library(readxl)
## Warning: package 'readxl' was built under R version 4.0.5
library(car)
## Warning: package 'car' was built under R version 4.0.5
## Loading required package: carData
car_sales <- read_excel("car_sales.xlsx")
car_sales$manufact <- as.factor(car_sales$manufact)
levels(car_sales$manufact)
## [1] "Acura" "Audi" "BMW" "Buick"
## [5] "Cadillac" "Chevrolet" "Chrysler" "Dodge"
## [9] "Ford" "Honda" "Hyundai" "Infiniti"
## [13] "Jaguar" "Jeep" "Lexus" "Lincoln"
## [17] "Mercedes-Benz" "Mercury" "Mitsubishi" "Nissan"
## [21] "Oldsmobile" "Plymouth" "Pontiac" "Porsche"
## [25] "Saab" "Saturn" "Subaru" "Toyota"
## [29] "Volkswagen" "Volvo"
car_sales$model <- as.factor(car_sales$model)
levels(car_sales$model)
## [1] "3000GT" "300M" "323i"
## [4] "328i" "44442" "44444"
## [7] "4Runner" "528i" "A4"
## [10] "A6" "A8" "Accent"
## [13] "Accord" "Alero" "Altima"
## [16] "Aurora" "Avalon" "Avenger"
## [19] "Beetle" "Bonneville" "Boxter"
## [22] "Bravada" "Breeze" "C-Class"
## [25] "C70" "Cabrio" "Camaro"
## [28] "Camry" "Caravan" "Carrera Cabriolet"
## [31] "Carrera Coupe" "Catera" "Cavalier"
## [34] "Celica" "Century" "Cherokee"
## [37] "Cirrus" "Civic" "CL"
## [40] "CL500" "CLK Coupe" "Concorde"
## [43] "Continental" "Contour" "Corolla"
## [46] "Corvette" "Cougar" "CR-V"
## [49] "Crown Victoria" "Cutlass" "Dakota"
## [52] "DeVille" "Diamante" "Durango"
## [55] "E-Class" "Eclipse" "Elantra"
## [58] "Eldorado" "ES300" "Escalade"
## [61] "Escort" "Expedition" "Explorer"
## [64] "F-Series" "Firebird" "Focus"
## [67] "Forester" "Frontier" "Galant"
## [70] "Golf" "Grand Am" "Grand Cherokee"
## [73] "Grand Marquis" "Grand Prix" "GS300"
## [76] "GS400" "GTI" "I30"
## [79] "Impala" "Integra" "Intrepid"
## [82] "Intrigue" "Jetta" "Land Cruiser"
## [85] "LeSabre" "LHS" "LS"
## [88] "LS400" "Lumina" "LW"
## [91] "LX470" "M-Class" "Malibu"
## [94] "Maxima" "Metro" "Mirage"
## [97] "Montana" "Monte Carlo" "Montero"
## [100] "Montero Sport" "Mountaineer" "Mustang"
## [103] "Mystique" "Navigator" "Neon"
## [106] "Odyssey" "Outback" "Park Avenue"
## [109] "Passat" "Passport" "Pathfinder"
## [112] "Prizm" "Prowler" "Quest"
## [115] "Ram Pickup" "Ram Van" "Ram Wagon"
## [118] "Ranger" "RAV4" "Regal"
## [121] "RL" "RX300" "S-Class"
## [124] "S-Type" "S40" "S70"
## [127] "S80" "Sable" "SC"
## [130] "Sebring Conv." "Sebring Coupe" "Sentra"
## [133] "Seville" "Sienna" "Silhouette"
## [136] "SL" "SL-Class" "SLK"
## [139] "SLK230" "Sonata" "Stratus"
## [142] "Sunfire" "SW" "Tacoma"
## [145] "Taurus" "TL" "Town & Country"
## [148] "Town car" "V40" "V70"
## [151] "Villager" "Viper" "Voyager"
## [154] "Windstar" "Wrangler" "Xterra"
car_sales$type <- as.factor(car_sales$type)
levels(car_sales$type)
## [1] "0" "1"
summary(car_sales)
## manufact model sales resale type
## Dodge : 11 Neon : 2 Min. : 0.11 Min. : 5.16 0:116
## Ford : 11 3000GT : 1 1st Qu.: 14.11 1st Qu.:11.26 1: 41
## Chevrolet : 9 300M : 1 Median : 29.45 Median :14.18
## Mercedes-Benz: 9 323i : 1 Mean : 53.00 Mean :18.07
## Toyota : 9 328i : 1 3rd Qu.: 67.96 3rd Qu.:19.88
## Chrysler : 7 44442 : 1 Max. :540.56 Max. :67.55
## (Other) :101 (Other):150 NA's :36
## price engine_s horsepow wheelbas
## Min. : 9.235 Min. :1.000 Min. : 55.0 Min. : 92.6
## 1st Qu.:18.017 1st Qu.:2.300 1st Qu.:149.5 1st Qu.:103.0
## Median :22.799 Median :3.000 Median :177.5 Median :107.0
## Mean :27.391 Mean :3.061 Mean :185.9 Mean :107.5
## 3rd Qu.:31.948 3rd Qu.:3.575 3rd Qu.:215.0 3rd Qu.:112.2
## Max. :85.500 Max. :8.000 Max. :450.0 Max. :138.7
## NA's :2 NA's :1 NA's :1 NA's :1
## width length curb_wgt fuel_cap
## Min. :62.60 Min. :149.4 Min. :1.895 Min. :10.30
## 1st Qu.:68.40 1st Qu.:177.6 1st Qu.:2.971 1st Qu.:15.80
## Median :70.55 Median :187.9 Median :3.342 Median :17.20
## Mean :71.15 Mean :187.3 Mean :3.378 Mean :17.95
## 3rd Qu.:73.42 3rd Qu.:196.1 3rd Qu.:3.800 3rd Qu.:19.57
## Max. :79.90 Max. :224.5 Max. :5.572 Max. :32.00
## NA's :1 NA's :1 NA's :2 NA's :1
## mpg
## Min. :15.00
## 1st Qu.:21.00
## Median :24.00
## Mean :23.84
## 3rd Qu.:26.00
## Max. :45.00
## NA's :3
library(mice)
## Warning: package 'mice' was built under R version 4.0.5
##
## Attaching package: 'mice'
## The following object is masked from 'package:stats':
##
## filter
## The following objects are masked from 'package:base':
##
## cbind, rbind
md.pattern(car_sales, plot = TRUE, rotate.names = FALSE)
## manufact model sales type engine_s horsepow wheelbas width length fuel_cap
## 117 1 1 1 1 1 1 1 1 1 1
## 35 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 0 0 0 0 0 0
## 0 0 0 0 1 1 1 1 1 1
## price curb_wgt mpg resale
## 117 1 1 1 1 0
## 35 1 1 1 0 1
## 1 1 1 0 1 1
## 1 1 1 0 0 2
## 1 1 0 1 1 1
## 1 0 1 1 1 1
## 1 0 0 0 1 9
## 2 2 3 36 49
car_sales1 <- car_sales[-2]
md.pattern(car_sales1, plot = TRUE, rotate.names = FALSE)
## manufact sales type engine_s horsepow wheelbas width length fuel_cap price
## 117 1 1 1 1 1 1 1 1 1 1
## 35 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 0
## 1 1 1 1 0 0 0 0 0 0 0
## 0 0 0 1 1 1 1 1 1 2
## curb_wgt mpg resale
## 117 1 1 1 0
## 35 1 1 0 1
## 1 1 0 1 1
## 1 1 0 0 2
## 1 0 1 1 1
## 1 1 1 1 1
## 1 0 0 1 9
## 2 3 36 49
methods(mice)
## Warning in .S3methods(generic.function, class, envir): function 'mice' appears
## not to be S3 generic; found functions that look like S3 methods
## [1] mice.impute.2l.bin mice.impute.2l.lmer mice.impute.2l.norm
## [4] mice.impute.2l.pan mice.impute.2lonly.mean mice.impute.2lonly.norm
## [7] mice.impute.2lonly.pmm mice.impute.cart mice.impute.jomoImpute
## [10] mice.impute.lda mice.impute.logreg mice.impute.logreg.boot
## [13] mice.impute.mean mice.impute.midastouch mice.impute.mnar.logreg
## [16] mice.impute.mnar.norm mice.impute.norm mice.impute.norm.boot
## [19] mice.impute.norm.nob mice.impute.norm.predict mice.impute.panImpute
## [22] mice.impute.passive mice.impute.pmm mice.impute.polr
## [25] mice.impute.polyreg mice.impute.quadratic mice.impute.rf
## [28] mice.impute.ri mice.impute.sample mice.mids
## [31] mice.theme
## see '?methods' for accessing help and source code
imputation = mice(car_sales1, m=10, method=c("","","pmm","","pmm","pmm","pmm","pmm","pmm","pmm","pmm","pmm","pmm"), maxit = 5)
##
## iter imp variable
## 1 1 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 2 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 3 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 4 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 5 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 6 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 7 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 8 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 9 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 1 10 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 1 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 2 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 3 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 4 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 5 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 6 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 7 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 8 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 9 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 2 10 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 1 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 2 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 3 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 4 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 5 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 6 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 7 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 8 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 9 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 3 10 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 1 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 2 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 3 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 4 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 5 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 6 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 7 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 8 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 9 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 4 10 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 1 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 2 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 3 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 4 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 5 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 6 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 7 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 8 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 9 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## 5 10 resale price engine_s horsepow wheelbas width length curb_wgt fuel_cap mpg
## Warning: Number of logged events: 50
imputation$imp
## $manufact
## [1] 1 2 3 4 5 6 7 8 9 10
## <0 rows> (or 0-length row.names)
##
## $sales
## [1] 1 2 3 4 5 6 7 8 9 10
## <0 rows> (or 0-length row.names)
##
## $resale
## 1 2 3 4 5 6 7 8 9 10
## 8 22.525 26.050 23.555 26.975 25.725 26.975 22.525 22.525 22.525 27.100
## 19 25.725 26.975 36.225 26.975 32.075 36.125 28.675 26.975 22.525 32.075
## 28 10.790 11.295 11.225 10.290 12.025 12.640 12.025 11.260 11.240 13.245
## 35 17.325 16.360 22.255 18.225 19.540 17.810 19.690 19.690 20.430 19.540
## 45 19.490 20.525 16.640 20.525 19.125 23.575 19.125 17.325 23.575 17.810
## 51 7.825 5.160 7.850 7.425 7.425 5.860 7.825 7.825 7.850 7.425
## 67 29.725 22.525 29.725 28.675 27.100 26.050 32.075 32.075 26.050 32.075
## 73 41.450 28.675 36.225 28.675 34.080 34.080 41.250 36.125 40.375 40.375
## 75 50.375 41.450 41.250 50.375 39.000 41.250 41.450 41.250 39.000 40.375
## 76 29.725 29.725 22.525 27.100 32.075 26.050 29.725 28.675 26.050 27.100
## 79 20.525 20.525 22.525 19.890 19.690 26.975 17.525 25.725 16.640 19.875
## 97 32.075 27.100 27.100 36.125 34.080 32.075 36.225 36.225 28.675 29.725
## 98 36.125 29.725 26.975 28.675 36.225 34.080 28.675 36.125 36.125 28.675
## 99 28.675 36.125 28.675 34.080 41.450 36.125 36.125 41.450 36.225 28.675
## 100 58.600 67.550 67.550 58.600 58.600 58.600 58.470 67.550 67.550 50.375
## 101 26.975 26.975 21.725 29.725 27.100 28.675 26.050 32.075 26.975 27.100
## 107 13.240 15.075 13.325 14.010 15.240 13.740 14.210 17.805 16.575 12.760
## 108 12.545 13.225 13.175 10.395 12.760 13.790 10.290 13.175 15.240 11.295
## 110 12.025 9.850 13.325 13.740 13.425 15.075 12.360 13.210 12.275 16.575
## 111 7.750 7.850 10.595 9.200 9.200 11.240 9.200 7.750 7.750 10.790
## 118 28.675 25.725 26.975 26.050 36.225 22.525 32.075 36.225 28.675 27.100
## 124 16.575 18.810 15.510 19.425 14.875 19.540 20.940 16.575 13.740 13.760
## 128 23.555 21.725 23.575 22.525 23.575 23.575 26.050 23.575 19.890 19.890
## 129 18.810 16.360 15.510 20.525 18.810 13.775 20.430 20.525 15.510 18.225
## 133 11.525 13.880 13.790 10.395 10.290 10.025 11.295 15.445 10.025 11.425
## 134 9.800 11.525 10.595 8.835 11.260 9.125 8.325 12.025 8.910 11.295
## 135 14.875 14.795 13.760 20.940 13.225 14.875 15.125 15.510 13.325 14.875
## 136 16.360 16.575 15.445 18.810 17.805 13.775 15.510 20.940 15.240 19.425
## 142 18.225 16.725 13.025 16.725 14.875 19.490 17.810 15.510 16.725 22.255
## 151 10.290 13.240 14.010 13.240 13.325 11.525 12.025 12.760 13.425 12.360
## 152 13.360 14.180 16.575 18.810 16.360 15.380 20.940 17.810 13.760 15.510
## 153 19.540 17.710 17.710 19.490 14.875 19.540 18.810 18.225 16.725 15.510
## 154 18.225 18.225 19.875 20.525 20.190 19.490 20.430 19.890 18.810 20.525
## 155 16.640 16.640 16.640 21.725 19.490 19.690 17.525 17.525 19.125 17.525
## 156 28.675 26.050 32.075 29.725 26.050 27.100 28.675 28.675 27.100 26.975
## 157 21.725 22.525 25.725 25.725 18.225 25.725 26.050 26.975 23.555 26.050
##
## $type
## [1] 1 2 3 4 5 6 7 8 9 10
## <0 rows> (or 0-length row.names)
##
## $price
## 1 2 3 4 5 6 7 8 9 10
## 3 25.345 25.345 31.807 24.997 26.31 26.399 28.340 31.930 25.450 28.80
## 34 31.930 25.345 27.560 25.345 33.95 24.400 29.299 22.288 29.185 25.45
##
## $engine_s
## 1 2 3 4 5 6 7 8 9 10
## 34 2.4 2.5 4.7 2.9 3 3 2.2 3.3 2.5 3.5
##
## $horsepow
## 1 2 3 4 5 6 7 8 9 10
## 34 160 175 230 170 193 170 161 205 175 210
##
## $wheelbas
## 1 2 3 4 5 6 7 8 9 10
## 34 114.5 106.5 117.5 114.2 111 112.2 108 106.4 107 112.2
##
## $width
## 1 2 3 4 5 6 7 8 9 10
## 34 69.3 67.9 79.1 72.1 73.1 70.3 70.3 69.6 71.4 72.2
##
## $length
## 1 2 3 4 5 6 7 8 9 10
## 34 193.5 174 212 194.8 192.9 185.8 178.2 190.2 181.2 185.4
##
## $curb_wgt
## 1 2 3 4 5 6 7 8 9 10
## 16 3.908 3.958 3.843 4.125 4.133 4.520 4.121 4.387 3.978 3.948
## 34 3.876 2.932 4.245 3.638 4.288 3.561 3.567 3.561 3.561 3.880
##
## $fuel_cap
## 1 2 3 4 5 6 7 8 9 10
## 34 17.5 16 25 17.5 20.9 18.5 20 18.5 19.8 23.7
##
## $mpg
## 1 2 3 4 5 6 7 8 9 10
## 34 21 22 16 21 18 22 22 18 23 19
## 39 19 24 22 23 22 23 22 24 21 26
## 110 23 24 24 25 26 25 23 22 25 24
car_sales2 = complete(imputation,6)
model1 <- lm(sales~., data = car_sales2)
vif(model1)
## GVIF Df GVIF^(1/(2*Df))
## manufact 510.111189 29 1.113484
## resale 44.315361 1 6.656978
## type 5.685071 1 2.384339
## price 31.366588 1 5.600588
## engine_s 16.105274 1 4.013138
## horsepow 11.807129 1 3.436150
## wheelbas 8.634535 1 2.938458
## width 5.446142 1 2.333697
## length 10.660976 1 3.265115
## curb_wgt 13.858025 1 3.722637
## fuel_cap 7.424258 1 2.724749
## mpg 7.588183 1 2.754666
anova(model1)
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## manufact 29 322541 11122 4.5547 2.367e-09 ***
## resale 1 13295 13295 5.4443 0.02135 *
## type 1 2715 2715 1.1120 0.29384
## price 1 1728 1728 0.7076 0.40196
## engine_s 1 11509 11509 4.7130 0.03197 *
## horsepow 1 3188 3188 1.3055 0.25556
## wheelbas 1 59994 59994 24.5686 2.470e-06 ***
## width 1 3945 3945 1.6154 0.20628
## length 1 2438 2438 0.9986 0.31973
## curb_wgt 1 11259 11259 4.6108 0.03385 *
## fuel_cap 1 4709 4709 1.9286 0.16757
## mpg 1 1383 1383 0.5665 0.45318
## Residuals 116 283263 2442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car_sales3 <- car_sales2[-3]
model2 <- lm(sales~., data = car_sales3)
vif(model2)
## GVIF Df GVIF^(1/(2*Df))
## manufact 173.843051 29 1.093008
## type 5.600459 1 2.366529
## price 9.389291 1 3.064195
## engine_s 13.525797 1 3.677743
## horsepow 11.785778 1 3.433042
## wheelbas 8.495348 1 2.914678
## width 5.395542 1 2.322831
## length 10.644132 1 3.262535
## curb_wgt 13.109593 1 3.620717
## fuel_cap 6.569842 1 2.563170
## mpg 7.396750 1 2.719697
anova(model2)
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## manufact 29 322541 11122 4.5643 2.106e-09 ***
## type 1 1268 1268 0.5204 0.47210
## price 1 10258 10258 4.2095 0.04243 *
## engine_s 1 6319 6319 2.5933 0.11001
## horsepow 1 2712 2712 1.1130 0.29360
## wheelbas 1 70992 70992 29.1335 3.585e-07 ***
## width 1 3174 3174 1.3027 0.25606
## length 1 1982 1982 0.8134 0.36898
## curb_wgt 1 9375 9375 3.8472 0.05220 .
## fuel_cap 1 7297 7297 2.9945 0.08619 .
## mpg 1 947 947 0.3885 0.53428
## Residuals 117 285103 2437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step.model2 <- step(model2, direction = "backward", trace = 0)
summary(step.model2)
##
## Call:
## lm(formula = sales ~ manufact + engine_s + horsepow + wheelbas +
## curb_wgt + fuel_cap, data = car_sales3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -136.171 -21.541 -1.164 15.252 247.236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -423.5998 83.6482 -5.064 1.47e-06 ***
## manufactAudi 16.6689 37.9920 0.439 0.66162
## manufactBMW -9.6708 37.2185 -0.260 0.79543
## manufactBuick 19.9438 35.0361 0.569 0.57024
## manufactCadillac 10.8292 33.7275 0.321 0.74870
## manufactChevrolet 34.1374 30.9404 1.103 0.27206
## manufactChrysler -3.8099 30.5094 -0.125 0.90083
## manufactDodge 34.4883 30.7892 1.120 0.26485
## manufactFord 127.6008 30.6013 4.170 5.73e-05 ***
## manufactHonda 93.6553 32.9228 2.845 0.00521 **
## manufactHyundai 28.1879 38.4330 0.733 0.46470
## manufactInfiniti 18.8499 54.8005 0.344 0.73146
## manufactJaguar -12.1587 55.1422 -0.220 0.82585
## manufactJeep 110.1995 40.9063 2.694 0.00806 **
## manufactLexus 29.9758 32.2676 0.929 0.35474
## manufactLincoln 6.5825 38.5688 0.171 0.86477
## manufactMercedes-Benz 22.6979 29.7138 0.764 0.44641
## manufactMercury -4.7427 32.8272 -0.144 0.88536
## manufactMitsubishi 25.6502 31.9233 0.803 0.42325
## manufactNissan 36.5628 31.2437 1.170 0.24418
## manufactOldsmobile -9.3022 32.2628 -0.288 0.77359
## manufactPlymouth -36.6988 34.8986 -1.052 0.29507
## manufactPontiac 23.4937 32.7363 0.718 0.47434
## manufactPorsche 84.1075 41.4809 2.028 0.04478 *
## manufactSaab 16.7040 42.9567 0.389 0.69806
## manufactSaturn -15.4567 34.3725 -0.450 0.65374
## manufactSubaru 48.4913 42.5633 1.139 0.25682
## manufactToyota 71.9888 30.2387 2.381 0.01883 *
## manufactVolkswagen 27.6145 32.8547 0.841 0.40227
## manufactVolvo 23.1805 32.3332 0.717 0.47479
## engine_s 22.2340 13.0882 1.699 0.09191 .
## horsepow -0.4253 0.2084 -2.041 0.04342 *
## wheelbas 5.7923 0.8889 6.516 1.70e-09 ***
## curb_wgt -26.1741 17.0890 -1.532 0.12820
## fuel_cap -4.4071 2.4547 -1.795 0.07507 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.55 on 122 degrees of freedom
## Multiple R-squared: 0.6017, Adjusted R-squared: 0.4907
## F-statistic: 5.421 on 34 and 122 DF, p-value: 1.987e-12
anova(step.model2)
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## manufact 29 322541 11122 4.7188 6.526e-10 ***
## engine_s 1 675 675 0.2864 0.593522
## horsepow 1 10806 10806 4.5847 0.034248 *
## wheelbas 1 67033 67033 28.4400 4.513e-07 ***
## curb_wgt 1 25761 25761 10.9295 0.001243 **
## fuel_cap 1 7598 7598 3.2234 0.075067 .
## Residuals 122 287554 2357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car_sales4 <- data.frame(car_sales3,car_sales$model)
model3 <- lm(sales~manufact+engine_s+horsepow+wheelbas+fuel_cap, data = car_sales4)
summary(model3)
##
## Call:
## lm(formula = sales ~ manufact + engine_s + horsepow + wheelbas +
## fuel_cap, data = car_sales4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -139.599 -18.812 -1.504 15.204 255.224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -429.8772 84.0036 -5.117 1.16e-06 ***
## manufactAudi 20.1029 38.1327 0.527 0.599018
## manufactBMW -6.2275 37.3532 -0.167 0.867865
## manufactBuick 23.4910 35.1502 0.668 0.505192
## manufactCadillac 5.8197 33.7517 0.172 0.863384
## manufactChevrolet 43.3119 30.5206 1.419 0.158398
## manufactChrysler -1.9075 30.6504 -0.062 0.950478
## manufactDodge 47.3414 29.7852 1.589 0.114531
## manufactFord 136.9068 30.1557 4.540 1.32e-05 ***
## manufactHonda 94.9986 33.0907 2.871 0.004821 **
## manufactHyundai 37.0094 38.2063 0.969 0.334610
## manufactInfiniti 23.9638 54.9972 0.436 0.663798
## manufactJaguar -14.0450 55.4292 -0.253 0.800393
## manufactJeep 121.1636 40.4948 2.992 0.003349 **
## manufactLexus 26.7646 32.3751 0.827 0.410006
## manufactLincoln 6.6066 38.7793 0.170 0.865004
## manufactMercedes-Benz 22.2982 29.8748 0.746 0.456857
## manufactMercury -0.3329 32.8792 -0.010 0.991938
## manufactMitsubishi 32.4197 31.7883 1.020 0.309796
## manufactNissan 40.6248 31.3008 1.298 0.196756
## manufactOldsmobile -5.7580 32.3553 -0.178 0.859047
## manufactPlymouth -30.3704 34.8422 -0.872 0.385095
## manufactPontiac 30.7552 32.5679 0.944 0.346847
## manufactPorsche 90.7112 41.4814 2.187 0.030650 *
## manufactSaab 20.2420 43.1286 0.469 0.639656
## manufactSaturn -9.2081 34.3158 -0.268 0.788892
## manufactSubaru 47.6416 42.7919 1.113 0.267737
## manufactToyota 78.0185 30.1449 2.588 0.010812 *
## manufactVolkswagen 30.6585 32.9735 0.930 0.354300
## manufactVolvo 23.9185 32.5060 0.736 0.463241
## engine_s 15.0822 12.2936 1.227 0.222231
## horsepow -0.3842 0.2078 -1.849 0.066831 .
## wheelbas 5.4981 0.8727 6.300 4.81e-09 ***
## fuel_cap -6.6944 1.9589 -3.417 0.000857 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.81 on 123 degrees of freedom
## Multiple R-squared: 0.594, Adjusted R-squared: 0.4851
## F-statistic: 5.454 on 33 and 123 DF, p-value: 2.191e-12
anova(model3)
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## manufact 29 322541 11122 4.6677 8.132e-10 ***
## engine_s 1 675 675 0.2833 0.5955176
## horsepow 1 10806 10806 4.5350 0.0352007 *
## wheelbas 1 67033 67033 28.1322 5.080e-07 ***
## fuel_cap 1 27829 27829 11.6792 0.0008574 ***
## Residuals 123 293084 2383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.5
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.0.5
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:car':
##
## recode
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
car_sales4 %>%
ggplot(aes(x=manufact, y=log(sales)))+
geom_point(aes(size=sales, col = manufact), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
ggplot(aes(x=log(sales), y=log(horsepow)))+
geom_point(aes(size=sales, col = manufact), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
ggplot(aes(x=log(sales), y=log(wheelbas)))+
geom_point(aes(size=sales, col = manufact), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
ggplot(aes(x=log(sales), y=log(fuel_cap)))+
geom_point(aes(size=sales, col = manufact), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
filter(manufact == "Ford" | manufact == "Honda" | manufact == "Jeep" | manufact == "Porsche" | manufact == "Toyota") %>%
ggplot(aes(x=car_sales.model, y=log(sales)))+
geom_point(aes(size=sales, col = car_sales.model), alpha=1)+
geom_smooth(method = lm)+
facet_wrap(~manufact)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
filter(manufact == "Ford" | manufact == "Honda" | manufact == "Jeep" | manufact == "Porsche" | manufact == "Toyota") %>%
ggplot(aes(x=log(sales), y=log(horsepow)))+
geom_point(aes(size=sales, col = car_sales.model), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
filter(manufact == "Ford" | manufact == "Honda" | manufact == "Jeep" | manufact == "Porsche" | manufact == "Toyota") %>%
ggplot(aes(x=log(sales), y=log(wheelbas)))+
geom_point(aes(size=sales, col = car_sales.model), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
car_sales4 %>%
filter(manufact == "Ford" | manufact == "Honda" | manufact == "Jeep" | manufact == "Porsche" | manufact == "Toyota") %>%
ggplot(aes(x=log(sales), y=log(fuel_cap)))+
geom_point(aes(size=sales, col = car_sales.model), alpha=1)+
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'
model4 <- lm(sales~manufact+car_sales.model, data = car_sales4)
summary(model4)
##
## Call:
## lm(formula = sales ~ manufact + car_sales.model, data = car_sales4)
##
## Residuals:
## ALL 157 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (28 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.384 NA NA NA
## manufactAudi -38.004 NA NA NA
## manufactBMW -21.857 NA NA NA
## manufactBuick -0.034 NA NA NA
## manufactCadillac -23.441 NA NA NA
## manufactChevrolet -7.085 NA NA NA
## manufactChrysler 14.096 NA NA NA
## manufactDodge 28.071 NA NA NA
## manufactFord 116.403 NA NA NA
## manufactHonda -26.529 NA NA NA
## manufactHyundai -9.934 NA NA NA
## manufactInfiniti -15.671 NA NA NA
## manufactJaguar -23.917 NA NA NA
## manufactJeep 16.173 NA NA NA
## manufactLexus 11.854 NA NA NA
## manufactLincoln 9.527 NA NA NA
## manufactMercedes-Benz -37.858 NA NA NA
## manufactMercury -19.004 NA NA NA
## manufactMitsubishi -39.274 NA NA NA
## manufactNissan 14.774 NA NA NA
## manufactOldsmobile -15.023 NA NA NA
## manufactPlymouth -15.229 NA NA NA
## manufactPontiac 12.261 NA NA NA
## manufactPorsche -38.104 NA NA NA
## manufactSaab -30.193 NA NA NA
## manufactSaturn -34.161 NA NA NA
## manufactSubaru 7.723 NA NA NA
## manufactToyota 44.703 NA NA NA
## manufactVolkswagen 11.718 NA NA NA
## manufactVolvo -21.853 NA NA NA
## car_sales.model300M -22.784 NA NA NA
## car_sales.model323i 2.220 NA NA NA
## car_sales.model328i -8.296 NA NA NA
## car_sales.model44442 2.924 NA NA NA
## car_sales.model44444 NA NA NA NA
## car_sales.model4Runner -15.676 NA NA NA
## car_sales.model528i NA NA NA NA
## car_sales.modelA4 19.017 NA NA NA
## car_sales.modelA6 17.400 NA NA NA
## car_sales.modelA8 NA NA NA NA
## car_sales.modelAccent 11.734 NA NA NA
## car_sales.modelAccord 218.047 NA NA NA
## car_sales.modelAlero 55.894 NA NA NA
## car_sales.modelAltima 33.936 NA NA NA
## car_sales.modelAurora -9.671 NA NA NA
## car_sales.modelAvalon -20.238 NA NA NA
## car_sales.modelAvenger -62.721 NA NA NA
## car_sales.modelBeetle -1.639 NA NA NA
## car_sales.modelBonneville -15.700 NA NA NA
## car_sales.modelBoxter 7.702 NA NA NA
## car_sales.modelBravada -4.344 NA NA NA
## car_sales.modelBreeze -18.915 NA NA NA
## car_sales.modelC-Class 16.866 NA NA NA
## car_sales.modelC70 -14.038 NA NA NA
## car_sales.modelCabrio -41.533 NA NA NA
## car_sales.modelCamaro -5.897 NA NA NA
## car_sales.modelCamry 163.907 NA NA NA
## car_sales.modelCaravan 114.294 NA NA NA
## car_sales.modelCarrera Cabriolet 0.586 NA NA NA
## car_sales.modelCarrera Coupe NA NA NA NA
## car_sales.modelCatera -4.758 NA NA NA
## car_sales.modelCavalier 113.220 NA NA NA
## car_sales.modelCelica -50.818 NA NA NA
## car_sales.modelCentury 52.211 NA NA NA
## car_sales.modelCherokee 24.999 NA NA NA
## car_sales.modelCirrus -21.174 NA NA NA
## car_sales.modelCivic 186.830 NA NA NA
## car_sales.modelCL -25.270 NA NA NA
## car_sales.modelCL500 -0.572 NA NA NA
## car_sales.modelCLK Coupe 10.066 NA NA NA
## car_sales.modelConcorde -22.332 NA NA NA
## car_sales.modelContinental -35.113 NA NA NA
## car_sales.modelContour -120.719 NA NA NA
## car_sales.modelCorolla 58.448 NA NA NA
## car_sales.modelCorvette -14.352 NA NA NA
## car_sales.modelCougar 6.149 NA NA NA
## car_sales.modelCR-V 60.348 NA NA NA
## car_sales.modelCrown Victoria -92.384 NA NA NA
## car_sales.modelCutlass -23.249 NA NA NA
## car_sales.modelDakota 43.858 NA NA NA
## car_sales.modelDeVille 47.786 NA NA NA
## car_sales.modelDiamante 5.601 NA NA NA
## car_sales.modelDurango 33.868 NA NA NA
## car_sales.modelE-Class 26.076 NA NA NA
## car_sales.modelEclipse 42.431 NA NA NA
## car_sales.modelElantra 37.242 NA NA NA
## car_sales.modelEldorado -9.407 NA NA NA
## car_sales.modelES300 -27.166 NA NA NA
## car_sales.modelEscalade -1.158 NA NA NA
## car_sales.modelEscort -85.560 NA NA NA
## car_sales.modelExpedition -30.449 NA NA NA
## car_sales.modelExplorer 120.960 NA NA NA
## car_sales.modelF-Series 384.774 NA NA NA
## car_sales.modelFirebird -31.734 NA NA NA
## car_sales.modelFocus 19.883 NA NA NA
## car_sales.modelForester -14.079 NA NA NA
## car_sales.modelFrontier 10.847 NA NA NA
## car_sales.modelGalant 55.506 NA NA NA
## car_sales.modelGolf -41.341 NA NA NA
## car_sales.modelGrand Am 79.452 NA NA NA
## car_sales.modelGrand Cherokee 101.483 NA NA NA
## car_sales.modelGrand Marquis 60.794 NA NA NA
## car_sales.modelGrand Prix 40.719 NA NA NA
## car_sales.modelGS300 -38.540 NA NA NA
## car_sales.modelGS400 -47.904 NA NA NA
## car_sales.modelGTI -45.506 NA NA NA
## car_sales.modelI30 NA NA NA NA
## car_sales.modelImpala 75.696 NA NA NA
## car_sales.modelIntegra -22.465 NA NA NA
## car_sales.modelIntrepid 20.573 NA NA NA
## car_sales.modelIntrigue 14.193 NA NA NA
## car_sales.modelJetta 32.619 NA NA NA
## car_sales.modelLand Cruiser -74.252 NA NA NA
## car_sales.modelLeSabre 43.907 NA NA NA
## car_sales.modelLHS -40.018 NA NA NA
## car_sales.modelLS 44.766 NA NA NA
## car_sales.modelLS400 -44.863 NA NA NA
## car_sales.modelLumina -7.670 NA NA NA
## car_sales.modelLW 3.249 NA NA NA
## car_sales.modelLX470 -42.112 NA NA NA
## car_sales.modelM-Class 27.450 NA NA NA
## car_sales.modelMalibu 102.827 NA NA NA
## car_sales.modelMaxima 25.695 NA NA NA
## car_sales.modelMetro -10.444 NA NA NA
## car_sales.modelMirage 26.122 NA NA NA
## car_sales.modelMontana -12.073 NA NA NA
## car_sales.modelMonte Carlo 10.294 NA NA NA
## car_sales.modelMontero 11.227 NA NA NA
## car_sales.modelMontero Sport 39.238 NA NA NA
## car_sales.modelMountaineer 7.229 NA NA NA
## car_sales.modelMustang -42.418 NA NA NA
## car_sales.modelMystique -6.029 NA NA NA
## car_sales.modelNavigator -25.986 NA NA NA
## car_sales.modelNeon 8.579 NA NA NA
## car_sales.modelOdyssey 63.174 NA NA NA
## car_sales.modelOutback NA NA NA NA
## car_sales.modelPark Avenue -11.499 NA NA NA
## car_sales.modelPassat NA NA NA NA
## car_sales.modelPassport NA NA NA NA
## car_sales.modelPathfinder -11.584 NA NA NA
## car_sales.modelPrizm NA NA NA NA
## car_sales.modelProwler -22.283 NA NA NA
## car_sales.modelQuest -26.850 NA NA NA
## car_sales.modelRam Pickup 159.606 NA NA NA
## car_sales.modelRam Van -36.417 NA NA NA
## car_sales.modelRam Wagon -50.688 NA NA NA
## car_sales.modelRanger 64.863 NA NA NA
## car_sales.modelRAV4 -58.981 NA NA NA
## car_sales.modelRegal NA NA NA NA
## car_sales.modelRL -30.796 NA NA NA
## car_sales.modelRX300 NA NA NA NA
## car_sales.modelS-Class 15.248 NA NA NA
## car_sales.modelS-Type NA NA NA NA
## car_sales.modelS40 -0.574 NA NA NA
## car_sales.modelS70 -2.286 NA NA NA
## car_sales.modelS80 1.438 NA NA NA
## car_sales.modelSable 47.576 NA NA NA
## car_sales.modelSC 19.323 NA NA NA
## car_sales.modelSebring Conv. -20.705 NA NA NA
## car_sales.modelSebring Coupe -45.626 NA NA NA
## car_sales.modelSentra -11.515 NA NA NA
## car_sales.modelSeville NA NA NA NA
## car_sales.modelSienna -18.968 NA NA NA
## car_sales.modelSilhouette NA NA NA NA
## car_sales.modelSL 75.397 NA NA NA
## car_sales.modelSL-Class 1.785 NA NA NA
## car_sales.modelSLK 6.472 NA NA NA
## car_sales.modelSLK230 NA NA NA NA
## car_sales.modelSonata NA NA NA NA
## car_sales.modelStratus 3.731 NA NA NA
## car_sales.modelSunfire NA NA NA NA
## car_sales.modelSW NA NA NA NA
## car_sales.modelTacoma NA NA NA NA
## car_sales.modelTaurus 90.028 NA NA NA
## car_sales.modelTL NA NA NA NA
## car_sales.modelTown & Country NA NA NA NA
## car_sales.modelTown car NA NA NA NA
## car_sales.modelV40 -13.986 NA NA NA
## car_sales.modelV70 NA NA NA NA
## car_sales.modelVillager NA NA NA NA
## car_sales.modelViper -66.539 NA NA NA
## car_sales.modelVoyager NA NA NA NA
## car_sales.modelWindstar NA NA NA NA
## car_sales.modelWrangler NA NA NA NA
## car_sales.modelXterra NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 156 and 0 DF, p-value: NA
anova(model4)
## Warning in anova.lm(model4): ANOVA F-tests on an essentially perfect fit are
## unreliable
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## manufact 29 322541 11122.1
## car_sales.model 127 399427 3145.1
## Residuals 0 0
model5 <- lm(sales~car_sales.model, data = car_sales4)
summary(model5)
##
## Call:
## lm(formula = sales ~ car_sales.model, data = car_sales4)
##
## Residuals:
## 1 2 3 4 5 6 7
## -3.159e-15 -1.659e-15 3.334e-15 2.190e-16 -1.604e-16 -1.051e-16 -1.231e-16
## 8 9 10 11 12 13 14
## -1.193e-16 -1.222e-16 -1.021e-16 2.913e-16 -1.172e-15 -1.152e-15 -4.370e-16
## 15 16 17 18 19 20 21
## 1.006e-15 5.323e-15 2.004e-16 -5.590e-17 -3.813e-16 -6.889e-17 -4.544e-17
## 22 23 24 25 26 27 28
## -3.064e-16 -3.494e-16 -3.071e-17 -7.654e-17 -9.322e-16 1.180e-15 -3.253e-16
## 29 30 31 32 33 34 35
## -1.543e-15 -3.979e-16 -3.844e-17 -4.193e-16 -1.530e-15 1.031e-15 -5.861e-17
## 36 37 38 39 40 41 42
## 2.165e+01 -6.115e-17 2.098e-15 -3.092e-15 -5.951e-15 1.737e-15 -6.775e-16
## 43 44 45 46 47 48 49
## 2.126e-15 5.437e-16 4.327e-16 -4.093e-17 -6.498e-16 1.487e-15 -1.177e-15
## 50 51 52 53 54 55 56
## 1.210e-15 -1.066e-15 -1.260e-15 2.106e-16 -1.954e-15 -3.722e-16 6.270e-16
## 57 58 59 60 61 62 63
## 9.323e-16 -8.996e-16 -5.347e-17 7.187e-17 -3.407e-14 -1.538e-15 -4.036e-17
## 64 65 66 67 68 69 70
## 4.327e-16 -4.591e-15 -1.260e-15 6.825e-16 -1.538e-15 -1.174e-13 -2.334e-16
## 71 72 73 74 75 76 77
## 9.963e-17 -2.334e-16 5.437e-16 -8.996e-16 1.820e-15 5.540e-15 5.437e-16
## 78 79 80 81 82 83 84
## -1.233e-15 6.547e-16 2.098e-15 9.963e-17 -3.074e-14 4.049e-16 1.198e-13
## 85 86 87 88 89 90 91
## 1.432e-15 -2.334e-16 3.772e-16 7.935e-16 1.043e-15 -2.890e-16 9.963e-17
## 92 93 94 95 96 97 98
## -1.224e-16 -6.081e-17 -2.890e-16 -1.177e-15 -2.121e-15 -1.566e-15 1.551e-16
## 99 100 101 102 103 104 105
## -9.466e-17 1.636e-17 2.106e-16 -1.011e-15 -2.048e-17 4.412e-17 2.069e-14
## 106 107 108 109 110 111 112
## 1.654e-15 1.551e-16 -1.502e-16 -2.612e-16 -2.565e-15 -3.751e-17 -6.755e-17
## 113 114 115 116 117 118 119
## -9.084e-17 7.760e-15 -2.165e+01 -6.300e-17 -6.775e-16 5.437e-16 -2.334e-16
## 120 121 122 123 124 125 126
## 1.265e-15 1.636e-17 7.102e-16 -6.085e-17 5.437e-16 -6.014e-17 -6.758e-17
## 127 128 129 130 131 132 133
## -4.070e-17 -7.392e-17 -7.500e-17 -4.555e-16 8.768e-16 1.765e-15 7.658e-16
## 134 135 136 137 138 139 140
## -1.140e-17 3.772e-16 7.187e-17 1.829e-16 -5.925e-17 -5.925e-17 -4.815e-17
## 141 142 143 144 145 146 147
## -2.334e-16 -6.775e-16 2.106e-16 -1.007e-16 1.551e-16 4.882e-16 8.213e-16
## 148 149 150 151 152 153 154
## 4.327e-16 -6.948e-17 3.217e-16 -6.134e-17 6.547e-16 4.327e-16 8.768e-16
## 155 156 157
## 2.209e-15 -6.202e-17 -2.334e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.110 30.618 0.004 0.9977
## car_sales.model300M 30.586 43.300 0.706 0.6085
## car_sales.model323i 19.637 43.300 0.454 0.7289
## car_sales.model328i 9.121 43.300 0.211 0.8678
## car_sales.model44442 12.005 43.300 0.277 0.8278
## car_sales.model44444 9.081 43.300 0.210 0.8684
## car_sales.model4Runner 68.301 43.300 1.577 0.3597
## car_sales.model528i 17.417 43.300 0.402 0.7565
## car_sales.modelA4 20.287 43.300 0.469 0.7211
## car_sales.modelA6 18.670 43.300 0.431 0.7408
## car_sales.modelA8 1.270 43.300 0.029 0.9813
## car_sales.modelAccent 41.074 43.300 0.949 0.5168
## car_sales.modelAccord 230.792 43.300 5.330 0.1181
## car_sales.modelAlero 80.145 43.300 1.851 0.3153
## car_sales.modelAltima 87.984 43.300 2.032 0.2911
## car_sales.modelAurora 14.580 43.300 0.337 0.7932
## car_sales.modelAvalon 63.739 43.300 1.472 0.3799
## car_sales.modelAvenger 4.624 43.300 0.107 0.9323
## car_sales.modelBeetle 49.353 43.300 1.140 0.4585
## car_sales.modelBonneville 35.835 43.300 0.828 0.5599
## car_sales.modelBoxter 8.872 43.300 0.205 0.8713
## car_sales.modelBravada 19.907 43.300 0.460 0.7257
## car_sales.modelBreeze 5.130 43.300 0.118 0.9249
## car_sales.modelC-Class 18.282 43.300 0.422 0.7457
## car_sales.modelC70 3.383 43.300 0.078 0.9504
## car_sales.modelCabrio 9.459 43.300 0.218 0.8631
## car_sales.modelCamaro 26.292 43.300 0.607 0.6526
## car_sales.modelCamry 247.884 43.300 5.725 0.1101
## car_sales.modelCaravan 181.639 43.300 4.195 0.1490
## car_sales.modelCarrera Cabriolet 1.756 43.300 0.041 0.9742
## car_sales.modelCarrera Coupe 1.170 43.300 0.027 0.9828
## car_sales.modelCatera 11.075 43.300 0.256 0.8406
## car_sales.modelCavalier 145.409 43.300 3.358 0.1843
## car_sales.modelCelica 33.159 43.300 0.766 0.5839
## car_sales.modelCentury 91.451 43.300 2.112 0.2815
## car_sales.modelCherokee 80.446 43.300 1.858 0.3143
## car_sales.modelCirrus 32.196 43.300 0.744 0.5930
## car_sales.modelCivic 199.575 43.300 4.609 0.1360
## car_sales.modelCL 14.004 43.300 0.323 0.8009
## car_sales.modelCL500 0.844 43.300 0.019 0.9876
## car_sales.modelCLK Coupe 11.482 43.300 0.265 0.8350
## car_sales.modelConcorde 31.038 43.300 0.717 0.6041
## car_sales.modelContinental 13.688 43.300 0.316 0.8051
## car_sales.modelContour 34.958 43.300 0.807 0.5676
## car_sales.modelCorolla 142.425 43.300 3.289 0.1879
## car_sales.modelCorvette 17.837 43.300 0.412 0.7512
## car_sales.modelCougar 26.419 43.300 0.610 0.6512
## car_sales.modelCR-V 73.093 43.300 1.688 0.3405
## car_sales.modelCrown Victoria 63.293 43.300 1.462 0.3820
## car_sales.modelCutlass 1.002 43.300 0.023 0.9853
## car_sales.modelDakota 111.203 43.300 2.568 0.2364
## car_sales.modelDeVille 63.619 43.300 1.469 0.3804
## car_sales.modelDiamante 5.601 43.300 0.129 0.9181
## car_sales.modelDurango 101.213 43.300 2.337 0.2574
## car_sales.modelE-Class 27.492 43.300 0.635 0.6399
## car_sales.modelEclipse 42.431 43.300 0.980 0.5065
## car_sales.modelElantra 66.582 43.300 1.538 0.3671
## car_sales.modelEldorado 6.426 43.300 0.148 0.9062
## car_sales.modelES300 23.962 43.300 0.553 0.6782
## car_sales.modelEscalade 14.675 43.300 0.339 0.7920
## car_sales.modelEscort 70.117 43.300 1.619 0.3522
## car_sales.modelExpedition 125.228 43.300 2.892 0.2119
## car_sales.modelExplorer 276.637 43.300 6.389 0.0988 .
## car_sales.modelF-Series 540.451 43.300 12.482 0.0509 .
## car_sales.modelFirebird 19.801 43.300 0.457 0.7269
## car_sales.modelFocus 175.560 43.300 4.055 0.1539
## car_sales.modelForester 32.918 43.300 0.760 0.5862
## car_sales.modelFrontier 64.895 43.300 1.499 0.3746
## car_sales.modelGalant 55.506 43.300 1.282 0.4218
## car_sales.modelGolf 9.651 43.300 0.223 0.8604
## car_sales.modelGrand Am 130.987 43.300 3.025 0.2032
## car_sales.modelGrand Cherokee 156.930 43.300 3.624 0.1714
## car_sales.modelGrand Marquis 81.064 43.300 1.872 0.3123
## car_sales.modelGrand Prix 92.254 43.300 2.131 0.2794
## car_sales.modelGS300 12.588 43.300 0.291 0.8199
## car_sales.modelGS400 3.224 43.300 0.074 0.9527
## car_sales.modelGTI 5.486 43.300 0.127 0.9198
## car_sales.modelI30 23.603 43.300 0.545 0.6823
## car_sales.modelImpala 107.885 43.300 2.492 0.2430
## car_sales.modelIntegra 16.809 43.300 0.388 0.7643
## car_sales.modelIntrepid 87.918 43.300 2.030 0.2913
## car_sales.modelIntrigue 38.444 43.300 0.888 0.5378
## car_sales.modelJetta 83.611 43.300 1.931 0.3042
## car_sales.modelLand Cruiser 9.725 43.300 0.225 0.8594
## car_sales.modelLeSabre 83.147 43.300 1.920 0.3057
## car_sales.modelLHS 13.352 43.300 0.308 0.8096
## car_sales.modelLS 49.879 43.300 1.152 0.4551
## car_sales.modelLS400 6.265 43.300 0.145 0.9085
## car_sales.modelLumina 24.519 43.300 0.566 0.6720
## car_sales.modelLW 8.362 43.300 0.193 0.8786
## car_sales.modelLX470 9.016 43.300 0.208 0.8693
## car_sales.modelM-Class 28.866 43.300 0.667 0.6257
## car_sales.modelMalibu 135.016 43.300 3.118 0.1976
## car_sales.modelMaxima 79.743 43.300 1.842 0.3167
## car_sales.modelMetro 21.745 43.300 0.502 0.7037
## car_sales.modelMirage 26.122 43.300 0.603 0.6544
## car_sales.modelMontana 39.462 43.300 0.911 0.5295
## car_sales.modelMonte Carlo 42.483 43.300 0.981 0.5061
## car_sales.modelMontero 11.227 43.300 0.259 0.8385
## car_sales.modelMontero Sport 39.238 43.300 0.906 0.5313
## car_sales.modelMountaineer 27.499 43.300 0.635 0.6398
## car_sales.modelMustang 113.259 43.300 2.616 0.2325
## car_sales.modelMystique 14.241 43.300 0.329 0.7977
## car_sales.modelNavigator 22.815 43.300 0.527 0.6913
## car_sales.modelNeon 54.274 37.499 1.447 0.3849
## car_sales.modelOdyssey 75.919 43.300 1.753 0.3300
## car_sales.modelOutback 46.997 43.300 1.085 0.4739
## car_sales.modelPark Avenue 27.741 43.300 0.641 0.6373
## car_sales.modelPassat 50.992 43.300 1.178 0.4482
## car_sales.modelPassport 12.745 43.300 0.294 0.8178
## car_sales.modelPathfinder 42.464 43.300 0.981 0.5062
## car_sales.modelPrizm 32.189 43.300 0.743 0.5930
## car_sales.modelProwler 1.762 43.300 0.041 0.9741
## car_sales.modelQuest 27.198 43.300 0.628 0.6430
## car_sales.modelRam Pickup 226.951 43.300 5.241 0.1200
## car_sales.modelRam Van 30.928 43.300 0.714 0.6051
## car_sales.modelRam Wagon 16.657 43.300 0.385 0.7662
## car_sales.modelRanger 220.540 43.300 5.093 0.1234
## car_sales.modelRAV4 24.996 43.300 0.577 0.6667
## car_sales.modelRegal 39.240 43.300 0.906 0.5313
## car_sales.modelRL 8.478 43.300 0.196 0.8769
## car_sales.modelRX300 51.128 43.300 1.181 0.4473
## car_sales.modelS-Class 16.664 43.300 0.385 0.7661
## car_sales.modelS-Type 15.357 43.300 0.355 0.7830
## car_sales.modelS40 16.847 43.300 0.389 0.7638
## car_sales.modelS70 15.135 43.300 0.350 0.7859
## car_sales.modelS80 18.859 43.300 0.436 0.7385
## car_sales.modelSable 67.846 43.300 1.567 0.3616
## car_sales.modelSC 24.436 43.300 0.564 0.6729
## car_sales.modelSebring Conv. 32.665 43.300 0.754 0.5886
## car_sales.modelSebring Coupe 7.744 43.300 0.179 0.8873
## car_sales.modelSentra 42.533 43.300 0.982 0.5057
## car_sales.modelSeville 15.833 43.300 0.366 0.7768
## car_sales.modelSienna 65.009 43.300 1.501 0.3741
## car_sales.modelSilhouette 24.251 43.300 0.560 0.6750
## car_sales.modelSL 80.510 43.300 1.859 0.3141
## car_sales.modelSL-Class 3.201 43.300 0.074 0.9530
## car_sales.modelSLK 7.888 43.300 0.182 0.8853
## car_sales.modelSLK230 1.416 43.300 0.033 0.9792
## car_sales.modelSonata 29.340 43.300 0.678 0.6209
## car_sales.modelStratus 71.076 43.300 1.641 0.3483
## car_sales.modelSunfire 51.535 43.300 1.190 0.4449
## car_sales.modelSW 5.113 43.300 0.118 0.9252
## car_sales.modelTacoma 83.977 43.300 1.939 0.3031
## car_sales.modelTaurus 245.705 43.300 5.674 0.1110
## car_sales.modelTL 39.274 43.300 0.907 0.5310
## car_sales.modelTown & Country 53.370 43.300 1.233 0.4339
## car_sales.modelTown car 48.801 43.300 1.127 0.4620
## car_sales.modelV40 3.435 43.300 0.079 0.9496
## car_sales.modelV70 17.421 43.300 0.402 0.7565
## car_sales.modelVillager 20.270 43.300 0.468 0.7213
## car_sales.modelViper 0.806 43.300 0.019 0.9882
## car_sales.modelVoyager 24.045 43.300 0.555 0.6773
## car_sales.modelWindstar 155.677 43.300 3.595 0.1727
## car_sales.modelWrangler 55.447 43.300 1.281 0.4221
## car_sales.modelXterra 54.048 43.300 1.248 0.4300
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.62 on 1 degrees of freedom
## Multiple R-squared: 0.9987, Adjusted R-squared: 0.7974
## F-statistic: 4.962 on 155 and 1 DF, p-value: 0.3459
anova(model5)
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## car_sales.model 155 721031 4651.8 4.9622 0.3459
## Residuals 1 937 937.4
model6 <- lm(sales~manufact, data = car_sales4)
summary(model6)
##
## Call:
## lm(formula = sales ~ manufact, data = car_sales4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -148.81 -19.00 -5.62 14.59 356.69
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.751 28.041 0.704 0.48249
## manufactAudi -6.232 42.833 -0.146 0.88455
## manufactBMW -4.250 42.833 -0.099 0.92113
## manufactBuick 40.754 39.655 1.028 0.30605
## manufactCadillac 2.684 37.620 0.071 0.94323
## manufactChevrolet 41.845 33.701 1.242 0.21665
## manufactChrysler 9.066 35.151 0.258 0.79689
## manufactDodge 62.990 32.744 1.924 0.05663 .
## manufactFord 164.125 32.744 5.012 1.76e-06 ***
## manufactHonda 98.784 37.620 2.626 0.00971 **
## manufactHyundai 26.024 42.833 0.608 0.54455
## manufactInfiniti 3.962 62.701 0.063 0.94972
## manufactJaguar -4.284 62.701 -0.068 0.94563
## manufactJeep 77.966 42.833 1.820 0.07107 .
## manufactLexus -1.944 36.200 -0.054 0.95726
## manufactLincoln 8.793 42.833 0.205 0.83767
## manufactMercedes-Benz -6.737 33.701 -0.200 0.84186
## manufactMercury 19.915 36.200 0.550 0.58319
## manufactMitsubishi 6.091 35.151 0.173 0.86271
## manufactNissan 37.339 35.151 1.062 0.29013
## manufactOldsmobile 10.080 36.200 0.278 0.78111
## manufactPlymouth -3.751 39.655 -0.095 0.92479
## manufactPontiac 42.004 36.200 1.160 0.24809
## manufactPorsche -15.709 42.833 -0.367 0.71442
## manufactSaab -9.098 48.568 -0.187 0.85170
## manufactSaturn 14.019 37.620 0.373 0.71004
## manufactSubaru 20.316 48.568 0.418 0.67643
## manufactToyota 62.494 33.701 1.854 0.06600 .
## manufactVolkswagen 15.117 36.200 0.418 0.67694
## manufactVolvo -7.128 36.200 -0.197 0.84422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.08 on 127 degrees of freedom
## Multiple R-squared: 0.4468, Adjusted R-squared: 0.3204
## F-statistic: 3.536 on 29 and 127 DF, p-value: 4.745e-07
anova(model6)
## Analysis of Variance Table
##
## Response: sales
## Df Sum Sq Mean Sq F value Pr(>F)
## manufact 29 322541 11122.1 3.5363 4.745e-07 ***
## Residuals 127 399427 3145.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1