Store24
Kumar Saurabh
18 December 2017
Analysis of the Case Store24: Managing Employee Retention
Store24
2c
store.df <- read.csv(paste("Store24.csv", sep=""))
View(store.df)summary(store.df)## 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. :100.002d
apply(store.df[,3:5],2,mean)## Profit MTenure CTenure
## 276313.61333 45.29644 13.93150apply(store.df[,3:5],2,sd)## Profit MTenure CTenure
## 89404.07634 57.67155 17.697522e & 2f
store_dec.df <- store.df[order(-store.df$Profit),]
store_dec.df[1:10,1:5]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550tail(store_dec.df,10)## store Sales Profit MTenure CTenure Pop Comp Visibility
## 37 37 1202917 187765 23.1985000 1.347023 8870 4.491863 3
## 61 61 716589 177046 21.8184200 13.305950 3014 3.263994 3
## 52 52 1073008 169201 24.1185600 3.416838 14859 6.585143 3
## 54 54 811190 159792 6.6703910 3.876797 3747 3.756011 3
## 13 13 857843 152513 0.6571813 1.577002 14186 4.435671 3
## 32 32 828918 149033 36.0792600 6.636550 9697 4.641468 3
## 55 55 925744 147672 6.6703910 18.365500 10532 6.389294 4
## 41 41 744211 147327 14.9180200 11.926080 9701 4.364600 2
## 66 66 879581 146058 115.2039000 3.876797 1046 6.569790 2
## 57 57 699306 122180 24.3485700 2.956879 3642 2.973376 3
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## 37 3 1 1 3.38 4.016667 73.68654
## 61 1 1 1 3.07 3.126667 73.68654
## 52 3 1 1 3.83 3.833333 94.73510
## 54 2 1 1 3.08 3.933333 65.78734
## 13 2 1 1 4.10 3.000000 76.30609
## 32 3 1 0 3.28 3.550000 73.68654
## 55 3 1 1 3.49 3.477778 76.31346
## 41 3 1 1 3.03 3.672222 81.13993
## 66 3 1 1 4.03 3.673333 80.26675
## 57 2 1 1 3.35 2.956667 84.21266task 2g
library(car)## Warning: package 'car' was built under R version 3.3.3scatterplot(store.df$MTenure ,store.df$Profit,
xlab = "MTenure", ylab = "Profit",
main = "Scatterplot of Profit vs. MTenure")
##2h
scatterplot(store.df$CTenure ,store.df$Profit,
xlab = "CTenure", ylab = "Profit",
main = "Scatterplot of Profit vs. CTenure")
## 2i
round(digits=2,cor(store.df))## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 1.00 -0.23 -0.20 -0.06 0.02 -0.29 0.03 -0.03
## Sales -0.23 1.00 0.92 0.45 0.25 0.40 -0.24 0.13
## Profit -0.20 0.92 1.00 0.44 0.26 0.43 -0.33 0.14
## MTenure -0.06 0.45 0.44 1.00 0.24 -0.06 0.18 0.16
## CTenure 0.02 0.25 0.26 0.24 1.00 0.00 -0.07 0.07
## Pop -0.29 0.40 0.43 -0.06 0.00 1.00 -0.27 -0.05
## Comp 0.03 -0.24 -0.33 0.18 -0.07 -0.27 1.00 0.03
## Visibility -0.03 0.13 0.14 0.16 0.07 -0.05 0.03 1.00
## PedCount -0.22 0.42 0.45 0.06 -0.08 0.61 -0.15 -0.14
## Res -0.03 -0.17 -0.16 -0.06 -0.34 -0.24 0.22 0.02
## Hours24 0.03 0.06 -0.03 -0.17 0.07 -0.22 0.13 0.05
## CrewSkill 0.05 0.16 0.16 0.10 0.26 0.28 -0.04 -0.20
## MgrSkill -0.07 0.31 0.32 0.23 0.12 0.08 0.22 0.07
## ServQual -0.32 0.39 0.36 0.18 0.08 0.12 0.02 0.21
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store -0.22 -0.03 0.03 0.05 -0.07 -0.32
## Sales 0.42 -0.17 0.06 0.16 0.31 0.39
## Profit 0.45 -0.16 -0.03 0.16 0.32 0.36
## MTenure 0.06 -0.06 -0.17 0.10 0.23 0.18
## CTenure -0.08 -0.34 0.07 0.26 0.12 0.08
## Pop 0.61 -0.24 -0.22 0.28 0.08 0.12
## Comp -0.15 0.22 0.13 -0.04 0.22 0.02
## Visibility -0.14 0.02 0.05 -0.20 0.07 0.21
## PedCount 1.00 -0.28 -0.28 0.21 0.09 -0.01
## Res -0.28 1.00 -0.09 -0.15 -0.03 0.09
## Hours24 -0.28 -0.09 1.00 0.11 -0.04 0.06
## CrewSkill 0.21 -0.15 0.11 1.00 -0.02 -0.03
## MgrSkill 0.09 -0.03 -0.04 -0.02 1.00 0.36
## ServQual -0.01 0.09 0.06 -0.03 0.36 1.002j
round(digits=2,cor(store.df$Profit,store.df$MTenure))## [1] 0.44round(digits=2,cor(store.df$Profit,store.df$CTenure))## [1] 0.262k
library(corrgram)## Warning: package 'corrgram' was built under R version 3.3.3corrgram(store.df, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Store Variables")
Managerially Relavant Correlations In the decreasing order of correlation value with the profit:
Sales>PedCount>MTenure>Pop>ServQual
2l
cor.test(store.df$Profit,store.df$MTenure)##
## Pearson's product-moment correlation
##
## data: store.df$Profit and store.df$MTenure
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692p-vlaue= 8.193e-05 .This implies that p-value is much smaller(<0.05).So we can easily reject the null hypothesis,This means Profit and MTenure have a relation.
cor.test(store.df$Profit,store.df$CTenure,method = "pearson")##
## Pearson's product-moment correlation
##
## data: store.df$Profit and store.df$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
## 0.2576789p-vlaue= 0.02562 .This implies that p-value is smaller(<0.05).So we can reject the null hypothesis,This means Profit and CTenure have a relation.
2m
fit <- lm(Profit ~ MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility, data = store.df)
summary(fit)##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = store.df)
##
## 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-122n
Explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05) are
-MTenure,CTenure,Comp,Pop,PedCount,Res,Hours24
Explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05)
-Visibility
17.What is expected change in the Profit at a store, if the Manager’s tenure i.e. number of months of experience with Store24, increases by one month?
answer- 760.993
18.What is expected change in the Profit at a store, if the Crew’s tenure i.e. number of months of experience with Store24, increases by one month?
Answer- 944.978
19. Executive Summary
Statistically speaking,
1.correlation between profit and manager’s tenure is 0.44
2.correlation between profit and crew’s tenure is 0.26.
This indicates that increase in tenure of Manager and Crew will lead to increase in Profit, but the Manager’s tenure has greater than Crew. So, Manager’s work experience is more important than of Crew at the store.
But at the same time, the beta value of Manager’s tenure is less than that of Crew’s Tenure. This suggests that both Manager and Crew are assets to the store, but greater priority goes to the manager becuase of the greater correlation value.