Manager Retention
Siddharth tyagi
December 17, 2017
Manager retention case study
Importing data into workspace
myData <- read.csv(paste("Store24.csv"));
head(myData)## store Sales Profit MTenure CTenure Pop Comp Visibility
## 1 1 1060294 265014 0.00000 24.804930 7535 2.797888 3
## 2 2 1619874 424007 86.22219 6.636550 8630 4.235555 4
## 3 3 1099921 222735 23.88854 5.026694 9695 4.494666 3
## 4 4 1053860 210122 0.00000 5.371663 2797 4.253946 4
## 5 5 1227841 300480 3.87737 6.866530 20335 1.651364 2
## 6 6 1703140 469050 149.93590 11.351130 16926 3.184613 3
## 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 94.73510Mean:
mean(myData$Profit)## [1] 276313.6mean(myData$CTenure)## [1] 13.9315mean(myData$MTenure)## [1] 45.29644Sorting data
attach(mtcars)
View(mtcars)
newdata <- mtcars[order(mpg),] # sort by mpg (ascending)
View(newdata)
newdata[1:5,] # see the first 5 rows## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
## Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4newdata <- mtcars[order(-mpg),] # sort by mpg (descending)
View(newdata)
detach(mtcars)Top 10 most profitable stores..
attach(myData)
newData <- myData[order(Profit),]
newData[1:10][,c("store", "Sales", "Profit", "MTenure", "CTenure")]## store Sales Profit MTenure CTenure
## 57 57 699306 122180 24.3485700 2.9568790
## 66 66 879581 146058 115.2039000 3.8767970
## 41 41 744211 147327 14.9180200 11.9260800
## 55 55 925744 147672 6.6703910 18.3655000
## 32 32 828918 149033 36.0792600 6.6365500
## 13 13 857843 152513 0.6571813 1.5770020
## 54 54 811190 159792 6.6703910 3.8767970
## 52 52 1073008 169201 24.1185600 3.4168380
## 61 61 716589 177046 21.8184200 13.3059500
## 37 37 1202917 187765 23.1985000 1.3470230
## 56 56 916197 189235 4.7974240 2.7268990
## 49 49 983296 195276 55.4003900 14.6858300
## 16 16 883864 196277 23.6585300 4.6817250
## 72 72 848140 196772 126.4745000 27.4496900
## 71 71 977566 198529 43.8997200 38.3737200
## 62 62 942915 202641 12.1578600 6.8665300
## 38 38 991524 203184 15.6080600 1.5770020
## 15 15 1005627 203951 0.0000000 8.4763860
## 4 4 1053860 210122 0.0000000 5.3716630
## 31 31 993597 211885 0.0000000 10.7761800
## 26 26 898548 211912 0.6571813 20.4353200
## 36 36 1016950 219292 41.5995800 20.8952800
## 39 39 979361 221130 34.6991800 5.4866530
## 64 64 969509 221157 0.0000000 0.8870637
## 3 3 1099921 222735 23.8885400 5.0266940
## 40 40 1042664 222913 122.7943000 16.7556500
## 58 58 989760 227601 4.5674100 4.1067760
## 27 27 985862 230194 50.1100800 17.4455900
## 68 68 1018195 236339 17.4481600 2.2669400
## 51 51 1027035 237344 3.4173430 7.0965090
## 63 63 1045264 239036 8.2476260 6.8665300
## 50 50 935257 251013 12.8479000 16.0657100
## 70 70 1207204 254203 14.9180200 3.8767970
## 19 19 1127332 261495 3.4173430 16.9856300
## 14 14 1171491 261571 87.3722600 2.9568790
## 29 29 924782 263956 19.5182900 23.5400400
## 42 42 1273855 264072 2.4972890 86.0944600
## 1 1 1060294 265014 0.0000000 24.8049300
## 17 17 1095695 265584 31.7090000 3.6468170
## 24 24 1071307 267354 44.8197700 3.4168380
## 20 20 1320950 269235 65.0609500 5.9466120
## 28 28 1141465 273036 23.8885400 16.9856300
## 23 23 1351972 277414 12.3878700 3.4168380
## 10 10 1080979 278625 31.4789900 23.1950700
## 73 73 1115450 279193 41.1395500 6.4065710
## 25 25 1282886 282124 0.0000000 10.3162200
## 21 21 1237518 282584 24.1185600 7.2114990
## 48 48 1243167 284169 31.4789900 8.2464070
## 33 33 1369092 292745 51.7201700 3.8767970
## 75 75 1321870 296826 2.2672760 8.7063660
## 5 5 1227841 300480 3.8773700 6.8665300
## 65 65 1349972 301641 150.2317000 23.4250500
## 59 59 1334898 303069 13.3079200 13.7659100
## 46 46 1339214 315780 6.1775050 5.2566730
## 35 35 1443230 322624 36.9993100 14.8008200
## 12 12 1444714 329020 277.9877000 6.6365500
## 30 30 1874873 333607 73.3414400 23.4250500
## 43 43 1296711 337233 177.5704000 5.4866530
## 60 60 1433624 356071 33.5162500 6.4065710
## 8 8 1378482 361115 0.0000000 56.7720800
## 67 67 1228052 362067 5.2574510 3.4168380
## 53 53 1355684 365018 57.2404900 8.2464070
## 22 22 1433440 367036 18.3682200 25.9548300
## 69 69 1574290 375393 44.1297300 26.7597500
## 34 34 1557084 382199 29.1788500 19.7453800
## 47 47 1665657 387853 12.8479000 6.6365500
## 11 11 1583446 389886 44.8197700 2.0369610
## 18 18 1704826 394039 239.9698000 33.7741300
## 45 45 1602362 410149 47.6456500 9.1663250
## 2 2 1619874 424007 86.2221900 6.6365500
## 44 44 1807740 439781 182.2364000 114.1519000
## 6 6 1703140 469050 149.9359000 11.3511300
## 9 9 2113089 474725 108.9935000 6.0616020
## 7 7 1809256 476355 62.5308000 7.3264880
## 74 74 1782957 518998 171.0972000 29.5195100Top 10 least profitable stores
attach(myData)## The following objects are masked from myData (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, VisibilitynewData <- myData[order(-Profit),]
newData[1:10][,c("store", "Sales", "Profit", "MTenure", "CTenure")]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.0972000 29.5195100
## 7 7 1809256 476355 62.5308000 7.3264880
## 9 9 2113089 474725 108.9935000 6.0616020
## 6 6 1703140 469050 149.9359000 11.3511300
## 44 44 1807740 439781 182.2364000 114.1519000
## 2 2 1619874 424007 86.2221900 6.6365500
## 45 45 1602362 410149 47.6456500 9.1663250
## 18 18 1704826 394039 239.9698000 33.7741300
## 11 11 1583446 389886 44.8197700 2.0369610
## 47 47 1665657 387853 12.8479000 6.6365500
## 34 34 1557084 382199 29.1788500 19.7453800
## 69 69 1574290 375393 44.1297300 26.7597500
## 22 22 1433440 367036 18.3682200 25.9548300
## 53 53 1355684 365018 57.2404900 8.2464070
## 67 67 1228052 362067 5.2574510 3.4168380
## 8 8 1378482 361115 0.0000000 56.7720800
## 60 60 1433624 356071 33.5162500 6.4065710
## 43 43 1296711 337233 177.5704000 5.4866530
## 30 30 1874873 333607 73.3414400 23.4250500
## 12 12 1444714 329020 277.9877000 6.6365500
## 35 35 1443230 322624 36.9993100 14.8008200
## 46 46 1339214 315780 6.1775050 5.2566730
## 59 59 1334898 303069 13.3079200 13.7659100
## 65 65 1349972 301641 150.2317000 23.4250500
## 5 5 1227841 300480 3.8773700 6.8665300
## 75 75 1321870 296826 2.2672760 8.7063660
## 33 33 1369092 292745 51.7201700 3.8767970
## 48 48 1243167 284169 31.4789900 8.2464070
## 21 21 1237518 282584 24.1185600 7.2114990
## 25 25 1282886 282124 0.0000000 10.3162200
## 73 73 1115450 279193 41.1395500 6.4065710
## 10 10 1080979 278625 31.4789900 23.1950700
## 23 23 1351972 277414 12.3878700 3.4168380
## 28 28 1141465 273036 23.8885400 16.9856300
## 20 20 1320950 269235 65.0609500 5.9466120
## 24 24 1071307 267354 44.8197700 3.4168380
## 17 17 1095695 265584 31.7090000 3.6468170
## 1 1 1060294 265014 0.0000000 24.8049300
## 42 42 1273855 264072 2.4972890 86.0944600
## 29 29 924782 263956 19.5182900 23.5400400
## 14 14 1171491 261571 87.3722600 2.9568790
## 19 19 1127332 261495 3.4173430 16.9856300
## 70 70 1207204 254203 14.9180200 3.8767970
## 50 50 935257 251013 12.8479000 16.0657100
## 63 63 1045264 239036 8.2476260 6.8665300
## 51 51 1027035 237344 3.4173430 7.0965090
## 68 68 1018195 236339 17.4481600 2.2669400
## 27 27 985862 230194 50.1100800 17.4455900
## 58 58 989760 227601 4.5674100 4.1067760
## 40 40 1042664 222913 122.7943000 16.7556500
## 3 3 1099921 222735 23.8885400 5.0266940
## 64 64 969509 221157 0.0000000 0.8870637
## 39 39 979361 221130 34.6991800 5.4866530
## 36 36 1016950 219292 41.5995800 20.8952800
## 26 26 898548 211912 0.6571813 20.4353200
## 31 31 993597 211885 0.0000000 10.7761800
## 4 4 1053860 210122 0.0000000 5.3716630
## 15 15 1005627 203951 0.0000000 8.4763860
## 38 38 991524 203184 15.6080600 1.5770020
## 62 62 942915 202641 12.1578600 6.8665300
## 71 71 977566 198529 43.8997200 38.3737200
## 72 72 848140 196772 126.4745000 27.4496900
## 16 16 883864 196277 23.6585300 4.6817250
## 49 49 983296 195276 55.4003900 14.6858300
## 56 56 916197 189235 4.7974240 2.7268990
## 37 37 1202917 187765 23.1985000 1.3470230
## 61 61 716589 177046 21.8184200 13.3059500
## 52 52 1073008 169201 24.1185600 3.4168380
## 54 54 811190 159792 6.6703910 3.8767970
## 13 13 857843 152513 0.6571813 1.5770020
## 32 32 828918 149033 36.0792600 6.6365500
## 55 55 925744 147672 6.6703910 18.3655000
## 41 41 744211 147327 14.9180200 11.9260800
## 66 66 879581 146058 115.2039000 3.8767970
## 57 57 699306 122180 24.3485700 2.9568790Scatterplots
Correlation
Correlation matrix of datafram
cor(myData, method = "pearson", use = "complete.obs")## 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 1.000000000Correlation between Profit and MTenure, and CTenure.
cor(Profit,MTenure, method = "pearson", use = "complete.obs")## [1] 0.4388692cor(Profit,CTenure, method = "pearson", use = "complete.obs")## [1] 0.2576789Corrgram
library("corrgram")## Warning: package 'corrgram' was built under R version 3.3.3corrgram(myData, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Car Milage Data in PC2/PC1 Order") 
Correlation test
cor.test(Profit,MTenure,method = "pearson")##
## 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
## 0.4388692cor.test(Profit,CTenure,method = "pearson")##
## 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
## 0.2576789Regression analysis
model <- lm(Profit~ MTenure + CTenure+ Comp+ Pop+ PedCount+ Res+ Hours24+ Visibility)
summary(model)##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility)
##
## 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-12Since, p coefficent for MTenure and visibility is > 0.05. Therefore their beta coefficients will have statistically no significance. Where as, CTenure, Comp,Pop,PepCount,Res,Hours24 is less than 0.05. Therefore their beta coefficients will be grater than 0.05.
Since, MTenure has no significant effect on profit. Therefore, increasing MTenure by 1 month will have no effect on Profit.
Increasing Creq Tenure by 1 month will increase the profit by 944.978.