manager employee retention
Pooja Gundu
December 18, 2017
TASK-2a
->List of most important questions from the Managing Employee Retention Case:
What are the basis on which the strategies are employed, for increasing store level employees retention?
How are the several variables efffecting the store success?
What is the relationship between each of these variables ,on whole, and the Profit?
What is the financial impact of increased tenure ? How is it related( linear or non-linear)?
How is the increased financial status useful in stategizing the method for increasing tenure?
TASK-2b
->Answering questions through the Exhibit 2
A firm’s performance depends wholly on the efficiency of the people’s performance.( i.e , people factors). Efficiency of people’s performance goes up gradually by increased tenure. In this case, the tenure can be increased by increasing wages, by promising insurances , reduced mechanical working ,etc. Instituting new training programs , developing a career development program , implementing bonuses helps in the personality development of an individual along with technical bonuses and fascinates them towards thier work increasing the tenure , furthermore increasing the store success.
TASK-2c
->Reading the data and summary ststistics of data
–reading the data
store.df <- read.csv(paste("Store24.csv", sep=""))
View(store.df)–summary statistics of data
library(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-2d
1- Mean and sd of Profit respectively
mean(store.df$Profit)## [1] 276313.6sd(store.df$Profit)## [1] 89404.082-Mean and sd of MTenure respectively
mean(store.df$MTenure)## [1] 45.29644sd(store.df$MTenure)## [1] 57.671553-Mean and sd of CTenure respectively
mean(store.df$CTenure)## [1] 13.9315sd(store.df$CTenure)## [1] 17.69752TASK-2f
4-Top 10 most profitable stores
attach(store.df)
newdata1 <- store.df[order(- Profit),1:5]
newdata1[1:10,]## 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 6.6365505- Bottom 10 least profitable stores
newdata2 <- store.df[order(Profit),1:5]
newdata2[1:10 ,]## 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 1.347023TASK-2g
6-Scatter plot of Profit Vs MTenure
plot(Profit~MTenure , data=store.df , main="Scatterplot of Profit Vs MTenure" )
abline(lm(Profit ~ MTenure, data=store.df ))
TASK-2h
7-Scatter plot of Profit Vs CTenure
plot(Profit~CTenure , data=store.df , main="Scatterplot of Profit Vs CTenure" )
abline(lm(Profit ~ CTenure, data=store.df ))
TASK-2i
8-Correlation Matrix of all the variables in the dataset
–Correlation
round(cor(store.df[ , ]),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 1.00–Visualizing correlation matrix
library(corrplot) ## corrplot 0.84 loadedcorrplot(corr=cor(store.df[ , ], use="complete.obs"),
method ="ellipse")
TASK-2j
9-Measuring correlation between Profit and MTenure
cor(store.df$Profit , store.df$MTenure )## [1] 0.4388692round(cor(store.df$Profit , store.df$MTenure ),2)## [1] 0.4410-Measuring correlation between Profit and CTenure
cor(store.df$Profit , store.df$CTenure )## [1] 0.2576789round(cor(store.df$Profit , store.df$CTenure ),2)## [1] 0.26TASK-2k
11-Corrgram based on all variables in the dataset
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowesscorrplot.mixed(corr =cor(store.df[ , ]) , lower="color" , upper="pie")
- From the corrogram achieved it can be concluded clearly that the Profit has a higher impact on the sales i.e , strongly correlated and also the Profit has a least impact on the Comp(Number of competitors per 10,000 people within a 1/2 mile radius) i.e , weakly correlated.
- For financial profits or for the store success , a strong observation on the trend of sales and also the number of competitors for cunter-attack , is desired . The variables effecting sales in a way show great impact on the profit , so, corresponding strategies encouraged to produce higher sales (through the relatable variables) is quite advisable.
TASK-2l
12- Pearson’s correlation test on correlation between Profit and MTenure , its p-value
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
## 0.4388692(cor.test(store.df$Profit , store.df$MTenure ))$p.value ## [1] 8.193133e-05-The p-value is: 8.193133e-05
13-Pearson’s correlation test on correlation between Profit and CTenure , its p-value
cor.test(store.df$Profit , store.df$CTenure )##
## Pearson's product-moment correlation
##
## data: store.df$Profit and store.df$CTenure
## t = 2.2786, df = 73, p-value = 0.02562
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03262507 0.45786339
## sample estimates:
## cor
## 0.2576789(cor.test(store.df$Profit , store.df$CTenure ))$p.value## [1] 0.0256203-The p-value is: 0.0256203
TASK-2m
14-Regression analysis on Profit
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-12–Coefficients
fit$coefficients## (Intercept) MTenure CTenure Comp Pop
## 7610.041452 760.992734 944.978026 -25286.886662 3.666606
## PedCount Res Hours24 Visibility
## 34087.358789 91584.675234 63233.307162 12625.447050-Model : Profit = b0 + b1MTenure + b2CTenure + b3Comp + b4Pop + b5PedCount + b6Res + b7Hours24 + b8Visibility
- b0=7610.041452 ,b1= 760.992734 ,b2= 944.978026 ,b3= -25286.886662 ,b4= 3.666606 ,b5= 34087.358789 ,b6= 91584.675234 ,b7= 63233.307162
,b8= 12625.447050
–Confidence Interval ( 95% by default)
confint(fit)## 2.5 % 97.5 %
## (Intercept) -1.258044e+05 141024.457560
## MTenure 5.072581e+02 1014.727399
## CTenure 1.030519e+02 1786.904132
## Comp -3.625189e+04 -14321.880698
## Pop 7.390282e-01 6.594184
## PedCount 1.597214e+04 52202.579289
## Res 1.325689e+04 169912.458917
## Hours24 2.401856e+04 102448.057104
## Visibility -5.518571e+03 30769.464999TASK-2n
15- List of explanatory variables whose beta coefficients are statistically significant
1)MTenure- Average manager tenure
2)CTenure- Average crew tenure
3)Comp- Number of competitors per 10,000 people within a 1???2 mile radius
4)Pop- Population within a 1???2 mile radius
5)PedCount- 5-point rating on pedestrian foot traffic volume with 5 being the highest
6)Res- Indicator for located in residential vs. industrial area
7)Hours24- Indicator for open 24 hours or not
16-List of explanatory variables whose beta coefficients are not statistically significant
1)Visibility-5-point rating on visibility of store front with 5 being the highest
17-Expected change in Profit at a store if the manager’s tenure increases by one month
fit1<- lm(Profit~MTenure, data=store.df)
summary (fit1)##
## Call:
## lm(formula = Profit ~ MTenure, data = store.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177817 -52029 -8635 50871 188316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 245496.3 11906.4 20.619 < 2e-16 ***
## MTenure 680.3 163.0 4.173 8.19e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80880 on 73 degrees of freedom
## Multiple R-squared: 0.1926, Adjusted R-squared: 0.1815
## F-statistic: 17.41 on 1 and 73 DF, p-value: 8.193e-05-Model : Profit = b0 + b1*MTenure
-b0= 245496.3 , b1= 680.3
-Model : Profit = 245496.3 + 680.3 *MTenure
- The regression coefficient (680.3) is statistically significant (p<0.001)
-There is an expected increase of 680.3 units of Profit for an increase of a month’s experience of the managers.
18-Expected change in Profit at a store if the Crew’s tenure increases by one month
fit2<- lm(Profit~CTenure, data=store.df)
summary (fit2)##
## Call:
## lm(formula = Profit ~ CTenure, data = store.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -139848 -64869 -9022 45057 222393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258178.4 12814.4 20.148 <2e-16 ***
## CTenure 1301.7 571.3 2.279 0.0256 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 86970 on 73 degrees of freedom
## Multiple R-squared: 0.0664, Adjusted R-squared: 0.05361
## F-statistic: 5.192 on 1 and 73 DF, p-value: 0.02562-Model : Profit = b0 + b1*CTenure
-b0= 258178.4 , b1= 1301.7
-Model : Profit = 258178.4 + 1301.7*MTenure
- The regression coefficient (1301.7) is statistically significant (p<0.05)
-There is an expected increase of 1301.7 units of Profit for an increase of a month’s experience of the Crew at Store24.
19-Executive Summary
-> The store success is directly related on the amount of profit obtained at the store.
-> The Profit is inter-related with all the variables , but the strength of the correlation between any two individual variables should be observed carefully for deciding the Profit.
-> The strength of the several variables with the Profit are listed as follows ranging from strong correlation to the weaker correlation based on the correlation matrix and the corrogram :
-Sales
-Pedestrain foot traffic volume rating
-Manager's Tenure
-Population with a 1/2 mile radius
-Crew's Tenure
-Visibility of store front rating
-Indicator for open 24 hours or not
-Residential or industrial location
-Number of competetors per 10000 people within a 1/2 mile radius
-> From the regression analysis , the beta coefficients obtained help in determining whether a particular variable is positively related to the profit or negatively related.
-> From the results, the number of competetors has a dicremental effect on the Profit.
-> A greater increase in Profit is observed by increasing Crew’s tenure rather than the Manager’s tenure.So, Crew’s tenure increased by increasing wages, by promising insurances , reduced mechanical working ,etc.