Rav4 24 4 8 10 8 4 4
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## 216 Kia Sorento 22 4 8 9 7 5 4
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## 220 Kia Sorento 22 4 8 9 7 6 4
## 221 Kia Sorento 22 4 8 9 7 5 4
## 222 Kia Sorento 22 4 8 9 7 7 4
## 223 Kia Sorento 22 4 8 9 7 6 5
## 224 Kia Sorento 22 4 8 9 7 7 5
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## 226 Kia Sorento 22 4 8 9 7 5 6
## 227 Kia Sorento 22 4 8 9 7 6 4
## 228 Kia Sorento 22 4 8 9 7 5 4
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## 232 Ford Explorer 19 6 6 13 11 6 4
## 233 Ford Explorer 19 6 6 13 11 4 4
## 234 Ford Explorer 19 6 6 13 11 6 4
## 235 Ford Explorer 19 6 6 13 11 6 6
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## 238 Ford Explorer 19 6 6 13 11 3 4
## 239 Ford Explorer 19 6 6 13 11 3 5
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## 242 Ford Explorer 19 6 6 13 11 3 4
## 243 Ford Explorer 19 6 6 13 11 6 4
## 244 Ford Explorer 19 6 6 13 11 6 6
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## 271 Ford Explorer 19 6 6 13 11 5 4
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## 276 Ford Explorer 19 6 6 13 11 3 4
## 277 Ford Explorer 19 6 6 13 11 5 4
## 278 Ford Explorer 19 6 6 13 11 7 4
## 279 Ford Explorer 19 6 6 13 11 6 6
## 280 Ford Explorer 19 6 6 13 11 5 2
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## 283 Ford Explorer 19 6 6 13 11 6 4
## 284 Ford Explorer 19 6 6 13 11 7 4
## 285 Dodge Journey 16 6 6 12 11 5 7
## 286 Ford Explorer 19 6 6 13 11 7 4
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## 303 Ford Explorer 19 6 6 13 11 6 6
## 304 Ford Explorer 19 6 6 13 11 7 4
## 305 Ford Explorer 19 6 6 13 11 5 4
## 306 Ford Explorer 19 6 6 13 11 7 4
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## 309 Ford Explorer 19 6 6 13 11 7 6
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## 313 Kia Sorento 22 4 8 9 7 5 4
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## 315 Kia Sorento 22 4 8 9 7 7 6
## 316 Kia Sorento 22 4 8 9 7 6 6
## 317 Kia Sorento 22 4 8 9 7 6 3
## 318 Dodge Journey 16 6 6 12 11 5 6
## 319 Kia Sorento 22 4 8 9 7 5 2
## 320 Kia Sorento 22 4 8 9 7 5 4
## 321 Kia Sorento 22 4 8 9 7 6 4
## 322 Kia Sorento 22 4 8 9 7 6 4
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## 324 Kia Sorento 22 4 8 9 7 6 4
## 325 Kia Sorento 22 4 8 9 7 7 4
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## 340 Dodge Journey 16 6 6 12 11 4 6
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## 362 Dodge Journey 16 6 6 12 11 3 6
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## 368 Toyota Highlander 20 6 7 10 8 6 4
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## 385 Dodge Journey 16 6 6 12 11 3 1
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## 390 Ford Explorer 19 6 6 13 11 6 4
## 391 Ford Explorer 19 6 6 13 11 6 4
## 392 Ford Explorer 19 6 6 13 11 6 6
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## 395 Ford Explorer 19 6 6 13 11 7 4
## 396 Dodge Journey 16 6 6 12 11 4 1
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## 398 Ford Explorer 19 6 6 13 11 6 4
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## 400 Ford Explorer 19 6 6 13 11 5 4
## 401 Ford Explorer 19 6 6 13 11 6 4
## 402 Ford Explorer 19 6 6 13 11 5 4
## 403 Ford Explorer 19 6 6 13 11 6 4
## 404 Ford Explorer 19 6 6 13 11 7 6
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## 410 Ford Explorer 19 6 6 13 11 5 6
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## 544 Dodge Journey 16 6 6 12 11 3 2
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## 548 Dodge Journey 16 6 6 12 11 3 2
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## 556 Dodge Journey 16 6 6 12 11 5 7
## 557 Dodge Journey 16 6 6 12 11 3 6
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## 559 Dodge Journey 16 6 6 12 11 5 7
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## 612 Toyota Highlander 20 6 7 10 8 4 6
## 613 Toyota Highlander 20 6 7 10 8 4 6
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## 630 Honda Pilot 20 6 6 12 10 5 4
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## 654 Toyota Highlander 20 6 7 10 8 5 4
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## 657 Toyota Highlander 20 6 7 10 8 6 6
## 658 Toyota Highlander 20 6 7 10 8 5 1
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## 691 Chevrolet Camaro 14 8 4 14 12 7 6
## 692 Chevrolet Camaro 14 8 4 14 12 7 5
## 693 Chevrolet Camaro 14 8 4 14 12 6 7
## 694 Chevrolet Camaro 14 8 4 14 12 6 6
## 695 Chrysler Jeep 18 6 4 12 10 5 6
## 696 Chevrolet Camaro 14 8 4 14 12 4 2
## 697 Chevrolet Camaro 14 8 4 14 12 2 6
## 698 Chevrolet Camaro 14 8 4 14 12 5 5
## 699 Chevrolet Camaro 14 8 4 14 12 2 4
## 700 Chevrolet Camaro 14 8 4 14 12 2 5
## 701 Chevrolet Camaro 14 8 4 14 12 5 2
## 702 Chevrolet Camaro 14 8 4 14 12 2 6
## 703 Chevrolet Camaro 14 8 4 14 12 5 4
## 704 Chevrolet Camaro 14 8 4 14 12 5 4
## 705 Chevrolet Camaro 14 8 4 14 12 2 2
## 706 Chrysler Jeep 18 6 4 12 10 3 5
## 707 Chevrolet Camaro 14 8 4 14 12 6 2
## 708 Chevrolet Camaro 14 8 4 14 12 6 2
## 709 Chevrolet Camaro 14 8 4 14 12 6 3
## 710 Chevrolet Camaro 14 8 4 14 12 6 5
## 711 Chevrolet Camaro 14 8 4 14 12 3 6
## 712 Chevrolet Camaro 14 8 4 14 12 2 6
## 713 Chevrolet Camaro 14 8 4 14 12 5 2
## 714 Chevrolet Camaro 14 8 4 14 12 7 6
## 715 Chevrolet Camaro 14 8 4 14 12 6 6
## 716 Chevrolet Camaro 14 8 4 14 12 7 5
## 717 Ford Expedition 15 8 6 16 14 3 1
## 718 Chrysler Jeep 18 6 4 12 10 6 6
## 719 Chevrolet Camaro 14 8 4 14 12 2 6
## 720 Chevrolet Camaro 14 8 4 14 12 4 6
## 721 Chevrolet Camaro 14 8 4 14 12 2 6
## 722 Chevrolet Camaro 14 8 4 14 12 7 6
## 723 Chevrolet Camaro 14 8 4 14 12 2 3
## 724 Chevrolet Camaro 14 8 4 14 12 2 6
## 725 Chevrolet Camaro 14 8 4 14 12 7 4
## 726 Chevrolet Camaro 14 8 4 14 12 5 6
## 727 Chevrolet Camaro 14 8 4 14 12 2 5
## 728 Chevrolet Camaro 14 8 4 14 12 5 4
## 729 Chrysler Jeep 18 6 4 12 10 3 6
## 730 Chevrolet Camaro 14 8 4 14 12 2 5
## 731 Chevrolet Camaro 14 8 4 14 12 6 4
## 732 Chevrolet Camaro 14 8 4 14 12 6 6
## 733 Chevrolet Camaro 14 8 4 14 12 5 7
## 734 Chevrolet Camaro 14 8 4 14 12 6 7
## 735 Chevrolet Camaro 14 8 4 14 12 6 7
## 736 Chevrolet Camaro 14 8 4 14 12 7 7
## 737 Chevrolet Camaro 14 8 4 14 12 5 7
## 738 Chevrolet Camaro 14 8 4 14 12 6 6
## 739 Chevrolet Camaro 14 8 4 14 12 6 6
## 740 Chrysler Jeep 18 6 4 12 10 3 6
## 741 Chevrolet Camaro 14 8 4 14 12 6 6
## 742 Chevrolet Camaro 14 8 4 14 12 5 7
## 743 Chevrolet Camaro 14 8 4 14 12 6 6
## 744 Chevrolet Camaro 14 8 4 14 12 7 6
## 745 Toyota Highlander 20 6 7 10 8 4 6
## 746 Toyota Highlander 20 6 7 10 8 5 7
## 747 Toyota Highlander 20 6 7 10 8 5 7
## 748 Toyota Highlander 20 6 7 10 8 6 7
## 749 Toyota Highlander 20 6 7 10 8 6 7
## 750 Toyota Highlander 20 6 7 10 8 5 5
## 751 Chrysler Jeep 18 6 4 12 10 5 6
## 752 Toyota Highlander 20 6 7 10 8 5 6
## 753 Toyota Highlander 20 6 7 10 8 6 7
## 754 Toyota Highlander 20 6 7 10 8 6 4
## 755 Toyota Highlander 20 6 7 10 8 6 7
## 756 Toyota Highlander 20 6 7 10 8 6 6
## 757 Toyota Highlander 20 6 7 10 8 5 6
## 758 Toyota Highlander 20 6 7 10 8 4 6
## 759 Toyota Highlander 20 6 7 10 8 5 7
## 760 Toyota Highlander 20 6 7 10 8 4 7
## 761 Toyota Highlander 20 6 7 10 8 3 6
## 762 Honda CRV 26 4 8 8 7 6 4
## 763 Toyota Highlander 20 6 7 10 8 4 6
## 764 Toyota Highlander 20 6 7 10 8 4 6
## 765 Toyota Highlander 20 6 7 10 8 4 5
## 766 Toyota Highlander 20 6 7 10 8 4 7
## 767 Toyota Highlander 20 6 7 10 8 6 6
## 768 Toyota Highlander 20 6 7 10 8 5 5
## 769 Toyota Highlander 20 6 7 10 8 5 6
## 770 Toyota Highlander 20 6 7 10 8 5 5
## 771 Toyota Highlander 20 6 7 10 8 6 7
## 772 Toyota Highlander 20 6 7 10 8 4 7
## 773 Honda CRV 26 4 8 8 7 7 2
## 774 Toyota Highlander 20 6 7 10 8 6 7
## 775 Toyota Highlander 20 6 7 10 8 4 4
## 776 Toyota Highlander 20 6 7 10 8 5 6
## 777 Toyota Highlander 20 6 7 10 8 5 7
## 778 Toyota Highlander 20 6 7 10 8 5 6
## 779 Toyota Highlander 20 6 7 10 8 3 3
## 780 Toyota Highlander 20 6 7 10 8 6 6
## 781 Toyota Highlander 20 6 7 10 8 5 6
## 782 Toyota Highlander 20 6 7 10 8 6 2
## 783 Toyota Highlander 20 6 7 10 8 5 6
## 784 Honda CRV 26 4 8 8 7 6 4
## 785 Toyota Highlander 20 6 7 10 8 5 7
## 786 Toyota Highlander 20 6 7 10 8 6 6
## 787 Toyota Highlander 20 6 7 10 8 5 6
## 788 Honda Pilot 20 6 6 12 10 5 7
## 789 Honda Pilot 20 6 6 12 10 6 5
## 790 Honda Pilot 20 6 6 12 10 6 6
## 791 Honda Pilot 20 6 6 12 10 5 4
## 792 Honda Pilot 20 6 6 12 10 6 6
## 793 Honda Pilot 20 6 6 12 10 4 4
## 794 Honda Pilot 20 6 6 12 10 5 6
## 795 Honda CRV 26 4 8 8 7 6 6
## 796 Honda Pilot 20 6 6 12 10 6 6
## 797 Honda Pilot 20 6 6 12 10 6 6
## 798 Honda Pilot 20 6 6 12 10 7 5
## 799 Honda Pilot 20 6 6 12 10 5 7
## 800 Honda Pilot 20 6 6 12 10 5 7
## 801 Honda Pilot 20 6 6 12 10 6 5
## 802 Honda Pilot 20 6 6 12 10 6 4
## 803 Honda Pilot 20 6 6 12 10 6 6
## 804 Honda Pilot 20 6 6 12 10 5 6
## 805 Honda Pilot 20 6 6 12 10 6 4
## 806 Honda CRV 26 4 8 8 7 6 6
## 807 Honda Pilot 20 6 6 12 10 5 6
## 808 Buick Encore 17 4 7 8 7 5 6
## 809 Buick Encore 17 4 7 8 7 4 6
## 810 Buick Encore 17 4 7 8 7 4 6
## 811 Buick Encore 17 4 7 8 7 6 6
## 812 Buick Encore 17 4 7 8 7 7 1
## 813 Buick Encore 17 4 7 8 7 7 6
## 814 Buick Encore 17 4 7 8 7 4 6
## 815 Buick Encore 17 4 7 8 7 5 6
## 816 Buick Encore 17 4 7 8 7 3 5
## 817 Honda CRV 26 4 8 8 7 6 5
## 818 Buick Encore 17 4 7 8 7 6 4
## 819 Buick Encore 17 4 7 8 7 2 4
## 820 Buick Encore 17 4 7 8 7 2 7
## 821 Buick Encore 17 4 7 8 7 6 5
## 822 Buick Encore 17 4 7 8 7 6 7
## 823 Buick Encore 17 4 7 8 7 7 6
## 824 Buick Encore 17 4 7 8 7 6 6
## 825 Buick Encore 17 4 7 8 7 3 6
## 826 Buick Encore 17 4 7 8 7 5 6
## 827 Buick Encore 17 4 7 8 7 6 6
## 828 Ford Expedition 15 8 6 16 14 6 6
## 829 Honda CRV 26 4 8 8 7 7 6
## 830 Buick Encore 17 4 7 8 7 7 6
## 831 Buick Encore 17 4 7 8 7 6 6
## 832 Buick Encore 17 4 7 8 7 6 6
## 833 Buick Encore 17 4 7 8 7 5 2
## 834 Buick Encore 17 4 7 8 7 5 3
## 835 Buick Encore 17 4 7 8 7 6 6
## 836 Buick Encore 17 4 7 8 7 6 4
## 837 Buick Encore 17 4 7 8 7 5 6
## 838 Buick Encore 17 4 7 8 7 5 6
## 839 Buick Encore 17 4 7 8 7 6 5
## 840 Honda CRV 26 4 8 8 7 4 6
## 841 Buick Encore 17 4 7 8 7 7 6
## 842 Buick Encore 17 4 7 8 7 5 6
## 843 Lincoln Navigator 15 8 5 16 13 6 5
## 844 Lincoln Navigator 15 8 5 16 13 5 2
## 845 Lincoln Navigator 15 8 5 16 13 7 2
## 846 Lincoln Navigator 15 8 5 16 13 5 7
## 847 Lincoln Navigator 15 8 5 16 13 6 6
## 848 Lincoln Navigator 15 8 5 16 13 4 6
## 849 Lincoln Navigator 15 8 5 16 13 4 5
## 850 Lincoln Navigator 15 8 5 16 13 5 6
## 851 Honda CRV 26 4 8 8 7 6 6
## 852 Lincoln Navigator 15 8 5 16 13 5 6
## 853 Lincoln Navigator 15 8 5 16 13 6 7
## 854 Lincoln Navigator 15 8 5 16 13 7 6
## 855 Lincoln Navigator 15 8 5 16 13 7 6
## 856 Lincoln Navigator 15 8 5 16 13 6 6
## 857 Lincoln Navigator 15 8 5 16 13 7 6
## 858 Lincoln Navigator 15 8 5 16 13 5 6
## 859 Lincoln Navigator 15 8 5 16 13 6 6
## 860 Lincoln Navigator 15 8 5 16 13 7 6
## 861 Lincoln Navigator 15 8 5 16 13 7 5
## 862 Honda CRV 26 4 8 8 7 6 6
## 863 Lincoln Navigator 15 8 5 16 13 6 6
## 864 Lincoln Navigator 15 8 5 16 13 7 6
## 865 Lincoln Navigator 15 8 5 16 13 5 7
## 866 Lincoln Navigator 15 8 5 16 13 3 4
## 867 Lincoln Navigator 15 8 5 16 13 6 7
## 868 Lincoln Navigator 15 8 5 16 13 7 6
## 869 Ford Expedition 15 8 6 16 14 7 7
## 870 Ford Expedition 15 8 6 16 14 4 6
## 871 Ford Expedition 15 8 6 16 14 5 4
## 872 Ford Expedition 15 8 6 16 14 6 7
## 873 Honda CRV 26 4 8 8 7 4 6
## 874 Ford Expedition 15 8 6 16 14 7 7
## 875 Ford Expedition 15 8 6 16 14 2 7
## 876 Ford Expedition 15 8 6 16 14 4 7
## 877 Ford Expedition 15 8 6 16 14 3 7
## 878 Ford Expedition 15 8 6 16 14 3 7
## 879 Ford Expedition 15 8 6 16 14 7 6
## 880 Ford Expedition 15 8 6 16 14 3 4
## 881 Ford Expedition 15 8 6 16 14 3 5
## 882 Ford Expedition 15 8 6 16 14 2 7
## 883 Ford Expedition 15 8 6 16 14 5 6
## 884 Honda CRV 26 4 8 8 7 6 6
## 885 Ford Expedition 15 8 6 16 14 2 7
## 886 Ford Expedition 15 8 6 16 14 6 7
## 887 Ford Expedition 15 8 6 16 14 2 7
## 888 Ford Expedition 15 8 6 16 14 6 6
## 889 Toyota Corolla 26 4 8 7 6 7 6
## 890 Toyota Corolla 26 4 8 7 6 5 6
## 891 Toyota Corolla 26 4 8 7 6 6 7
## 892 Toyota Corolla 26 4 8 7 6 7 4
## 893 Toyota Corolla 26 4 8 7 6 7 7
## 894 Toyota Corolla 26 4 8 7 6 6 4
## 895 Honda CRV 26 4 8 8 7 3 7
## 896 Toyota Corolla 26 4 8 7 6 5 7
## 897 Toyota Corolla 26 4 8 7 6 5 7
## 898 Toyota Corolla 26 4 8 7 6 6 6
## 899 Toyota Corolla 26 4 8 7 6 6 7
## 900 Toyota Corolla 26 4 8 7 6 5 6
## 901 Toyota Corolla 26 4 8 7 6 6 7
## 902 Toyota Corolla 26 4 8 7 6 7 4
## 903 Toyota Corolla 26 4 8 7 6 6 6
## 904 Toyota Corolla 26 4 8 7 6 6 6
## 905 Toyota Corolla 26 4 8 7 6 5 5
## 906 Honda CRV 26 4 8 8 7 6 4
## 907 Toyota Corolla 26 4 8 7 6 7 6
## 908 Toyota Corolla 26 4 8 7 6 7 6
## 909 Toyota Corolla 26 4 8 7 6 7 4
## 910 Toyota Corolla 26 4 8 7 6 7 6
## 911 Toyota Corolla 26 4 8 7 6 7 7
## 912 Toyota Corolla 26 4 8 7 6 5 6
## 913 Toyota Corolla 26 4 8 7 6 6 5
## 914 Toyota Corolla 26 4 8 7 6 6 6
## 915 Toyota Corolla 26 4 8 7 6 6 4
## 916 Toyota Corolla 26 4 8 7 6 6 6
## 917 Honda CRV 26 4 8 8 7 4 7
## 918 Toyota Corolla 26 4 8 7 6 6 7
## 919 Toyota Corolla 26 4 8 7 6 7 6
## 920 Toyota Corolla 26 4 8 7 6 6 6
## 921 Toyota Corolla 26 4 8 7 6 6 6
## 922 Toyota Corolla 26 4 8 7 6 5 4
## 923 Toyota Corolla 26 4 8 7 6 6 4
## 924 Toyota Corolla 26 4 8 7 6 7 6
## 925 Toyota Corolla 26 4 8 7 6 7 5
## 926 Toyota Corolla 26 4 8 7 6 7 4
## 927 Toyota Corolla 26 4 8 7 6 7 6
## 928 Honda CRV 26 4 8 8 7 4 7
## 929 Toyota Corolla 26 4 8 7 6 6 6
## 930 Toyota Corolla 26 4 8 7 6 7 7
## 931 Toyota Corolla 26 4 8 7 6 6 6
## 932 Toyota Corolla 26 4 8 7 6 5 6
## 933 Toyota Corolla 26 4 8 7 6 7 6
## 934 Honda Pilot 20 6 6 12 10 7 6
## 935 Honda Pilot 20 6 6 12 10 5 4
## 936 Honda Pilot 20 6 6 12 10 6 7
## 937 Honda Pilot 20 6 6 12 10 5 4
## 938 Honda Pilot 20 6 6 12 10 5 6
## 939 Ford Expedition 15 8 6 16 14 3 2
## 940 Honda CRV 26 4 8 8 7 5 6
## 941 Honda Pilot 20 6 6 12 10 5 6
## 942 Honda Pilot 20 6 6 12 10 5 5
## 943 Honda Pilot 20 6 6 12 10 6 4
## 944 Honda Pilot 20 6 6 12 10 6 7
## 945 Honda Pilot 20 6 6 12 10 5 6
## 946 Honda Pilot 20 6 6 12 10 6 7
## 947 Honda Pilot 20 6 6 12 10 5 5
## 948 Honda Pilot 20 6 6 12 10 4 6
## 949 Honda Pilot 20 6 6 12 10 5 6
## 950 Honda Pilot 20 6 6 12 10 6 6
## 951 Honda CRV 26 4 8 8 7 5 7
## 952 Honda Pilot 20 6 6 12 10 5 7
## 953 Ford Expedition 15 8 6 16 14 4 4
## 954 Ford Expedition 15 8 6 16 14 5 6
## 955 Ford Expedition 15 8 6 16 14 6 6
## 956 Ford Expedition 15 8 6 16 14 5 7
## 957 Ford Expedition 15 8 6 16 14 2 4
## 958 Ford Expedition 15 8 6 16 14 5 6
## 959 Ford Expedition 15 8 6 16 14 2 2
## 960 Ford Expedition 15 8 6 16 14 6 7
## 961 Ford Expedition 15 8 6 16 14 2 4
## 962 Honda CRV 26 4 8 8 7 4 6
## 963 Ford Expedition 15 8 6 16 14 6 4
## 964 Ford Expedition 15 8 6 16 14 2 6
## 965 Ford Expedition 15 8 6 16 14 2 6
## 966 Ford Expedition 15 8 6 16 14 5 6
## 967 Ford Expedition 15 8 6 16 14 6 6
## 968 Ford Expedition 15 8 6 16 14 2 7
## 969 Ford Expedition 15 8 6 16 14 6 5
## 970 Ford Expedition 15 8 6 16 14 2 6
## 971 Ford Expedition 15 8 6 16 14 2 4
## 972 Ford Expedition 15 8 6 16 14 5 5
## 973 Honda CRV 26 4 8 8 7 5 6
## 974 Ford Expedition 15 8 6 16 14 2 4
## 975 Ford Expedition 15 8 6 16 14 6 4
## 976 Ford Expedition 15 8 6 16 14 2 2
## 977 Ford Expedition 15 8 6 16 14 2 4
## 978 Ford Expedition 15 8 6 16 14 4 7
## 979 Ford Expedition 15 8 6 16 14 4 5
## 980 Ford Expedition 15 8 6 16 14 3 2
## 981 Ford Expedition 15 8 6 16 14 6 7
## 982 Ford Expedition 15 8 6 16 14 5 2
## 983 Ford Expedition 15 8 6 16 14 5 7
## 984 Honda CRV 26 4 8 8 7 7 6
## 985 Ford Expedition 15 8 6 16 14 3 3
## 986 Ford Expedition 15 8 6 16 14 6 5
## 987 Ford Expedition 15 8 6 16 14 5 7
## 988 Ford Expedition 15 8 6 16 14 2 4
## 989 Ford Expedition 15 8 6 16 14 6 5
## 990 Ford Expedition 15 8 6 16 14 4 6
## 991 Ford Expedition 15 8 6 16 14 2 4
## 992 Ford Expedition 15 8 6 16 14 6 5
## 993 Ford Expedition 15 8 6 16 14 6 5
## 994 Ford Expedition 15 8 6 16 14 6 7
## 995 Honda CRV 26 4 8 8 7 5 6
## 996 Ford Expedition 15 8 6 16 14 5 6
## 997 Toyota Corolla 26 4 8 7 6 5 7
## 998 Toyota Corolla 26 4 8 7 6 6 5
## 999 Toyota Corolla 26 4 8 7 6 6 6
## 1000 Toyota Corolla 26 4 8 7 6 6 7
## 1001 Toyota Corolla 26 4 8 7 6 5 4
## 1002 Toyota Corolla 26 4 8 7 6 5 6
## 1003 Toyota Corolla 26 4 8 7 6 6 7
## 1004 Toyota Corolla 26 4 8 7 6 6 6
## 1005 Toyota Corolla 26 4 8 7 6 6 4
## 1006 Toyota Rav4 24 4 8 10 8 6 7
## 1007 Toyota Corolla 26 4 8 7 6 6 6
## 1008 Toyota Corolla 26 4 8 7 6 5 2
## 1009 Toyota Corolla 26 4 8 7 6 6 4
## 1010 Ford Expedition 15 8 6 16 14 2 3
## 1011 Ford Expedition 15 8 6 16 14 6 4
## 1012 Ford Expedition 15 8 6 16 14 2 6
## 1013 Ford Expedition 15 8 6 16 14 6 4
## 1014 Ford Expedition 15 8 6 16 14 2 6
## 1015 Ford Expedition 15 8 6 16 14 5 6
## 1016 Lincoln Navigator 15 8 5 16 13 7 5
## 1017 Toyota Rav4 24 4 8 10 8 6 6
## 1018 Lincoln Navigator 15 8 5 16 13 5 2
## 1019 Lincoln Navigator 15 8 5 16 13 6 6
## 1020 Lincoln Navigator 15 8 5 16 13 6 7
## 1021 Lincoln Navigator 15 8 5 16 13 6 4
## 1022 Lincoln Navigator 15 8 5 16 13 6 7
## 1023 Lincoln Navigator 15 8 5 16 13 7 7
## 1024 Lincoln Navigator 15 8 5 16 13 2 4
## 1025 Lincoln Navigator 15 8 5 16 13 6 6
## 1026 Lincoln Navigator 15 8 5 16 13 7 5
## 1027 Lincoln Navigator 15 8 5 16 13 3 4
## 1028 Toyota Rav4 24 4 8 10 8 6 6
## 1029 Lincoln Navigator 15 8 5 16 13 6 7
## 1030 Lincoln Navigator 15 8 5 16 13 6 6
## 1031 Lincoln Navigator 15 8 5 16 13 6 2
## 1032 Lincoln Navigator 15 8 5 16 13 6 5
## 1033 Honda Pilot 20 6 6 12 10 7 6
## 1034 Honda Pilot 20 6 6 12 10 6 4
## 1035 Honda Pilot 20 6 6 12 10 6 4
## 1036 Honda Pilot 20 6 6 12 10 5 6
## 1037 Honda Pilot 20 6 6 12 10 6 6
## 1038 Honda Pilot 20 6 6 12 10 6 4
## 1039 Toyota Rav4 24 4 8 10 8 4 7
## 1040 Honda Pilot 20 6 6 12 10 5 6
## 1041 Toyota Corolla 26 4 8 7 6 6 2
## 1042 Toyota Corolla 26 4 8 7 6 5 2
## 1043 Toyota Corolla 26 4 8 7 6 5 3
## 1044 Toyota Corolla 26 4 8 7 6 6 4
## 1045 Toyota Corolla 26 4 8 7 6 6 6
## 1046 Toyota Corolla 26 4 8 7 6 6 6
## 1047 Toyota Corolla 26 4 8 7 6 5 2
## 1048 Toyota Corolla 26 4 8 7 6 6 5
## 1049 Toyota Corolla 26 4 8 7 6 4 4Car_Total<-cars_separated
ggplot(Car_Total, aes(x=Region, fill=Brand))+
theme_bw()+
geom_bar()+
labs(y="Number of cars", title = "Number of Cars by Brands across Region")
The following includes determining the frequency by the total number of cars by brand. Once this is done, we are able to a crosstabelation table by brand. Then completing an observation of the attitudes toward the car brand by the regions. This completed the graph that shows the attitude toward car brands across regions.
count(Car_Total, Car_Total$Brand, name = "Freq")## Car_Total$Brand Freq
## 1 Buick 31
## 2 Chevrolet 64
## 3 Chrysler 169
## 4 Dodge 41
## 5 Fiat 18
## 6 Ford 202
## 7 Honda 159
## 8 Kia 34
## 9 Lincoln 39
## 10 Toyota 292count(Car_Total, Car_Total$Make, Car_Total$Brand, name = "Freq")## Car_Total$Brand Freq
## 1 Buick 31
## 2 Chevrolet 64
## 3 Chrysler 169
## 4 Dodge 41
## 5 Fiat 18
## 6 Ford 202
## 7 Honda 159
## 8 Kia 34
## 9 Lincoln 39
## 10 Toyota 292xtabs(~Region + Brand, Car_Total)## Brand
## Region Buick Chevrolet Chrysler Dodge Fiat Ford Honda Kia Lincoln
## American 17 22 54 22 9 81 38 15 0
## Asian 0 0 24 0 0 24 58 0 0
## European 10 21 13 0 9 29 29 19 0
## Middle Eastern 4 21 78 19 0 68 34 0 39
## Brand
## Region Toyota
## American 102
## Asian 102
## European 80
## Middle Eastern 8brand_region_counts<-table(Car_Total$Brand, Car_Total$Region)
print(brand_region_counts)##
## American Asian European Middle Eastern
## Buick 17 0 10 4
## Chevrolet 22 0 21 21
## Chrysler 54 24 13 78
## Dodge 22 0 0 19
## Fiat 9 0 9 0
## Ford 81 24 29 68
## Honda 38 58 29 34
## Kia 15 0 19 0
## Lincoln 0 0 0 39
## Toyota 102 102 80 8brand_region_table<-aggregate(Att_1~Brand+Region, Car_Total, mean)
print(brand_region_table)## Brand Region Att_1
## 1 Buick American 5.529412
## 2 Chevrolet American 5.227273
## 3 Chrysler American 5.018519
## 4 Dodge American 4.818182
## 5 Fiat American 4.666667
## 6 Ford American 4.567901
## 7 Honda American 5.657895
## 8 Kia American 3.266667
## 9 Toyota American 5.254902
## 10 Chrysler Asian 5.291667
## 11 Ford Asian 4.166667
## 12 Honda Asian 5.568966
## 13 Toyota Asian 4.882353
## 14 Buick European 5.000000
## 15 Chevrolet European 5.000000
## 16 Chrysler European 5.230769
## 17 Fiat European 3.777778
## 18 Ford European 5.241379
## 19 Honda European 4.758621
## 20 Kia European 3.789474
## 21 Toyota European 4.800000
## 22 Buick Middle Eastern 6.000000
## 23 Chevrolet Middle Eastern 5.904762
## 24 Chrysler Middle Eastern 4.038462
## 25 Dodge Middle Eastern 4.473684
## 26 Ford Middle Eastern 4.014706
## 27 Honda Middle Eastern 5.500000
## 28 Lincoln Middle Eastern 5.692308
## 29 Toyota Middle Eastern 5.375000ggplot(brand_region_table, aes(x=Region, y=Att_1, group=Brand))+
geom_line(aes(color=Brand))+
geom_point(aes(Color=Brand))+
labs(y="Att_1 Mean", title="Attitude Mean by Brand and Region")## Warning in geom_point(aes(Color = Brand)): Ignoring unknown aesthetics: Color
Targeting the Brand
In the next steps we targeted the specific brand, Chrysler, to get a deeper look into the data with a more focused vision. The first step was to create a brand and region table. With these results we were able to create a data visulization of the results with a graph showing the attitude mean for Chrysler
Chrysler_Att1_Mean<-brand_region_table %>%
filter(Brand=="Chrysler")
print(Chrysler_Att1_Mean)## Brand Region Att_1
## 1 Chrysler American 5.018519
## 2 Chrysler Asian 5.291667
## 3 Chrysler European 5.230769
## 4 Chrysler Middle Eastern 4.038462ggplot(Chrysler_Att1_Mean, aes(x=Region, y=Att_1, group=Brand))+
geom_line(aes(color=Brand))+
geom_point(aes(color=Brand))+
scale_y_continuous(limits=c(3,6))+
labs(y="Att_1 Mean", title="Attitude Mean for Chrysler by Region")
Comparison
Now that we have a more precise idea of the Chrysler data, it is time to compare to another company to get a more detailed picture of where we can better our outcomes. We picked Ford as it had similar data in some regions and very contrasting data in others. Once we compared attitudes of the competing brands, we were able to get a data visualization of the attitudes toward Chrsler vs. Ford across regions.
Multiple_Att1_Mean<-brand_region_table %>%
filter(Brand=="Chrysler"|Brand=="Ford")
print(Multiple_Att1_Mean)## Brand Region Att_1
## 1 Chrysler American 5.018519
## 2 Ford American 4.567901
## 3 Chrysler Asian 5.291667
## 4 Ford Asian 4.166667
## 5 Chrysler European 5.230769
## 6 Ford European 5.241379
## 7 Chrysler Middle Eastern 4.038462
## 8 Ford Middle Eastern 4.014706ggplot(Multiple_Att1_Mean, aes(x=Region, y=Att_1, group=Brand))+
geom_line(aes(color=Brand))+
geom_point(aes(color=Brand))+
scale_y_continuous(limits=c(3,6))+
labs(y="Att_1 Mean", title="Attitude Mean for Chrysler and Ford by Region")
Grouping by Age
To add another layer to the data, we combined demographics of age into 3 groups. There were age ranges of young adults, adults, and mature adults. The different groups determined by age, showed what stage of life individuals are in and which group is purchasing the most and the least. Once we had this data, we were able to see an age group analysis for Chrysler across regions. The graph shows the number of chrsyler cars by age group and region.
Car_Total$AgeGrp<-cut(Car_Total$Age, breaks = c(0,30,50,Inf),
lables=c("Young Adults", "Adults", "Mature Adults"),
right=FALSE)
names(Car_Total)## [1] "Resp" "Att_1" "Att_2" "Enj_1" "Enj_2"
## [6] "Perform_1" "Perform_2" "Perform_3" "WOM_1" "WOM_2"
## [11] "Futu_Pur_1" "Futu_Pur_2" "Valu_Percp_1" "Valu_Percp_2" "Pur_Proces_1"
## [16] "Pur_Proces_2" "Residence" "Pay_Meth" "Insur_Type" "Gender"
## [21] "Age" "Education" "X" "Region" "Brand"
## [26] "Model" "MPG" "Cyl" "acc1" "C_cost."
## [31] "H_Cost" "Post.Satis" "Att_Mean" "AgeGrp"head(Car_Total, n=5)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res10 6 6 4 4 4 4 1 5 6
## 3 Res100 6 7 7 3 5 6 6 3 5
## 4 Res1000 6 6 7 6 6 6 6 6 6
## 5 Res1001 6 6 7 6 6 6 6 4 4
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 6 6 6
## 3 6 6 7 6 5 5
## 4 6 6 4 6 6 3
## 5 4 6 5 6 6 7
## Residence Pay_Meth Insur_Type Gender Age Education X Region Brand
## 1 2 2 Collision Male 18 2 NaN European Ford
## 2 1 2 Collision Male 21 2 NaN European Ford
## 3 2 1 Collision Female 32 1 NaN American Toyota
## 4 2 3 Liability Female 24 2 NaN Asian Toyota
## 5 1 3 Liability Female 24 2 NaN Asian Toyota
## Model MPG Cyl acc1 C_cost. H_Cost Post.Satis Att_Mean AgeGrp
## 1 Expedition 15 8 6 16 14 4 6 [0,30)
## 2 Expedition 15 8 6 16 14 5 6 [0,30)
## 3 Rav4 24 4 8 10 8 4 6 [30,50)
## 4 Corolla 26 4 8 7 6 6 6 [0,30)
## 5 Corolla 26 4 8 7 6 5 6 [0,30)filtered_data_Chrysler<-Car_Total %>%
filter(Brand=="Chrysler")
ggplot(filtered_data_Chrysler, aes(x=Region, fill=AgeGrp))+
theme_bw()+
geom_bar(position = "dodge")+
labs(y="Number of Cars",title="Number of Chrysler Cars by Age Group and Region")
Post Purchase Satisfaction
We went a step further and took a look at the post purchase satisfaction data. We wanted to see the post purchase satisfaction of Chrsyler car purchases. To see this data, we created a boxplot graph.
ggplot(filtered_data_Chrysler, aes(x = Region, y = Post.Satis, fill = Region)) +
geom_boxplot() +
theme_bw()+
scale_y_continuous(limits=c(3,7))+
labs(title="Chrysler", x="Region", y="Post-Purchase Satisfaction")
#### The following graph shows the value perception each age group had
of the Chrysler brand by region.
Car_Total$Valu_Percp = (Car_Total$Valu_Percp_1 + Car_Total$Valu_Percp_2)/2
mean(Car_Total$Valu_Percp)## [1] 5.261678ggplot(filtered_data_Chrysler, aes(x=Valu_Percp_1, fill=AgeGrp))+
theme_bw()+
geom_bar(position="dodge")+
labs(y="Number of Cars",title="Value Perception of Chrysler Vehicles by Age Group")