Initial Analysis of the Capstone project
Sai Harshini
December 29, 2017
Initial Analysis of the Project
Dataset contains 9576 rows and 10 variables with essential meanings:
car: manufacturer brand price: seller’s price in advertisement (in USD) body: car body type mileage: as mentioned in advertisement (’000 Km) engV: rounded engine volume (’000 cubic cm) engType: type of fuel (“Other” in this case should be treated as NA) registration: whether car registered in Ukraine or not year: year of production model: specific model name drive: drive type
- To Read the dataset in R and visualize the length and breadth of your dataset.
Reading the dataset into R
car <- read.csv(paste("car_ad.csv", sep=""))
View(car)visualizing the length and breadth of your dataset
Length
dim(car)[1]## [1] 9576Breadth
dim(car)[2]## [1] 10str(car)## 'data.frame': 9576 obs. of 10 variables:
## $ car : Factor w/ 87 levels "Acura","Alfa Romeo",..: 28 53 53 53 53 60 35 65 53 53 ...
## $ price : num 15500 20500 35000 17800 33000 16600 6500 10500 21500 22700 ...
## $ body : Factor w/ 6 levels "crossover","hatch",..: 1 4 3 6 5 1 4 5 4 4 ...
## $ mileage : int 68 173 135 162 91 83 199 185 146 125 ...
## $ engV : num 2.5 1.8 5.5 1.8 NA 2 2 1.5 1.8 2.2 ...
## $ engType : Factor w/ 4 levels "Diesel","Gas",..: 2 2 4 1 3 4 4 1 2 1 ...
## $ registration: Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ year : int 2010 2011 2008 2012 2013 2013 2003 2011 2012 2010 ...
## $ model : Factor w/ 888 levels "1 Series","1.3",..: 504 337 273 213 337 861 182 559 337 337 ...
## $ drive : Factor w/ 4 levels "","front","full",..: 3 4 4 2 1 3 2 2 4 4 ...- To create a descriptive statistics (min, max, median etc) of each variable.
summary(car)## car price body mileage
## Volkswagen : 936 Min. : 0 crossover:2069 Min. : 0.0
## Mercedes-Benz: 921 1st Qu.: 4999 hatch :1252 1st Qu.: 70.0
## BMW : 694 Median : 9200 other : 838 Median :128.0
## Toyota : 541 Mean : 15633 sedan :3646 Mean :138.9
## VAZ : 489 3rd Qu.: 16700 vagon : 722 3rd Qu.:194.0
## Renault : 469 Max. :547800 van :1049 Max. :999.0
## (Other) :5526
## engV engType registration year
## Min. : 0.100 Diesel:3013 no : 561 Min. :1953
## 1st Qu.: 1.600 Gas :1722 yes:9015 1st Qu.:2004
## Median : 2.000 Other : 462 Median :2008
## Mean : 2.646 Petrol:4379 Mean :2007
## 3rd Qu.: 2.500 3rd Qu.:2012
## Max. :99.990 Max. :2016
## NA's :434
## model drive
## E-Class : 199 : 511
## A6 : 172 front:5188
## Camry : 134 full :2500
## Vito ïàññ.: 131 rear :1377
## Lanos : 127
## X5 : 119
## (Other) :8694library(psych)
describe(car)## vars n mean sd median trimmed mad min
## car* 1 9576 51.09 25.63 53.0 52.65 34.10 1.0
## price 2 9576 15633.32 24106.52 9200.0 10981.42 7709.52 0.0
## body* 3 9576 3.30 1.60 4.0 3.25 1.48 1.0
## mileage 4 9576 138.86 98.63 128.0 131.20 91.92 0.0
## engV 5 9142 2.65 5.93 2.0 2.10 0.74 0.1
## engType* 6 9576 2.65 1.33 3.0 2.69 1.48 1.0
## registration* 7 9576 1.94 0.23 2.0 2.00 0.00 1.0
## year 8 9576 2006.61 7.07 2008.0 2007.52 5.93 1953.0
## model* 9 9576 455.45 255.42 486.5 456.62 328.40 1.0
## drive* 10 9576 2.50 0.80 2.0 2.44 0.00 1.0
## max range skew kurtosis se
## car* 87.00 86.00 -0.42 -1.04 0.26
## price 547800.00 547800.00 7.13 93.67 246.34
## body* 6.00 5.00 -0.06 -1.05 0.02
## mileage 999.00 999.00 1.30 5.15 1.01
## engV 99.99 99.89 15.18 239.61 0.06
## engType* 4.00 3.00 -0.14 -1.76 0.01
## registration* 2.00 1.00 -3.76 12.13 0.00
## year 2016.00 63.00 -1.55 3.87 0.07
## model* 888.00 887.00 -0.03 -1.18 2.61
## drive* 4.00 3.00 0.54 -0.46 0.01Descriptive Statistics for price
summary(car$price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 4999 9200 15633 16700 547800Descriptive Statistics for mileage
summary(car$mileage)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 70.0 128.0 138.9 194.0 999.0Descriptive Statistics for engV
summary(car$mileage)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 70.0 128.0 138.9 194.0 999.03)To create one-way contingency tables for the categorical variables in the dataset.
one-way contingency table for car variable
table(car$car)##
## Acura Alfa Romeo Aro Aston Martin Audi
## 13 11 1 3 457
## Barkas Bentley BMW Bogdan Buick
## 1 16 694 2 1
## BYD Cadillac Changan Chery Chevrolet
## 7 3 1 53 246
## Chrysler Citroen Dacia Dadi Daewoo
## 28 108 12 2 235
## Daihatsu Dodge ËUAZ FAW Ferrari
## 3 16 6 1 2
## Fiat Fisker Ford GAZ Geely
## 119 1 350 34 56
## GMC Great Wall Groz Hafei Honda
## 3 9 2 1 206
## Huanghai Hummer Hyundai Infiniti Isuzu
## 1 5 367 64 2
## JAC Jaguar Jeep Kia Lamborghini
## 2 19 25 215 1
## Lancia Land Rover Lexus Lifan Lincoln
## 3 151 174 4 6
## Maserati Mazda Mercedes-Benz Mercury MG
## 1 198 921 1 4
## MINI Mitsubishi Moskvich-AZLK Moskvich-Izh Nissan
## 15 327 12 5 368
## Opel Other-Retro Peugeot Porsche Renault
## 400 1 182 92 469
## Rolls-Royce Rover Saab Samand Samsung
## 2 4 1 2 1
## Seat Skoda SMA Smart SsangYong
## 39 368 1 57 45
## Subaru Suzuki TATA Tesla Toyota
## 114 58 1 22 541
## UAZ VAZ Volkswagen Volvo Wartburg
## 21 489 936 32 1
## ZAZ ZX
## 102 1one-way contingency table for body column
table(car$body)##
## crossover hatch other sedan vagon van
## 2069 1252 838 3646 722 1049one-way contingency table for engType column
table(car$engType)##
## Diesel Gas Other Petrol
## 3013 1722 462 4379one-way contingency table for registration column
table(car$registration)##
## no yes
## 561 9015one-way contingency table for model column
table(car$model)##
## 1 Series 1.3 10
## 1 1 1
## 100 106 107
## 18 1 11
## 11 1102 Òàâðèÿ 1103 Ñëàâóòà
## 1 22 29
## 110557 1117 1118
## 9 2 11
## 1119 116 118
## 9 3 5
## 120 125 1301
## 1 1 1
## 1302 147 156
## 1 1 2
## 159 19 190
## 4 2 8
## 2 200 2008
## 3 7 4
## 205 206 207
## 1 10 11
## 208 21 210
## 5 10 3
## 2101 2102 2103
## 24 3 6
## 2104 2105 2106
## 13 19 32
## 2107 2108 2109
## 46 28 28
## 2109 (Áàëòèêà) 21093 21099
## 1 15 44
## 2110 2111 2112
## 39 18 13
## 2113 2114 2115
## 10 18 28
## 2117 2121 2123
## 1 45 1
## 2140 2141 2170
## 4 5 24
## 2171 2172 2190 Ãðàíòà
## 5 7 1
## 220 230 24
## 2 4 3
## 240 2410 25
## 1 3 3
## 250 2705 GAZåëü 2715
## 1 3 1
## 2717 2752 Ñîáîëü 3
## 3 2 47
## 300 300 C 300 M
## 1 9 1
## 3008 301 306
## 11 4 2
## 307 308 31029
## 15 18 1
## 3110 31105 3151
## 2 5 1
## 31512 31514 316
## 1 1 9
## 3163 318 320
## 1 29 61
## 3221 GAZåëü 32213 323
## 1 1 13
## 324 325 328
## 1 9 8
## 33 330 3302 GAZåëü
## 1 10 2
## 3303 335 340
## 1 2 1
## 350 350Z 3741
## 1 1 1
## 3962 4 Series Gran Coupe 401
## 1 1 1
## 403 405 406
## 1 2 2
## 407 412 428
## 14 1 1
## 452 ïàññ. 458 Italia 469
## 1 1 7
## 469Á 4Runner 5
## 2 1 5
## 5 Series 5 Series GT 500
## 3 3 2
## 500 L 5008 508
## 2 1 4
## 520 523 524
## 80 13 1
## 525 528 530
## 54 17 52
## 535 540 545
## 19 6 1
## 550 6 6 MPS
## 4 64 5
## 6 Series Gran Coupe 605 607
## 1 2 4
## 626 630 640
## 17 3 4
## 645 650 69
## 3 1 2
## 728 730 735
## 1 29 8
## 740 745 75
## 8 6 3
## 750 760 80
## 16 2 15
## 850 9 9-May
## 2 1 1
## 90 911 965
## 1 5 1
## 968 969 Âîëûíü 969Ì
## 3 1 3
## A 140 A 150 A 160
## 3 2 1
## A 170 A 180 A1
## 5 1 3
## A3 A4 A4 Allroad
## 11 58 4
## A5 A6 A6 Allroad
## 16 172 25
## A7 A8 Acadia
## 3 38 1
## Accent Accord Actyon
## 68 90 3
## Actyon Sports Agila Almera
## 1 1 17
## Alpina Altea Altea XL
## 1 1 1
## Altima Amarok Ampera
## 1 4 1
## Amulet Antara Armada
## 16 1 3
## Ascona Astra F Astra G
## 2 9 39
## Astra H Astra J Astro ïàññ.
## 30 10 2
## ASX Auris Aurora
## 7 30 1
## Avalon Avenger Avensis
## 2 2 29
## Aveo Aygo B-Class Electric Drive
## 80 3 5
## B-Max B 170 B 180
## 2 1 7
## B 200 B1000 BDD
## 3 1 1
## Beat Beetle Bentayga
## 1 2 4
## Berlingo ãðóç. Berlingo ïàññ. Besta
## 9 24 1
## Bipper ïàññ. Bluebird Bora
## 1 2 7
## Boxer ãðóç. Boxer ïàññ. Bravo
## 3 2 1
## C-Class C-Elysee C-Max
## 54 3 5
## C1 C3 C3 Picasso
## 5 5 5
## C30 C4 C4 Picasso
## 1 18 2
## C5 Cabrio Caddy
## 4 3 1
## Caddy ãðóç. Caddy ïàññ. Caliber
## 63 55 1
## Calibra California Camaro
## 2 1 9
## Camry Captiva Captur
## 134 11 2
## Caravelle Carens Carina
## 6 5 4
## Carisma Carnival Cayenne
## 7 4 61
## Cayman Ceed Cefiro
## 1 20 1
## Celica Century Cerato
## 2 1 24
## Challenger Cherokee Cinquecento
## 1 2 2
## City Civic CK
## 3 56 6
## CK-2 CK1 CL 180
## 6 2 1
## CL 500 CL 55 AMG CL 550
## 5 3 1
## CL 63 AMG CLA-Class CLA 200
## 3 1 3
## CLA 220 Clarus CLC 180
## 1 3 1
## CLC 200 Clio CLK 200
## 1 7 2
## CLK 220 CLK 230 CLK 240
## 1 2 1
## CLK 280 CLK 320 CLK 430
## 1 3 1
## CLS 350 CLS 400 CLS 500
## 6 1 2
## CLS 63 AMG Cobalt Colt
## 2 1 11
## Combo ãðóç. Combo ïàññ. Compass
## 14 21 1
## Continental Cooper Cooper S
## 9 10 1
## Cordoba Corolla Corolla Verso
## 6 54 1
## Corsa Corvette Countryman
## 10 2 2
## Coupe Courier CR-V
## 5 2 42
## Cross Touran CrossEastar Crossfire
## 1 1 1
## Crosstour Cruze CT
## 1 22 2
## Cuore CX-5 CX-7
## 1 5 26
## CX-9 D-Max Daimler
## 4 1 1
## DB9 Defender Discovery
## 1 2 7
## Discovery Sport Doblo ãðóç. Doblo ïàññ.
## 1 26 29
## Doblo Panorama Dokker ïàññ. DS3
## 2 2 1
## DS4 DS5 Ducato ãðóç.
## 3 2 6
## Ducato ïàññ. Durango Duster
## 2 1 8
## E-Class Eastar Eclipse
## 199 4 2
## Edge Êëàññè÷åñêèå EL
## 1 1 2
## Elantra Elara Elgrand
## 28 2 1
## Emgrand 7 (EC7) Emgrand 8 Emgrand X7
## 18 4 2
## Eos Epica ES 200
## 3 14 1
## ES 300 ES 330 ES 350
## 4 1 15
## Escalade Escort Escort van
## 2 16 1
## Espace Espero Evanda
## 4 3 6
## EX 35 EX 37 Expert ïàññ.
## 1 1 10
## Explorer Express ïàññ. F-150
## 2 4 1
## F-350 F3 F3R
## 1 4 1
## Fabia Felicia Fiesta
## 75 4 49
## Fiorino ãðóç. Fiorino ïàññ. FJ Cruiser
## 2 5 7
## Fluence Flying Spur Focus
## 10 2 62
## Focus Electric Forester Forfour
## 6 48 4
## Fortuner Fortwo Forza
## 1 47 7
## Freelander Frontera Fusion
## 10 3 12
## FX 30 FX 35 FX 37
## 3 25 7
## FX 45 G 320 G 350
## 2 1 6
## G 500 G 55 AMG G 63 AMG
## 11 5 6
## G25 G35 G37
## 4 3 2
## Galant Galaxy Gallardo
## 29 2 1
## Galloper GC6 Genesis
## 1 1 1
## Gentra Getz Giulietta
## 3 28 2
## GL 320 GL 350 GL 420
## 9 12 1
## GL 450 GL 500 GL 550
## 4 2 2
## GLC-Class GLE-Class GLK 220
## 3 18 2
## GLK 300 Gloria GLS 350
## 1 1 24
## GLS 400 GLS 500 GLS 63
## 3 5 6
## Golf GTI Golf II Golf III
## 2 12 19
## Golf IV Golf Plus Golf V
## 26 5 14
## Golf Variant Golf VI Golf VII
## 6 15 11
## Gran Move Granada Grand C4 Picasso
## 1 2 2
## Grand Cherokee Grand Marquis Grand Scenic
## 16 1 15
## Grand Vitara Grand Voyager Grande Punto
## 28 3 3
## Grandeur Grandis GranTurismo
## 4 4 1
## GS 250 GS 300 GS 350
## 1 16 8
## GT-R GX H 100 ïàññ.
## 5 8 1
## H 200 ãðóç. H 200 ïàññ. H1 ãðóç.
## 1 1 2
## H1 ïàññ. H2 H3
## 7 2 3
## Haval Hiace ïàññ. Highlander
## 1 3 14
## Hilux Hover HR-V
## 11 6 1
## i10 i20 I3
## 4 6 2
## i30 Ïàòðèîò Ibiza
## 29 4 11
## Ideal Ïðèîðà Ignis
## 1 1 1
## Impreza Impreza WRX STI Insight
## 12 4 3
## Insignia Intrepid IQ
## 13 1 1
## IS 200 IS 250 IS 300
## 3 3 4
## IX35 ix55 (Veracruz) J2
## 30 4 1
## Jaggi Jazz Jetta
## 3 2 49
## Jimny Juke Juke Nismo
## 2 26 1
## Jumper ãðóç. Jumpy ãðóç. Jumpy ïàññ.
## 2 5 8
## KA Kadett Kangoo ãðóç.
## 1 15 72
## Kangoo ïàññ. Karma Kimo
## 74 1 3
## Kizashi Koleos Korando
## 1 5 10
## Koup Kubistar Kuga
## 3 1 31
## Kyron L 200 L 400 ïàññ.
## 9 12 1
## Lacetti Laguna Lancer
## 64 29 54
## Lancer Evolution Lancer X Lancer X Sportback
## 4 52 1
## Land Cruiser 100 Land Cruiser 105 Land Cruiser 200
## 16 1 48
## Land Cruiser 76 Land Cruiser 80 Land Cruiser Prado
## 1 5 76
## LandMark Lanos Latitude
## 1 127 1
## Leaf Legacy Leganza
## 59 18 2
## Legend Leon LHS
## 2 14 1
## Linea Lite Ace Logan
## 3 1 23
## LS 400 LS 430 LS 460
## 1 1 14
## LT ïàññ. Lumina Lupo
## 4 2 2
## LX 450 LX 470 LX 570
## 7 10 18
## M11 M35 M37
## 2 1 1
## M5 M6 Macan
## 10 1 2
## Magentis Malibu Manta
## 25 2 1
## Maple C81 Mark II Master ãðóç.
## 1 1 8
## Master ïàññ. Matiz Matrix
## 2 35 6
## Maxima MB ãðóç. MDX
## 18 1 5
## Megane Micra MK
## 88 16 11
## MK-2 MK Cross MKX
## 2 4 1
## ML 250 ML 270 ML 280
## 3 3 1
## ML 320 ML 350 ML 400
## 11 22 3
## ML 430 ML 500 ML 550
## 1 2 1
## ML 63 AMG Model S Model X
## 5 19 3
## Modus Mohave Mondeo
## 1 1 38
## MPV Mulsanne Multivan
## 1 1 21
## Murano Mustang Mustang GT
## 14 8 2
## Navara Navigator Nemo ãðóç.
## 2 1 3
## Nemo ïàññ. Neon New Beetle
## 3 1 2
## Nexia Nitro Niva
## 25 1 15
## Note Nubira NV
## 19 12 2
## NX 200 NX 300 Òàâðèÿ-Íîâà
## 2 1 2
## Octavia Octavia A5 Octavia A7
## 32 108 25
## Octavia Scout Octavia Tour Omega
## 5 42 50
## One Opirus Optima
## 1 1 1
## Orion Outback Outlander
## 2 16 29
## Outlander XL Paceman Pacifica
## 24 1 1
## Pajero Pajero Pinin Pajero Sport
## 6 1 28
## Pajero Wagon Panamera Panda
## 48 23 3
## Partner ãðóç. Partner ïàññ. Passat B2
## 19 22 3
## Passat B3 Passat B4 Passat B5
## 17 15 58
## Passat B6 Passat B7 Passat B8
## 66 51 5
## Passat CC Pathfinder Patriot
## 22 9 1
## Patrol Phaeton Phantom
## 15 7 2
## Phedra Picanto Pilot
## 1 4 5
## Pointer Polarsun Business Van Polo
## 1 1 60
## Pony Prelude Premacy
## 1 2 1
## Previa Primastar ãðóç. Primastar ïàññ.
## 1 2 2
## Primera Prisma Prius
## 27 1 4
## Pro Ceed Probe PT Cruiser
## 1 1 5
## Punto Q3 Q5
## 4 5 14
## Q50 Q7 Q70
## 1 59 1
## Qashqai Qashqai+2 QQ
## 49 1 13
## Qubo ïàññ. QX50 QX70
## 2 2 9
## QX80 R 320 R8
## 1 1 4
## RAM Ram Van Range Rover
## 7 1 68
## Range Rover Evoque Range Rover Sport Ranger
## 8 56 5
## Rapid Rapide Rav 4
## 6 2 54
## RCZ Rekord Rexton
## 3 1 4
## Rexton II Rexton W Rio
## 14 3 31
## RL Roadster Rodius
## 2 1 1
## Roomster RX-8 RX 200
## 2 6 2
## RX 270 RX 300 RX 330
## 3 11 6
## RX 350 RX 400 RX 450
## 27 2 1
## S-Guard S-Type S 140
## 2 2 5
## S 250 S 280 S 300
## 1 2 1
## S 320 S 350 S 400
## 12 41 8
## S 420 S 430 S 500
## 1 2 51
## S 55 S 550 S 600
## 1 13 7
## S 63 AMG S 65 AMG S2000
## 6 2 1
## S4 S40 S5
## 2 3 2
## S6 S60 S8
## 2 3 3
## S80 Safari Safe
## 6 1 1
## Saibao Samurai Sandero
## 1 1 6
## Sandero StepWay Santa FE Savana
## 1 44 1
## SC 430 Scenic Scion
## 2 23 1
## Scirocco Scorpio Scudo ãðóç.
## 6 16 3
## Scudo ïàññ. Sebring Sens
## 12 5 42
## Sentra Sephia Sequoia
## 4 3 1
## Sharan Shuma Shuttle
## 11 1 2
## Sienna Sierra SL 500 (550)
## 2 12 1
## SL 55 AMG SLK 200 SLK 350
## 1 2 1
## SM5 Smart Solaris
## 1 1 1
## Solenza Sonata Sorento
## 2 26 43
## Soul Space Star Space Wagon
## 7 5 1
## Spaceback Splash Sportage
## 2 1 37
## Sprinter 208 ïàññ. Sprinter 210 ãðóç. Sprinter 211 ïàññ.
## 2 1 1
## Sprinter 212 ïàññ. Sprinter 213 ïàññ. Sprinter 310 ïàññ.
## 3 2 1
## Sprinter 311 ïàññ. Sprinter 312 ãðóç. Sprinter 312 ïàññ.
## 1 2 4
## Sprinter 313 ãðóç. Sprinter 313 ïàññ. Sprinter 315 ïàññ.
## 6 10 1
## Sprinter 316 ãðóç. Sprinter 316 ïàññ. Sprinter 318 ïàññ.
## 1 2 2
## Sprinter 319 ãðóç. Sprinter 319 ïàññ. Sprinter 324 ïàññ.
## 1 1 1
## Sprinter ãðóç. SRX Stanza
## 1 1 1
## Stilo Stratus Sunny
## 1 1 5
## Superb SuperNova Swift
## 61 1 10
## SX4 Symbol Syncro
## 8 11 1
## T2 (Transporter) T3 (Transporter) T4 (Transporter) ãðóç
## 1 2 8
## T4 (Transporter) ïàññ. T5 (Transporter) ãðóç T5 (Transporter) ïàññ.
## 45 40 61
## T6 (Transporter) ãðóç T6 (Transporter) ïàññ. Tacoma
## 8 5 3
## Tacuma Taurus Teana
## 5 1 12
## Tempra Terios Terracan
## 1 1 1
## Terrano Thema Thunderbird
## 2 1 1
## Tiggo Tigra Tiguan
## 8 1 17
## TIIDA Tipo TL
## 14 4 4
## TLX Toledo Touareg
## 1 6 69
## Touran Tourneo Connect ïàññ. Tourneo Courier
## 19 4 1
## Town Car Trafic ãðóç. Trafic ïàññ.
## 4 36 41
## Transit ãðóç. Transit Connect ãðóç. Transit Connect ïàññ.
## 19 9 13
## Transit Custom Transit ïàññ. Tribeca
## 1 21 15
## Tribute Trooper TT
## 1 1 5
## Tucson Tundra Uno
## 69 5 3
## Up V 250 V40
## 2 13 3
## V5 Vaneo Vanette ïàññ.
## 1 1 1
## Vectra A Vectra B Vectra C
## 19 46 45
## Vento Venza Viano ïàññ.
## 6 8 7
## Vida Virage Vista
## 15 1 1
## Vitara Vito ãðóç. Vito ïàññ.
## 6 40 131
## Vivaro ãðóç. Vivaro ïàññ. Volt
## 27 34 7
## Voyager Wrangler X-Trail
## 1 5 34
## X-Type X1 X3
## 3 11 12
## X5 X5 M X6
## 119 14 41
## X6 M Xantia XC60
## 5 1 4
## XC70 XC90 XE
## 4 5 2
## Xedos 6 Xedos 9 Xenon
## 4 2 1
## XF XJR-S XKR
## 9 1 1
## Xsara Xsara Picasso XV
## 1 2 1
## Yaris Yeti Z3
## 14 6 1
## Z4 Zafira ZDX
## 5 5 1one-way contingency table for drive column
table(car$drive)##
## front full rear
## 511 5188 2500 1377- To create two-way contingency tables for the categorical variables in the dataset
table(car$car, car$drive)##
## front full rear
## Acura 0 5 8 0
## Alfa Romeo 0 10 0 1
## Aro 0 0 1 0
## Aston Martin 0 0 0 3
## Audi 26 211 218 2
## Barkas 1 0 0 0
## Bentley 0 0 15 1
## BMW 36 7 227 424
## Bogdan 0 2 0 0
## Buick 0 1 0 0
## BYD 0 7 0 0
## Cadillac 0 0 3 0
## Changan 1 0 0 0
## Chery 2 51 0 0
## Chevrolet 8 194 33 11
## Chrysler 4 15 2 7
## Citroen 8 100 0 0
## Dacia 0 11 1 0
## Dadi 0 0 1 1
## Daewoo 9 224 1 1
## Daihatsu 0 2 1 0
## Dodge 1 6 8 1
## ËUAZ 1 0 5 0
## FAW 0 1 0 0
## Ferrari 0 0 0 2
## Fiat 6 112 0 1
## Fisker 1 0 0 0
## Ford 18 243 35 54
## GAZ 10 2 1 21
## Geely 4 51 1 0
## GMC 0 0 3 0
## Great Wall 1 1 7 0
## Groz 0 0 0 2
## Hafei 0 1 0 0
## Honda 9 145 50 2
## Huanghai 0 0 1 0
## Hummer 1 0 4 0
## Hyundai 23 203 129 12
## Infiniti 4 0 49 11
## Isuzu 1 0 1 0
## JAC 0 2 0 0
## Jaguar 1 0 3 15
## Jeep 0 0 25 0
## Kia 11 140 63 1
## Lamborghini 0 0 1 0
## Lancia 1 2 0 0
## Land Rover 2 0 149 0
## Lexus 3 26 112 33
## Lifan 2 2 0 0
## Lincoln 0 0 2 4
## Maserati 0 0 0 1
## Mazda 12 145 36 5
## Mercedes-Benz 61 90 291 479
## Mercury 0 0 0 1
## MG 0 4 0 0
## MINI 0 12 2 1
## Mitsubishi 20 159 147 1
## Moskvich-AZLK 2 5 0 5
## Moskvich-Izh 2 0 0 3
## Nissan 26 235 102 5
## Opel 20 324 6 50
## Other-Retro 1 0 0 0
## Peugeot 10 170 2 0
## Porsche 1 0 83 8
## Renault 15 436 16 2
## Rolls-Royce 0 0 0 2
## Rover 0 3 1 0
## Saab 0 1 0 0
## Samand 0 2 0 0
## Samsung 0 1 0 0
## Seat 2 37 0 0
## Skoda 19 337 12 0
## SMA 0 1 0 0
## Smart 9 2 0 46
## SsangYong 1 7 37 0
## Subaru 2 3 109 0
## Suzuki 3 20 35 0
## TATA 0 0 1 0
## Tesla 0 3 8 11
## Toyota 14 274 244 9
## UAZ 2 0 19 0
## VAZ 30 286 45 128
## Volkswagen 57 744 130 5
## Volvo 1 17 12 2
## Wartburg 0 1 0 0
## ZAZ 6 92 1 3
## ZX 0 0 1 0table(car$car, car$engType)##
## Diesel Gas Other Petrol
## Acura 0 5 1 7
## Alfa Romeo 1 1 0 9
## Aro 0 0 0 1
## Aston Martin 0 0 0 3
## Audi 214 53 23 167
## Barkas 0 0 0 1
## Bentley 0 0 0 16
## BMW 267 74 21 332
## Bogdan 0 1 0 1
## Buick 0 0 0 1
## BYD 0 3 0 4
## Cadillac 0 0 0 3
## Changan 0 0 1 0
## Chery 0 14 3 36
## Chevrolet 3 91 18 134
## Chrysler 3 7 2 16
## Citroen 64 10 1 33
## Dacia 1 2 1 8
## Dadi 0 0 0 2
## Daewoo 0 102 5 128
## Daihatsu 0 0 0 3
## Dodge 4 4 1 7
## ËUAZ 0 1 0 5
## FAW 0 0 0 1
## Ferrari 0 0 0 2
## Fiat 73 14 3 29
## Fisker 0 0 1 0
## Ford 110 55 19 166
## GAZ 0 14 5 15
## Geely 0 29 1 26
## GMC 0 0 0 3
## Great Wall 0 5 0 4
## Groz 0 1 0 1
## Hafei 0 0 0 1
## Honda 6 48 11 141
## Huanghai 0 0 0 1
## Hummer 0 4 1 0
## Hyundai 89 73 14 191
## Infiniti 5 19 2 38
## Isuzu 2 0 0 0
## JAC 0 1 0 1
## Jaguar 1 0 0 18
## Jeep 11 3 1 10
## Kia 65 49 5 96
## Lamborghini 0 0 0 1
## Lancia 2 0 1 0
## Land Rover 85 9 4 53
## Lexus 10 42 13 109
## Lifan 0 0 0 4
## Lincoln 0 3 0 3
## Maserati 0 0 0 1
## Mazda 8 38 13 139
## Mercedes-Benz 509 98 32 282
## Mercury 0 1 0 0
## MG 0 0 0 4
## MINI 2 0 0 13
## Mitsubishi 51 114 12 150
## Moskvich-AZLK 0 2 1 9
## Moskvich-Izh 0 3 0 2
## Nissan 52 59 71 186
## Opel 149 87 13 151
## Other-Retro 1 0 0 0
## Peugeot 85 23 5 69
## Porsche 20 6 4 62
## Renault 355 24 19 71
## Rolls-Royce 0 0 1 1
## Rover 1 0 0 3
## Saab 0 0 0 1
## Samand 0 2 0 0
## Samsung 0 0 0 1
## Seat 8 5 2 24
## Skoda 60 53 10 245
## SMA 0 0 0 1
## Smart 4 1 4 48
## SsangYong 43 1 1 0
## Subaru 10 17 6 81
## Suzuki 0 12 2 44
## TATA 1 0 0 0
## Tesla 0 0 22 0
## Toyota 94 163 18 266
## UAZ 0 6 6 9
## VAZ 1 143 28 317
## Volkswagen 529 87 29 291
## Volvo 14 2 2 14
## Wartburg 0 0 0 1
## ZAZ 0 38 3 61
## ZX 0 0 0 1table(car$car, car$registration)##
## no yes
## Acura 0 13
## Alfa Romeo 2 9
## Aro 0 1
## Aston Martin 0 3
## Audi 104 353
## Barkas 0 1
## Bentley 0 16
## BMW 109 585
## Bogdan 0 2
## Buick 0 1
## BYD 0 7
## Cadillac 0 3
## Changan 0 1
## Chery 0 53
## Chevrolet 2 244
## Chrysler 4 24
## Citroen 3 105
## Dacia 0 12
## Dadi 0 2
## Daewoo 3 232
## Daihatsu 0 3
## Dodge 0 16
## ËUAZ 0 6
## FAW 0 1
## Ferrari 0 2
## Fiat 2 117
## Fisker 0 1
## Ford 11 339
## GAZ 0 34
## Geely 0 56
## GMC 0 3
## Great Wall 0 9
## Groz 0 2
## Hafei 0 1
## Honda 5 201
## Huanghai 0 1
## Hummer 0 5
## Hyundai 5 362
## Infiniti 0 64
## Isuzu 0 2
## JAC 0 2
## Jaguar 1 18
## Jeep 3 22
## Kia 2 213
## Lamborghini 0 1
## Lancia 1 2
## Land Rover 10 141
## Lexus 5 169
## Lifan 0 4
## Lincoln 0 6
## Maserati 0 1
## Mazda 8 190
## Mercedes-Benz 69 852
## Mercury 0 1
## MG 0 4
## MINI 0 15
## Mitsubishi 11 316
## Moskvich-AZLK 0 12
## Moskvich-Izh 0 5
## Nissan 12 356
## Opel 53 347
## Other-Retro 0 1
## Peugeot 6 176
## Porsche 1 91
## Renault 27 442
## Rolls-Royce 0 2
## Rover 1 3
## Saab 0 1
## Samand 0 2
## Samsung 0 1
## Seat 1 38
## Skoda 19 349
## SMA 0 1
## Smart 2 55
## SsangYong 0 45
## Subaru 0 114
## Suzuki 0 58
## TATA 0 1
## Tesla 0 22
## Toyota 4 537
## UAZ 0 21
## VAZ 0 489
## Volkswagen 74 862
## Volvo 1 31
## Wartburg 0 1
## ZAZ 0 102
## ZX 0 1table(car$body, car$drive)##
## front full rear
## crossover 71 140 1851 7
## hatch 57 1137 24 34
## other 86 349 169 234
## sedan 202 2271 301 872
## vagon 33 509 103 77
## van 62 782 52 153table(car$body, car$registration)##
## no yes
## crossover 59 2010
## hatch 52 1200
## other 33 805
## sedan 246 3400
## vagon 136 586
## van 35 1014table(car$engType, car$drive)##
## front full rear
## Diesel 132 1485 932 464
## Gas 66 975 460 221
## Other 115 228 64 55
## Petrol 198 2500 1044 6375)To draw a boxplot of the variables that belong to the study.
For price
boxplot(car$price, data=car, xlab="Salary", main="Boxplot of Salary", horizontal=TRUE,col = "blue")
For mileage
boxplot(car$mileage, data=car, xlab="Mileage", main="Boxplot of Mileage", horizontal=TRUE , col = "yellow")
For engV
boxplot(car$engV, data=car, xlab="Engine Volume", main="Boxplot of Engine Volume", horizontal=TRUE,col = "red")
6)To draw Histograms for suitable data fields.
library(lattice)
histogram(~car, data = car,main = "Distribution of Manufacturing Brand of the cars", xlab="Car Brand", ylab = "Number of cars", col='blue' ) 
histogram(~price, data = car,main = "Distribution of Price of cars", xlab="Price", ylab = "Number of cars",col='yellow' ) 
histogram(~body, data = car,main = "Distribution of body type of the cars", xlab="Body type", ylab="Number of cars",col='green' ) 
histogram(~mileage, data = car,main = "Distribution of Mileage", xlab="Mileage", ylab="Number of cars",col='red') 
histogram(~engV, data = car,main = "Distribution of Engine Volume", xlab="Engine Volume",ylab="Number of cars", col='purple' ) 
histogram(~year, data = car,main = "Distribution of year of Production", xlab="Year",ylab="Number of cars", col='orange' ) 
- To draw suitable plot for the data fields.
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(car$mileage, car$price,main="Scatterplot of Price vs Mileage",
xlab="Mileage", ylab="Price")
scatterplot(car$engV, car$price,main="Scatterplot of Price vs Engine Volume",
xlab="Engine Volume", ylab="Price")
plot(car$year, car$price,main="Scatterplot of Price vs Year",
xlab="Year", ylab="Price")
- To create a correlation matrix.
cardata <- car[ , c(2,4,5,8)] #correlation matrix of numeric type
cor(cardata)## price mileage engV year
## price 1.0000000 -0.3124151 NA 0.3703792
## mileage -0.3124151 1.0000000 NA -0.4955992
## engV NA NA 1 NA
## year 0.3703792 -0.4955992 NA 1.0000000cor(car$price,car$mileage)## [1] -0.3124151cor(car$price,car$year)## [1] 0.37037929)To Visualize your correlation matrix using corrgram
library(corrgram)
corrgram(car, order=FALSE, lower.panel=panel.shade, upper.panel=panel.pie, text.panel=panel.txt, main="Corrgram of car Variables")
- To create a scatter plot matrix for the data set
library(car)
scatterplot.matrix(~price+mileage+engV++year, data=car,main="Price versus other variables")## Warning: 'scatterplot.matrix' is deprecated.
## Use 'scatterplotMatrix' instead.
## See help("Deprecated") and help("car-deprecated").
- To run a suitable test to check the hypothesis for your suitable assumptions(using cortest)
Null hypothesis 1 : There is no significant relationship between the price of the car and mileage
cor.test(car$price, car$mileage)##
## Pearson's product-moment correlation
##
## data: car$price and car$mileage
## t = -32.18, df = 9574, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3303770 -0.2942269
## sample estimates:
## cor
## -0.3124151p-value < 2.2e-16. So we reject the null hyphothesis in favour of alternative hyphothesis that there is significant relationship between price of car and its mileage.
Null hypothesis 2 : There is no significant relationship between the price of the car and engine volume
cor.test(car$price, car$engV)##
## Pearson's product-moment correlation
##
## data: car$price and car$engV
## t = 4.8889, df = 9140, p-value = 1.031e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03060302 0.07149463
## sample estimates:
## cor
## 0.05107023p-value = 1.031e-06 < 0.05. So we reject the null hyphothesis in favour of alternative hyphothesis that there is significant relationship between price of car and its engine volume.
12)To run a t-test to analyse the hypothesis.
Null Hyphothesis 3: There is no significant difference between the prices of cars based on whether the car is registered in Ukraine or not
t.test(car$price~car$registration)##
## Welch Two Sample t-test
##
## data: car$price by car$registration
## t = -42.281, df = 6577.2, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12979.45 -11829.22
## sample estimates:
## mean in group no mean in group yes
## 3955.677 16360.012p-value < 2.2e-16 < 0.05 . SO we reject the ull hyphothesis ans conclude that There is significant difference between the prices of cars based on whether the car is registered in Ukraine or not