store<-read.csv(paste("Store24.csv",sep=""))
store
## store Sales Profit MTenure CTenure Pop Comp Visibility
## 1 1 1060294 265014 0.0000000 24.8049300 7535 2.797888 3
## 2 2 1619874 424007 86.2221900 6.6365500 8630 4.235555 4
## 3 3 1099921 222735 23.8885400 5.0266940 9695 4.494666 3
## 4 4 1053860 210122 0.0000000 5.3716630 2797 4.253946 4
## 5 5 1227841 300480 3.8773700 6.8665300 20335 1.651364 2
## 6 6 1703140 469050 149.9359000 11.3511300 16926 3.184613 3
## 7 7 1809256 476355 62.5308000 7.3264880 17754 3.377900 2
## 8 8 1378482 361115 0.0000000 56.7720800 20824 2.895114 4
## 9 9 2113089 474725 108.9935000 6.0616020 26519 2.637630 2
## 10 10 1080979 278625 31.4789900 23.1950700 16381 2.270771 4
## 11 11 1583446 389886 44.8197700 2.0369610 21550 3.272398 2
## 12 12 1444714 329020 277.9877000 6.6365500 11160 4.903895 4
## 13 13 857843 152513 0.6571813 1.5770020 14186 4.435671 3
## 14 14 1171491 261571 87.3722600 2.9568790 6898 4.233057 4
## 15 15 1005627 203951 0.0000000 8.4763860 8684 3.844220 3
## 16 16 883864 196277 23.6585300 4.6817250 6872 3.344703 3
## 17 17 1095695 265584 31.7090000 3.6468170 14477 2.561704 3
## 18 18 1704826 394039 239.9698000 33.7741300 3807 3.994713 5
## 19 19 1127332 261495 3.4173430 16.9856300 4669 2.753616 2
## 20 20 1320950 269235 65.0609500 5.9466120 15377 4.148495 3
## 21 21 1237518 282584 24.1185600 7.2114990 14022 4.020201 3
## 22 22 1433440 367036 18.3682200 25.9548300 8280 4.464360 4
## 23 23 1351972 277414 12.3878700 3.4168380 13797 3.594539 3
## 24 24 1071307 267354 44.8197700 3.4168380 9069 3.280590 2
## 25 25 1282886 282124 0.0000000 10.3162200 6183 3.517020 3
## 26 26 898548 211912 0.6571813 20.4353200 9999 4.178195 3
## 27 27 985862 230194 50.1100800 17.4455900 8153 3.719806 3
## 28 28 1141465 273036 23.8885400 16.9856300 14673 3.193422 3
## 29 29 924782 263956 19.5182900 23.5400400 11350 5.392077 3
## 30 30 1874873 333607 73.3414400 23.4250500 1116 3.578323 3
## 31 31 993597 211885 0.0000000 10.7761800 2578 3.100689 2
## 32 32 828918 149033 36.0792600 6.6365500 9697 4.641468 3
## 33 33 1369092 292745 51.7201700 3.8767970 8177 5.309016 3
## 34 34 1557084 382199 29.1788500 19.7453800 10923 2.361195 4
## 35 35 1443230 322624 36.9993100 14.8008200 14361 3.613021 4
## 36 36 1016950 219292 41.5995800 20.8952800 3218 3.929021 3
## 37 37 1202917 187765 23.1985000 1.3470230 8870 4.491863 3
## 38 38 991524 203184 15.6080600 1.5770020 6557 4.225993 3
## 39 39 979361 221130 34.6991800 5.4866530 8896 5.046338 2
## 40 40 1042664 222913 122.7943000 16.7556500 2521 11.127880 3
## 41 41 744211 147327 14.9180200 11.9260800 9701 4.364600 2
## 42 42 1273855 264072 2.4972890 86.0944600 2106 3.231049 3
## 43 43 1296711 337233 177.5704000 5.4866530 3495 3.653641 4
## 44 44 1807740 439781 182.2364000 114.1519000 20624 3.628561 3
## 45 45 1602362 410149 47.6456500 9.1663250 17808 3.472609 5
## 46 46 1339214 315780 6.1775050 5.2566730 9285 3.144458 4
## 47 47 1665657 387853 12.8479000 6.6365500 23623 2.422707 2
## 48 48 1243167 284169 31.4789900 8.2464070 8491 4.848749 3
## 49 49 983296 195276 55.4003900 14.6858300 1863 3.713871 4
## 50 50 935257 251013 12.8479000 16.0657100 14653 1.751638 3
## 51 51 1027035 237344 3.4173430 7.0965090 3126 2.447474 2
## 52 52 1073008 169201 24.1185600 3.4168380 14859 6.585143 3
## 53 53 1355684 365018 57.2404900 8.2464070 6909 3.156869 2
## 54 54 811190 159792 6.6703910 3.8767970 3747 3.756011 3
## 55 55 925744 147672 6.6703910 18.3655000 10532 6.389294 4
## 56 56 916197 189235 4.7974240 2.7268990 13740 4.597269 2
## 57 57 699306 122180 24.3485700 2.9568790 3642 2.973376 3
## 58 58 989760 227601 4.5674100 4.1067760 8477 3.993874 4
## 59 59 1334898 303069 13.3079200 13.7659100 6231 3.301353 3
## 60 60 1433624 356071 33.5162500 6.4065710 8845 2.719548 3
## 61 61 716589 177046 21.8184200 13.3059500 3014 3.263994 3
## 62 62 942915 202641 12.1578600 6.8665300 9820 4.201450 3
## 63 63 1045264 239036 8.2476260 6.8665300 7581 4.136580 3
## 64 64 969509 221157 0.0000000 0.8870637 17110 2.378613 4
## 65 65 1349972 301641 150.2317000 23.4250500 1075 3.218960 3
## 66 66 879581 146058 115.2039000 3.8767970 1046 6.569790 2
## 67 67 1228052 362067 5.2574510 3.4168380 11552 3.583143 3
## 68 68 1018195 236339 17.4481600 2.2669400 9018 3.504810 4
## 69 69 1574290 375393 44.1297300 26.7597500 5050 3.949484 3
## 70 70 1207204 254203 14.9180200 3.8767970 19809 3.122484 3
## 71 71 977566 198529 43.8997200 38.3737200 3265 3.856324 2
## 72 72 848140 196772 126.4745000 27.4496900 3151 3.680586 2
## 73 73 1115450 279193 41.1395500 6.4065710 6276 4.180132 4
## 74 74 1782957 518998 171.0972000 29.5195100 10913 2.319850 3
## 75 75 1321870 296826 2.2672760 8.7063660 8966 1.886111 4
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## 1 3 1 1 3.56 3.150000 86.84327
## 2 3 1 1 3.20 3.556667 94.73510
## 3 3 1 1 3.80 4.116667 78.94776
## 4 2 1 1 2.06 4.100000 100.00000
## 5 5 0 1 3.65 3.588889 68.42164
## 6 4 1 0 3.58 4.605556 94.73510
## 7 5 1 1 3.94 4.100000 81.57837
## 8 3 1 1 3.98 3.800000 78.94776
## 9 4 1 1 3.22 3.583333 100.00000
## 10 3 1 0 3.54 3.561111 100.00000
## 11 5 1 1 3.43 3.200000 100.00000
## 12 4 1 0 3.35 3.238889 100.00000
## 13 2 1 1 4.10 3.000000 76.30609
## 14 2 1 1 2.54 3.890000 100.00000
## 15 4 1 1 3.50 3.427778 94.73510
## 16 3 1 0 3.05 4.000000 97.36939
## 17 4 1 1 3.20 3.000000 78.94776
## 18 3 1 1 3.18 3.866667 97.36939
## 19 3 1 1 3.65 3.083333 68.42164
## 20 2 1 1 3.80 3.894444 100.00000
## 21 3 1 1 3.60 3.677778 94.73510
## 22 3 1 1 3.30 3.973333 100.00000
## 23 4 1 1 3.65 3.588889 100.00000
## 24 3 1 0 2.40 4.083333 100.00000
## 25 3 1 1 2.93 3.500000 86.84327
## 26 2 1 1 3.52 3.100000 97.36939
## 27 2 1 1 4.03 3.543333 100.00000
## 28 4 1 0 3.55 2.988889 84.20898
## 29 2 1 1 3.46 3.610000 100.00000
## 30 2 1 1 3.52 3.473333 100.00000
## 31 2 1 1 2.86 3.673333 88.92860
## 32 3 1 0 3.28 3.550000 73.68654
## 33 2 1 1 3.56 3.088889 96.05408
## 34 4 1 1 3.58 3.577778 97.36939
## 35 3 1 1 3.90 3.916667 100.00000
## 36 2 1 1 3.50 4.244444 100.00000
## 37 3 1 1 3.38 4.016667 73.68654
## 38 2 1 1 3.68 4.494444 100.00000
## 39 4 1 1 3.90 3.872222 81.57837
## 40 4 1 1 3.45 4.194445 78.94776
## 41 3 1 1 3.03 3.672222 81.13993
## 42 2 1 1 3.44 3.656667 98.68839
## 43 3 1 1 3.73 3.608233 94.73510
## 44 4 0 1 4.06 4.172222 86.84327
## 45 3 1 1 3.58 4.622222 100.00000
## 46 3 1 1 3.40 3.444445 80.52612
## 47 5 1 1 4.23 3.950000 99.80105
## 48 2 1 1 3.42 3.872222 97.36939
## 49 1 1 1 3.23 3.576667 81.58205
## 50 4 1 0 4.64 3.016667 76.31715
## 51 4 1 1 3.66 3.150000 60.52612
## 52 3 1 1 3.83 3.833333 94.73510
## 53 2 1 1 3.63 3.683333 94.73510
## 54 2 1 1 3.08 3.933333 65.78734
## 55 3 1 1 3.49 3.477778 76.31346
## 56 3 1 0 3.10 3.800000 64.35046
## 57 2 1 1 3.35 2.956667 84.21266
## 58 2 1 1 2.93 3.050000 57.89552
## 59 3 1 1 3.22 3.233333 100.00000
## 60 4 1 1 3.37 3.344445 57.89552
## 61 1 1 1 3.07 3.126667 73.68654
## 62 4 1 0 3.08 3.300000 61.40299
## 63 3 1 1 3.38 3.666667 100.00000
## 64 3 1 1 3.28 3.311111 79.47388
## 65 1 1 1 3.73 3.440000 63.15673
## 66 3 1 1 4.03 3.673333 80.26675
## 67 3 1 1 3.37 4.150000 97.36939
## 68 2 1 1 2.83 3.000000 81.31677
## 69 3 1 1 3.56 3.940000 92.89294
## 70 4 1 0 3.24 3.538889 78.94407
## 71 1 1 1 3.88 3.466667 79.94253
## 72 1 1 1 3.73 3.416667 73.68654
## 73 3 1 1 3.20 4.083333 85.96640
## 74 4 1 0 3.50 4.405556 94.73878
## 75 4 0 1 3.57 3.344445 89.47388
summary(store)
## store Sales Profit MTenure
## Min. : 1.0 Min. : 699306 Min. :122180 Min. : 0.00
## 1st Qu.:19.5 1st Qu.: 984579 1st Qu.:211004 1st Qu.: 6.67
## Median :38.0 Median :1127332 Median :265014 Median : 24.12
## Mean :38.0 Mean :1205413 Mean :276314 Mean : 45.30
## 3rd Qu.:56.5 3rd Qu.:1362388 3rd Qu.:331314 3rd Qu.: 50.92
## Max. :75.0 Max. :2113089 Max. :518998 Max. :277.99
## CTenure Pop Comp Visibility
## Min. : 0.8871 Min. : 1046 Min. : 1.651 Min. :2.00
## 1st Qu.: 4.3943 1st Qu.: 5616 1st Qu.: 3.151 1st Qu.:3.00
## Median : 7.2115 Median : 8896 Median : 3.629 Median :3.00
## Mean : 13.9315 Mean : 9826 Mean : 3.788 Mean :3.08
## 3rd Qu.: 17.2156 3rd Qu.:14104 3rd Qu.: 4.230 3rd Qu.:4.00
## Max. :114.1519 Max. :26519 Max. :11.128 Max. :5.00
## PedCount Res Hours24 CrewSkill
## Min. :1.00 Min. :0.00 Min. :0.00 Min. :2.060
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:3.225
## Median :3.00 Median :1.00 Median :1.00 Median :3.500
## Mean :2.96 Mean :0.96 Mean :0.84 Mean :3.457
## 3rd Qu.:4.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:3.655
## Max. :5.00 Max. :1.00 Max. :1.00 Max. :4.640
## MgrSkill ServQual
## Min. :2.957 Min. : 57.90
## 1st Qu.:3.344 1st Qu.: 78.95
## Median :3.589 Median : 89.47
## Mean :3.638 Mean : 87.15
## 3rd Qu.:3.925 3rd Qu.: 99.90
## Max. :4.622 Max. :100.00
mean(store$Profit)
## [1] 276313.6
sd(store$Profit)
## [1] 89404.08
mean(store$MTenure)
## [1] 45.29644
sd(store$MTenure)
## [1] 57.67155
mean(store$CTenure)
## [1] 13.9315
sd(store$CTenure)
## [1] 17.69752
attach(store)
## The following object is masked _by_ .GlobalEnv:
##
## store
most<-store[order(-Profit),]
most[1:10,1:5]
## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550
least<-store[order(Profit),]
least[1:10,1:5]
## store Sales Profit MTenure CTenure
## 57 57 699306 122180 24.3485700 2.956879
## 66 66 879581 146058 115.2039000 3.876797
## 41 41 744211 147327 14.9180200 11.926080
## 55 55 925744 147672 6.6703910 18.365500
## 32 32 828918 149033 36.0792600 6.636550
## 13 13 857843 152513 0.6571813 1.577002
## 54 54 811190 159792 6.6703910 3.876797
## 52 52 1073008 169201 24.1185600 3.416838
## 61 61 716589 177046 21.8184200 13.305950
## 37 37 1202917 187765 23.1985000 1.347023
library(car)
scatterplot(MTenure,Profit)
scatterplot(CTenure,Profit)
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:car':
##
## logit
corr.test(store,use="complete")
## Call:corr.test(x = store, use = "complete")
## Correlation matrix
## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 1.00 -0.23 -0.20 -0.06 0.02 -0.29 0.03 -0.03
## Sales -0.23 1.00 0.92 0.45 0.25 0.40 -0.24 0.13
## Profit -0.20 0.92 1.00 0.44 0.26 0.43 -0.33 0.14
## MTenure -0.06 0.45 0.44 1.00 0.24 -0.06 0.18 0.16
## CTenure 0.02 0.25 0.26 0.24 1.00 0.00 -0.07 0.07
## Pop -0.29 0.40 0.43 -0.06 0.00 1.00 -0.27 -0.05
## Comp 0.03 -0.24 -0.33 0.18 -0.07 -0.27 1.00 0.03
## Visibility -0.03 0.13 0.14 0.16 0.07 -0.05 0.03 1.00
## PedCount -0.22 0.42 0.45 0.06 -0.08 0.61 -0.15 -0.14
## Res -0.03 -0.17 -0.16 -0.06 -0.34 -0.24 0.22 0.02
## Hours24 0.03 0.06 -0.03 -0.17 0.07 -0.22 0.13 0.05
## CrewSkill 0.05 0.16 0.16 0.10 0.26 0.28 -0.04 -0.20
## MgrSkill -0.07 0.31 0.32 0.23 0.12 0.08 0.22 0.07
## ServQual -0.32 0.39 0.36 0.18 0.08 0.12 0.02 0.21
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store -0.22 -0.03 0.03 0.05 -0.07 -0.32
## Sales 0.42 -0.17 0.06 0.16 0.31 0.39
## Profit 0.45 -0.16 -0.03 0.16 0.32 0.36
## MTenure 0.06 -0.06 -0.17 0.10 0.23 0.18
## CTenure -0.08 -0.34 0.07 0.26 0.12 0.08
## Pop 0.61 -0.24 -0.22 0.28 0.08 0.12
## Comp -0.15 0.22 0.13 -0.04 0.22 0.02
## Visibility -0.14 0.02 0.05 -0.20 0.07 0.21
## PedCount 1.00 -0.28 -0.28 0.21 0.09 -0.01
## Res -0.28 1.00 -0.09 -0.15 -0.03 0.09
## Hours24 -0.28 -0.09 1.00 0.11 -0.04 0.06
## CrewSkill 0.21 -0.15 0.11 1.00 -0.02 -0.03
## MgrSkill 0.09 -0.03 -0.04 -0.02 1.00 0.36
## ServQual -0.01 0.09 0.06 -0.03 0.36 1.00
## Sample Size
## [1] 75
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 0.00 1.00 1.00 1.00 1.00 0.89 1.00 1.00
## Sales 0.05 0.00 0.00 0.00 1.00 0.03 1.00 1.00
## Profit 0.09 0.00 0.00 0.01 1.00 0.01 0.26 1.00
## MTenure 0.63 0.00 0.00 0.00 1.00 1.00 1.00 1.00
## CTenure 0.87 0.03 0.03 0.04 0.00 1.00 1.00 1.00
## Pop 0.01 0.00 0.00 0.60 0.99 0.00 1.00 1.00
## Comp 0.79 0.04 0.00 0.12 0.55 0.02 0.00 1.00
## Visibility 0.82 0.26 0.25 0.18 0.57 0.67 0.81 0.00
## PedCount 0.06 0.00 0.00 0.60 0.47 0.00 0.21 0.23
## Res 0.79 0.15 0.17 0.60 0.00 0.04 0.06 0.85
## Hours24 0.82 0.59 0.83 0.16 0.53 0.06 0.27 0.69
## CrewSkill 0.68 0.16 0.17 0.39 0.03 0.01 0.72 0.09
## MgrSkill 0.54 0.01 0.00 0.05 0.29 0.48 0.05 0.53
## ServQual 0.00 0.00 0.00 0.12 0.49 0.29 0.88 0.07
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store 1.00 1.00 1.00 1.00 1.00 0.37
## Sales 0.01 1.00 1.00 1.00 0.49 0.05
## Profit 0.00 1.00 1.00 1.00 0.37 0.11
## MTenure 1.00 1.00 1.00 1.00 1.00 1.00
## CTenure 1.00 0.22 1.00 1.00 1.00 1.00
## Pop 0.00 1.00 1.00 1.00 1.00 1.00
## Comp 1.00 1.00 1.00 1.00 1.00 1.00
## Visibility 1.00 1.00 1.00 1.00 1.00 1.00
## PedCount 0.00 0.99 1.00 1.00 1.00 1.00
## Res 0.01 0.00 1.00 1.00 1.00 1.00
## Hours24 0.02 0.45 0.00 1.00 1.00 1.00
## CrewSkill 0.07 0.19 0.37 0.00 1.00 1.00
## MgrSkill 0.46 0.78 0.74 0.86 0.00 0.14
## ServQual 0.96 0.44 0.62 0.78 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option
x<-store[,c("Profit")]
y<-store[,c("MTenure")]
formatC(cor(x,y),digits=2)
## [1] "0.44"
x<-store[,c("Profit")]
y<-store[,c("CTenure")]
formatC(cor(x,y),digits=2)
## [1] "0.26"
library(corrgram)
corrgram(store, order = FALSE, lower.panel = panel.shade, upper.panel = panel.pie, text.panel= panel.txt, main = "Corrgram of store variables")
cor.test(store[, "Profit"], store[, "MTenure"])
##
## Pearson's product-moment correlation
##
## data: store[, "Profit"] and store[, "MTenure"]
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692
cor.test(store[,"Profit"],store[,"CTenure"])
##
## Pearson's product-moment correlation
##
## data: store[, "Profit"] and store[, "CTenure"]
## t = 2.2786, df = 73, p-value = 0.02562
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03262507 0.45786339
## sample estimates:
## cor
## 0.2576789
fit<-lm(Profit~MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility,data=store)
summary(fit)
##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -105789 -35946 -7069 33780 112390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7610.041 66821.994 0.114 0.909674
## MTenure 760.993 127.086 5.988 9.72e-08 ***
## CTenure 944.978 421.687 2.241 0.028400 *
## Comp -25286.887 5491.937 -4.604 1.94e-05 ***
## Pop 3.667 1.466 2.501 0.014890 *
## PedCount 34087.359 9073.196 3.757 0.000366 ***
## Res 91584.675 39231.283 2.334 0.022623 *
## Hours24 63233.307 19641.114 3.219 0.001994 **
## Visibility 12625.447 9087.620 1.389 0.169411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56970 on 66 degrees of freedom
## Multiple R-squared: 0.6379, Adjusted R-squared: 0.594
## F-statistic: 14.53 on 8 and 66 DF, p-value: 5.382e-12
summary(fit)$coef[summary(fit)$coef[,4] <= 0.05, 4]
## MTenure CTenure Comp Pop PedCount
## 9.715897e-08 2.839955e-02 1.938381e-05 1.489046e-02 3.664408e-04
## Res Hours24
## 2.262320e-02 1.993586e-03
summary(fit)$coef[summary(fit)$coef[,4] > 0.05, 4]
## (Intercept) Visibility
## 0.9096745 0.1694106
summary(fit)$coefficients["MTenure",1]
## [1] 760.9927
summary(fit)$coefficients["CTenure",1]
## [1] 944.978
Executive summary
- The mean profit was found to be 276313.6 and the standard deviation of Profit was found to be 89404.08.
- The mean manager tenure was found to be 45.29644 and the standard deviation of manager tenure was found to be 57.67155.
- The mean crew tenure was found to be 13.9315 and the standard deviation of manager tenure was found to be 17.69752.
- Visibility has p<0.05 and has no significant effect on profit.
- The most profitable store is Store 57 and the least profitable store is Store 74.
- The correlation between Profit and MTenure is 0.4388 and correlation between Profit and CTenure is 0.2576.
- We can say from the regression analysis that the an increase in Manager’s experience with Store24 can increase the store’s profit by USD 760.993.
- We can say from the regression analysis that the an increase in Crew’s experience with Store24 can increase the store’s profit by USD 944.978.