Managing Employee Retention
Dheepika C
12 February 2018
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. :100.00library(psych)
describe(store.df)## vars n mean sd median trimmed mad
## store 1 75 38.00 21.79 38.00 38.00 28.17
## Sales 2 75 1205413.12 304531.31 1127332.00 1182031.25 288422.04
## Profit 3 75 276313.61 89404.08 265014.00 270260.34 90532.00
## MTenure 4 75 45.30 57.67 24.12 33.58 29.67
## CTenure 5 75 13.93 17.70 7.21 10.60 6.14
## Pop 6 75 9825.59 5911.67 8896.00 9366.07 7266.22
## Comp 7 75 3.79 1.31 3.63 3.66 0.82
## Visibility 8 75 3.08 0.75 3.00 3.07 0.00
## PedCount 9 75 2.96 0.99 3.00 2.97 1.48
## Res 10 75 0.96 0.20 1.00 1.00 0.00
## Hours24 11 75 0.84 0.37 1.00 0.92 0.00
## CrewSkill 12 75 3.46 0.41 3.50 3.47 0.34
## MgrSkill 13 75 3.64 0.41 3.59 3.62 0.45
## ServQual 14 75 87.15 12.61 89.47 88.62 15.61
## min max range skew kurtosis se
## store 1.00 75.00 74.00 0.00 -1.25 2.52
## Sales 699306.00 2113089.00 1413783.00 0.71 -0.09 35164.25
## Profit 122180.00 518998.00 396818.00 0.62 -0.21 10323.49
## MTenure 0.00 277.99 277.99 2.01 3.90 6.66
## CTenure 0.89 114.15 113.26 3.52 15.00 2.04
## Pop 1046.00 26519.00 25473.00 0.62 -0.23 682.62
## Comp 1.65 11.13 9.48 2.48 11.31 0.15
## Visibility 2.00 5.00 3.00 0.25 -0.38 0.09
## PedCount 1.00 5.00 4.00 0.00 -0.52 0.11
## Res 0.00 1.00 1.00 -4.60 19.43 0.02
## Hours24 0.00 1.00 1.00 -1.82 1.32 0.04
## CrewSkill 2.06 4.64 2.58 -0.43 1.64 0.05
## MgrSkill 2.96 4.62 1.67 0.27 -0.53 0.05
## ServQual 57.90 100.00 42.10 -0.66 -0.72 1.46Task 4d
- Measure the mean and standard deviation of Profit
mean(store.df$Profit)## [1] 276313.6sd(store.df$Profit)## [1] 89404.08- Measure the mean and standard deviation of MTenure
mean(store.df$MTenure)## [1] 45.29644sd(store.df$MTenure)## [1] 57.67155- Measure the mean and standard deviation of CTenure
mean(store.df$CTenure)## [1] 13.9315sd(store.df$CTenure)## [1] 17.69752TASK 4f
- Print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores
attach(store.df)
newdata <- store.df[order(-Profit),]
View(newdata)
newdata[1:10,c("store","Sales","Profit","MTenure","CTenure")]## 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- Print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores
attach(store.df)## The following objects are masked from store.df (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibilitynewdata <- store.df[order(Profit),]
View(newdata)
newdata[10:1,c("store","Sales","Profit","MTenure","CTenure")]## store Sales Profit MTenure CTenure
## 37 37 1202917 187765 23.1985000 1.347023
## 61 61 716589 177046 21.8184200 13.305950
## 52 52 1073008 169201 24.1185600 3.416838
## 54 54 811190 159792 6.6703910 3.876797
## 13 13 857843 152513 0.6571813 1.577002
## 32 32 828918 149033 36.0792600 6.636550
## 55 55 925744 147672 6.6703910 18.365500
## 41 41 744211 147327 14.9180200 11.926080
## 66 66 879581 146058 115.2039000 3.876797
## 57 57 699306 122180 24.3485700 2.956879TASK 4g
- Draw a scatter plot of Profit vs. MTenure
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(store.df$MTenure,store.df$Profit,main = "Scatterplot of Profit Vs MTenure",xlab = "MTenure",ylab = "Profit",lwd = 2,smoother = FALSE)
lw1 <- loess(Profit~MTenure,data = store.df)
j <- order(store.df$MTenure)
lines(store.df$MTenure[j],lw1$fitted[j],col="red",lwd = 2,lty="dashed")
Task 4h
- Draw a scatter plot of Profit vs. CTenure.
library(car)
scatterplot(store.df$CTenure,store.df$Profit,main = "Scatterplot of Profit Vs CTenure",xlab = "CTenure",ylab = "Profit",lwd = 2,smoother = FALSE)
lw1 <- loess(Profit~CTenure,data = store.df)
j <- order(store.df$CTenure)
lines(store.df$CTenure[j],lw1$fitted[j],col="red",lwd = 2,lty="dashed")
Task 4i
- Construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)
cor <- cor(store.df)
round(cor,2)## 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 1.00Task 4j
9.Measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)
round(cor(Profit,MTenure),2)## [1] 0.4410.Measure the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)
round(cor(Profit,CTenure),2)## [1] 0.26Task 4k
- Construct the following Corrgram based on all variables in the dataset
library(corrgram)
corrgram(store.df,lower.panel = panel.shade,upper.panel=panel.pie,text.panel = panel.txt,main="Corrgram of Store Variables")
Task 4l
- Run a Pearson’s Correlation test on the correlation between Profit and MTenure. What is the p-value?
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
## 0.4388692The p-value is very very small(8.193e-05).
- Run a Pearson’s Correlation test on the correlation between Profit and CTenure. What is the p-value?
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
## 0.2576789The p-value is small(0.02562).
TASK 4m
- Run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}
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-12Task 4n
List the explanatory variable(s) whose beta-coefficients are statistically significant(p < 0.05). 1.MTenure: Average manager tenure during FY-2000 where tenure is defined as the number of months of experience with Store24. 2.CTenure: Average crew tenure during FY-2000 where tenure is defined as the number of months of experience with Store24. 3.PedCount: 5-point rating on pedestrian foot traffic volume with 5 being the highest 4.CrewSkill: Skill of Crew in the store (rating out of 5) 5.MgrSkill: Skill of Manager in the store (rating out of 5) 6.ServQual: Service Quality
List the explanatory variable(s) whose beta-coefficients are not statistically significant(p > 0.05). 1.Visibility: 5-point rating on visibility of store front with 5 being the highest 2.Pop: Population within a ½ mile radius
Task 4o
- 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?
model$coefficients[2]## MTenure
## 760.9927- What is expected change in the Profit at a store, if the Crew’s tenure i.e. number of months of experience with Store24, increases by one month?
model$coefficients[3]## CTenure
## 944.978Task 4p
- Executive Summary: From the above corrgram obtained we can say that Profit is correlated to MTenure, CTenure, Pop, CrewSkill, MgrSkill and ServQual. We can see that the skills of manager and the crew of a store is tightly correlated to the Profit. The MTenure and CTenure is more tightly correlated to the Profit of the store. So retaining a Manager and Crew Member is very much Important. Thus We can say that retaining an Employee and a Manager is very much important in making profits. Obviously as the Sales increses profits also increase which can be seen though the corrgram above. The Visibilty is weakly correlated with the Profit as the Store24 is itself a brand and we don’t need to care about Visibility. We can say from the regression analysis that 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 an increase in Crew’s experience with Store24 can increase the store’s profit by USD 944.978.