SETTING THE DIRECTORY AND READING THE DATA
setwd("C:/Users/Bagga/Desktop/Internship 2018/Week 3, Day 1")
store <- read.csv("C:/Users/Bagga/Desktop/Internship 2018/Week 3, Day 1/Store24.csv")
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 OF THE DATA
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
STRUCTURE OF THE DATA
str(store)
## 'data.frame': 75 obs. of 14 variables:
## $ store : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Sales : int 1060294 1619874 1099921 1053860 1227841 1703140 1809256 1378482 2113089 1080979 ...
## $ Profit : int 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625 ...
## $ MTenure : num 0 86.22 23.89 0 3.88 ...
## $ CTenure : num 24.8 6.64 5.03 5.37 6.87 ...
## $ Pop : int 7535 8630 9695 2797 20335 16926 17754 20824 26519 16381 ...
## $ Comp : num 2.8 4.24 4.49 4.25 1.65 ...
## $ Visibility: int 3 4 3 4 2 3 2 4 2 4 ...
## $ PedCount : int 3 3 3 2 5 4 5 3 4 3 ...
## $ Res : int 1 1 1 1 0 1 1 1 1 1 ...
## $ Hours24 : int 1 1 1 1 1 0 1 1 1 0 ...
## $ CrewSkill : num 3.56 3.2 3.8 2.06 3.65 3.58 3.94 3.98 3.22 3.54 ...
## $ MgrSkill : num 3.15 3.56 4.12 4.1 3.59 ...
## $ ServQual : num 86.8 94.7 78.9 100 68.4 ...
MEAN AND STANDARD DEVIATION
library(psych)
describe(store)[,1:4]
## vars n mean sd
## store 1 75 38.00 21.79
## Sales 2 75 1205413.12 304531.31
## Profit 3 75 276313.61 89404.08
## MTenure 4 75 45.30 57.67
## CTenure 5 75 13.93 17.70
## Pop 6 75 9825.59 5911.67
## Comp 7 75 3.79 1.31
## Visibility 8 75 3.08 0.75
## PedCount 9 75 2.96 0.99
## Res 10 75 0.96 0.20
## Hours24 11 75 0.84 0.37
## CrewSkill 12 75 3.46 0.41
## MgrSkill 13 75 3.64 0.41
## ServQual 14 75 87.15 12.61
Bottom 10 most profitable stores.
attach(store)
newdata <- store[order(Profit),]
newdata[1:10,]
## store Sales Profit MTenure CTenure Pop Comp Visibility
## 57 57 699306 122180 24.3485700 2.956879 3642 2.973376 3
## 66 66 879581 146058 115.2039000 3.876797 1046 6.569790 2
## 41 41 744211 147327 14.9180200 11.926080 9701 4.364600 2
## 55 55 925744 147672 6.6703910 18.365500 10532 6.389294 4
## 32 32 828918 149033 36.0792600 6.636550 9697 4.641468 3
## 13 13 857843 152513 0.6571813 1.577002 14186 4.435671 3
## 54 54 811190 159792 6.6703910 3.876797 3747 3.756011 3
## 52 52 1073008 169201 24.1185600 3.416838 14859 6.585143 3
## 61 61 716589 177046 21.8184200 13.305950 3014 3.263994 3
## 37 37 1202917 187765 23.1985000 1.347023 8870 4.491863 3
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## 57 2 1 1 3.35 2.956667 84.21266
## 66 3 1 1 4.03 3.673333 80.26675
## 41 3 1 1 3.03 3.672222 81.13993
## 55 3 1 1 3.49 3.477778 76.31346
## 32 3 1 0 3.28 3.550000 73.68654
## 13 2 1 1 4.10 3.000000 76.30609
## 54 2 1 1 3.08 3.933333 65.78734
## 52 3 1 1 3.83 3.833333 94.73510
## 61 1 1 1 3.07 3.126667 73.68654
## 37 3 1 1 3.38 4.016667 73.68654
detach(store)
Top 10 least profitable stores.
attach(store)
newdata <- store[order(-Profit),]
newdata[1:10,]
## store Sales Profit MTenure CTenure Pop Comp Visibility
## 74 74 1782957 518998 171.09720 29.519510 10913 2.319850 3
## 7 7 1809256 476355 62.53080 7.326488 17754 3.377900 2
## 9 9 2113089 474725 108.99350 6.061602 26519 2.637630 2
## 6 6 1703140 469050 149.93590 11.351130 16926 3.184613 3
## 44 44 1807740 439781 182.23640 114.151900 20624 3.628561 3
## 2 2 1619874 424007 86.22219 6.636550 8630 4.235555 4
## 45 45 1602362 410149 47.64565 9.166325 17808 3.472609 5
## 18 18 1704826 394039 239.96980 33.774130 3807 3.994713 5
## 11 11 1583446 389886 44.81977 2.036961 21550 3.272398 2
## 47 47 1665657 387853 12.84790 6.636550 23623 2.422707 2
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## 74 4 1 0 3.50 4.405556 94.73878
## 7 5 1 1 3.94 4.100000 81.57837
## 9 4 1 1 3.22 3.583333 100.00000
## 6 4 1 0 3.58 4.605556 94.73510
## 44 4 0 1 4.06 4.172222 86.84327
## 2 3 1 1 3.20 3.556667 94.73510
## 45 3 1 1 3.58 4.622222 100.00000
## 18 3 1 1 3.18 3.866667 97.36939
## 11 5 1 1 3.43 3.200000 100.00000
## 47 5 1 1 4.23 3.950000 99.80105
detach(store)
SCATTER PLOT OF PROFIT VS MTENURE
library("corrplot")
library("gplots")
plot(store$Profit,store$MTenure,
col="blue",
# ylim = c(0,300),
main = "Profit vs MTenure",
xlab = "Profit", ylab = "MTenure")
abline(h=mean(store$MTenure),col="black", lty="dotted")
abline(v=mean(store$Profit),col="black", lty="dotted")
abline(lm(store$MTenure ~ store$Profit)) #lm = REGRESSION LINE
SCATTER PLOT OF PROFIT VS TENURE
library(corrplot)
library(gplots)
plot(store$Profit,store$CTenure,
col="blue",
main = "Profit vs CTenure",
xlab = "Profit", ylab = "CTenure")
abline(h=mean(store$CTenure),col="black", lty="dotted")
abline(v=mean(store$Profit),col="black", lty="dotted")
abline(lm(store$CTenure ~ store$Profit)) #lm = REGRESSION LINE
Correlation matirx
cor(store [, ])
## store Sales Profit MTenure CTenure
## store 1.00000000 -0.22693400 -0.19993481 -0.05655216 0.019930097
## Sales -0.22693400 1.00000000 0.92387059 0.45488023 0.254315184
## Profit -0.19993481 0.92387059 1.00000000 0.43886921 0.257678895
## MTenure -0.05655216 0.45488023 0.43886921 1.00000000 0.243383135
## CTenure 0.01993010 0.25431518 0.25767890 0.24338314 1.000000000
## Pop -0.28936691 0.40348147 0.43063326 -0.06089646 -0.001532449
## Comp 0.03194023 -0.23501372 -0.33454148 0.18087179 -0.070281327
## Visibility -0.02648858 0.13065638 0.13569207 0.15651731 0.066506016
## PedCount -0.22117519 0.42391087 0.45023346 0.06198608 -0.084112627
## Res -0.03142976 -0.16672402 -0.15947734 -0.06234721 -0.340340876
## Hours24 0.02687986 0.06324716 -0.02568703 -0.16513872 0.072865022
## CrewSkill 0.04866273 0.16402179 0.16008443 0.10162169 0.257154817
## MgrSkill -0.07218804 0.31163056 0.32284842 0.22962743 0.124045346
## ServQual -0.32246921 0.38638112 0.36245032 0.18168875 0.081156172
## Pop Comp Visibility PedCount Res
## store -0.289366908 0.03194023 -0.02648858 -0.221175193 -0.03142976
## Sales 0.403481471 -0.23501372 0.13065638 0.423910867 -0.16672402
## Profit 0.430633264 -0.33454148 0.13569207 0.450233461 -0.15947734
## MTenure -0.060896460 0.18087179 0.15651731 0.061986084 -0.06234721
## CTenure -0.001532449 -0.07028133 0.06650602 -0.084112627 -0.34034088
## Pop 1.000000000 -0.26828355 -0.04998269 0.607638861 -0.23693726
## Comp -0.268283553 1.00000000 0.02844548 -0.146325204 0.21923878
## Visibility -0.049982694 0.02844548 1.00000000 -0.141068116 0.02194756
## PedCount 0.607638861 -0.14632520 -0.14106812 1.000000000 -0.28437852
## Res -0.236937265 0.21923878 0.02194756 -0.284378520 1.00000000
## Hours24 -0.221767927 0.12957478 0.04692587 -0.275973353 -0.08908708
## CrewSkill 0.282845090 -0.04229731 -0.19745297 0.213672596 -0.15331247
## MgrSkill 0.083554590 0.22407913 0.07348301 0.087475440 -0.03213640
## ServQual 0.123946521 0.01814508 0.20992919 -0.005445552 0.09081624
## Hours24 CrewSkill MgrSkill ServQual
## store 0.02687986 0.04866273 -0.07218804 -0.322469213
## Sales 0.06324716 0.16402179 0.31163056 0.386381121
## Profit -0.02568703 0.16008443 0.32284842 0.362450323
## MTenure -0.16513872 0.10162169 0.22962743 0.181688755
## CTenure 0.07286502 0.25715482 0.12404535 0.081156172
## Pop -0.22176793 0.28284509 0.08355459 0.123946521
## Comp 0.12957478 -0.04229731 0.22407913 0.018145080
## Visibility 0.04692587 -0.19745297 0.07348301 0.209929194
## PedCount -0.27597335 0.21367260 0.08747544 -0.005445552
## Res -0.08908708 -0.15331247 -0.03213640 0.090816237
## Hours24 1.00000000 0.10536295 -0.03883007 0.058325655
## CrewSkill 0.10536295 1.00000000 -0.02100949 -0.033516504
## MgrSkill -0.03883007 -0.02100949 1.00000000 0.356702708
## ServQual 0.05832565 -0.03351650 0.35670271 1.000000000
CORRELETION
attach(store)
cor.test(Profit,MTenure)
##
## Pearson's product-moment correlation
##
## data: Profit and 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(Profit,CTenure)
##
## Pearson's product-moment correlation
##
## data: Profit and 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
P-VALUE OF PROFIT AND MTENURE - 8.193e-05
P-VALUE OF PROFIT AND CTENURE - 0.02562
CORRERAM
library(corrplot)
library(gplots)
par(mfrow=c(1,1))
corrplot.mixed(corr = cor(store [, ]), use = "complete.obs",
upper = "ellipse",tl.pos= "lt")
REGRESSION ANALYSIS
model <- lm(Profit ~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility, data = store)
summary(model)
##
## 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
INTERPRETATION
EXECUTIVE SUMMARY
THE PREDICTED EQUATION : PROFIT = 7610 + 760.99MTENURE + 955.97CTENURE + (-25286.88COMP) + 3.66POP + 34087.35PEDCOUNT + 91584.67RES ## +63233.30HOURS24 + 12625.44VISIBILITY
THE BETA COEFFICIENTS, MTENURE, COMP(NUMBER OF COMPETITORS) AND PEDCOUNT(PEDESTRIAN FOOT RATING) ARE HIGHLY STATISTICALLY SIGNIFICANT,
WHILE THE BETA COEFFICIENTS OF VISIBILITY AND PROFIT ARE NOT AT ALL STATISTICALLY SIGNIFICANT.
THE BETA COEEFICIENTS OF CTENURE, POP(POPULATION), Res(RESIDENTIAL VS INDUSTRIAL) ARE LIKELY TO BE LESS STATISTICALLY SIGNIFICANT
The regression coefficient (760.99) is significantly dfferent from zero (p < 0.001)
There is an expected increase of profit of 761 for every 1 month increase in Manager’s Tenure.
The regression coefficient (944.97) is not significantly dfferent from zero (p > 0.001)
There is an expected decrease of profit of 945 for every 1 month increase in Crew’s Tenure.
THE MULTIPLE R-SQUARED (0.637) INDICATES THAT THE MODEL ACCOUNTS FOR 63.7% OF THE VARIANCE IN PROFIT
THE RESIDUAL STANDARD ERROR (56970) CAN BE THOUGHT OF AS THE AVERAGE ERROR IN PREDICTING PROFIT USING THIS MODEL
THE F-STATISITCS PREDICT THAT THE MODEL IS HIGHLY SIGNIFICANT AS P-VALUE IS 5.382e-12 (p< 0.001)