Managing Employee Retention
Sai Sandeep MN
15 January 2018
Task 4c
Using R, read the data into a data frame called store. Play close attention to Exhibit 3 - Summary Statistics from Sample Stores from the CASE. Using R, get the summary statistics of the data. Confirm that the summary statistics generated from R are consistent with Exhibit 3 from the Case.
View(stores.df)
summary(stores.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(stores.df[,2:11])[,c(1:4,8,9)]## vars n mean sd min max
## Sales 1 75 1205413.12 304531.31 699306.00 2113089.00
## Profit 2 75 276313.61 89404.08 122180.00 518998.00
## MTenure 3 75 45.30 57.67 0.00 277.99
## CTenure 4 75 13.93 17.70 0.89 114.15
## Pop 5 75 9825.59 5911.67 1046.00 26519.00
## Comp 6 75 3.79 1.31 1.65 11.13
## Visibility 7 75 3.08 0.75 2.00 5.00
## PedCount 8 75 2.96 0.99 1.00 5.00
## Res 9 75 0.96 0.20 0.00 1.00
## Hours24 10 75 0.84 0.37 0.00 1.00Task 4d:
- Use R to measure the mean and standard deviation of Profit. =>mean
mean(stores.df$Profit)## [1] 276313.6=>standard deviation
sd(stores.df$Profit)## [1] 89404.08- Use R to measure the mean and standard deviation of MTenure. =>mean
mean(stores.df$MTenure)## [1] 45.29644=>standard deviation
sd(stores.df$MTenure)## [1] 57.671553)Use R to measure the mean and standard deviation of CTenure. =>mean
mean(stores.df$CTenure)## [1] 13.9315=>standard deviation
sd(stores.df$CTenure)## [1] 17.69752Task 4e: Sorting and Subsetting data in R
attach(mtcars)
View(mtcars)
newdata <- mtcars[order(mpg),]
View(newdata)
newdata[1:5,]## 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),]
View(newdata)
detach(mtcars)Task 4f: Replicate Exhibit 1 shown in the case, using R
- Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.
topten.df <- stores.df[order(-stores.df$Profit),]
topten.df[1:10,1:5,]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550- Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.
leastprofitable <- stores.df[order(stores.df$Profit),]
leastprofitable[1:10,1:5,]## 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 4g: Scatter Plots
- Use R to draw a scatter plot of Profit vs. MTenure.
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(stores.df$Profit ~ stores.df$MTenure , main="Profit vs MTenure Scatterplot", xlab="MTenure", ylab="Profit")
Task 4h: Scatter Plots (contd.)
- Use R to draw a scatter plot of Profit vs. CTenure.
library(car)
scatterplot(stores.df$Profit ~ stores.df$CTenure , main="Profit vs CTenure Scatterplot", xlab="CTenure", ylab="Profit")
Task 4i: Correlation Matrix
- Use R to construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)
cor <- cor(stores.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: Correlations
- Use R to measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)
round(cor(stores.df$Profit, stores.df$MTenure),2)## [1] 0.44- Use R to measure the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)
round(cor(stores.df$Profit, stores.df$CTenure),2)## [1] 0.26Task 4k:
- Use R to construct the following Corrgram based on all variables in the dataset.
library(corrgram)
corrgram(stores.df,lower.panel = panel.shade,upper.panel=panel.pie,text.panel = panel.txt,main="Corrgram of Store Variables")
#Task 4l: Pearson’s Correlation Tests 12) Run a Pearson’s Correlation test on the correlation between Profit and MTenure. What is the p-value?
cor.test(stores.df$Profit,stores.df$MTenure)##
## Pearson's product-moment correlation
##
## data: stores.df$Profit and stores.df$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=> p value is 8.193e-05
13)Run a Pearson’s Correlation test on the correlation between Profit and CTenure. What is the p-value?
cor.test(stores.df$Profit,stores.df$CTenure)##
## Pearson's product-moment correlation
##
## data: stores.df$Profit and stores.df$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=> p value is 0.02562
Task 3m: Regression Analysis
- Run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.
summary(lm(Profit~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility, data=stores.df))##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = stores.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-12Task: 4n:
15)List the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05). => Explanatory variable(s) whose beta-coefficients are not statistically significant are MTenure, CTenure, Pop, PedCount, Res, Hours24, Comp
- List the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05). => Explanatory variable whose beta coffecient is statistically not significant is Visibility #Task 40:
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? => 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 = $810.971201
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? => 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 = $1016.017324
Task 4p: Executive Summary
Please prepare an “Executive Summary”. Please add this to the end of your Rmd file. Specifically, please create a qualitative summary of Managerial Insights, based on your data analysis, especially your Regression Analysis. You may write this in paragraph form or in point form. => So 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. Therefore 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. Also we can say from the regression analysis that the an increase in Manager’s experience with Store24 can increase the store’s profit by USD 760.993.
And we can say from the regression analysis that the an increase in Crew’s experience with Store24 can increase the store’s profit by USD 944.978.