Store24
Mahita Ajmera
December 17, 2017
Summary
store <- read.csv(paste("Store24.csv", sep=""))
library(psych)
summary(store)## 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. :100.00Mean and Standard Deviation
Mean of Profit: 276313.6
Standard deviation of Profit: 89404.07634
Mean of MTenure: 45.29644
Standard deviation of MTenure: 57.6155
Mean of CTenure: 13.9315
Standard deviation of CTenure: 17.69752
mean(store$Profit)## [1] 276313.6mean(store$MTenure)## [1] 45.29644mean(store$CTenure)## [1] 13.9315apply(store[,3:5],2,sd) ## Profit MTenure CTenure
## 89404.07634 57.67155 17.69752Most Profitable Stores & Least Profitable Stores
The 10 Most Profitable Stores:
topmost<- store[order(-store$Profit),]
View(topmost)
topmost[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 6.636550The 10 Least Profitable Stores:
bottomleast<- store[order(store$Profit),]
View(bottomleast)
bottomleast[1:10,1:5] ## store Sales Profit MTenure CTenure
## 57 57 699306 122180 24.3485700 2.956879
## 66 66 879581 146058 115.2039000 3.876797
## 41 41 744211 147327 14.9180200 11.926080
## 55 55 925744 147672 6.6703910 18.365500
## 32 32 828918 149033 36.0792600 6.636550
## 13 13 857843 152513 0.6571813 1.577002
## 54 54 811190 159792 6.6703910 3.876797
## 52 52 1073008 169201 24.1185600 3.416838
## 61 61 716589 177046 21.8184200 13.305950
## 37 37 1202917 187765 23.1985000 1.347023Scatter Plots
A scatter plot of Profit vs. MTenure
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit scatterplot(store$Profit ~ store$MTenure,
xlab="MTenure", ylab="Profit",
main="Scatter Plot of Profit Vs MTenure",
labels=row.names(store)) 
A scatter plot of Profit vs. CTenure
scatterplot(store$Profit ~ store$CTenure,
xlab="CTenure", ylab="Profit",
main="Scatter Plot of Profit Vs CTenure",
labels=row.names(store)) 
Correlation Matrix
round (cor(store), digits = 2)## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 1.00 -0.23 -0.20 -0.06 0.02 -0.29 0.03 -0.03
## Sales -0.23 1.00 0.92 0.45 0.25 0.40 -0.24 0.13
## Profit -0.20 0.92 1.00 0.44 0.26 0.43 -0.33 0.14
## MTenure -0.06 0.45 0.44 1.00 0.24 -0.06 0.18 0.16
## CTenure 0.02 0.25 0.26 0.24 1.00 0.00 -0.07 0.07
## Pop -0.29 0.40 0.43 -0.06 0.00 1.00 -0.27 -0.05
## Comp 0.03 -0.24 -0.33 0.18 -0.07 -0.27 1.00 0.03
## Visibility -0.03 0.13 0.14 0.16 0.07 -0.05 0.03 1.00
## PedCount -0.22 0.42 0.45 0.06 -0.08 0.61 -0.15 -0.14
## Res -0.03 -0.17 -0.16 -0.06 -0.34 -0.24 0.22 0.02
## Hours24 0.03 0.06 -0.03 -0.17 0.07 -0.22 0.13 0.05
## CrewSkill 0.05 0.16 0.16 0.10 0.26 0.28 -0.04 -0.20
## MgrSkill -0.07 0.31 0.32 0.23 0.12 0.08 0.22 0.07
## ServQual -0.32 0.39 0.36 0.18 0.08 0.12 0.02 0.21
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store -0.22 -0.03 0.03 0.05 -0.07 -0.32
## Sales 0.42 -0.17 0.06 0.16 0.31 0.39
## Profit 0.45 -0.16 -0.03 0.16 0.32 0.36
## MTenure 0.06 -0.06 -0.17 0.10 0.23 0.18
## CTenure -0.08 -0.34 0.07 0.26 0.12 0.08
## Pop 0.61 -0.24 -0.22 0.28 0.08 0.12
## Comp -0.15 0.22 0.13 -0.04 0.22 0.02
## Visibility -0.14 0.02 0.05 -0.20 0.07 0.21
## PedCount 1.00 -0.28 -0.28 0.21 0.09 -0.01
## Res -0.28 1.00 -0.09 -0.15 -0.03 0.09
## Hours24 -0.28 -0.09 1.00 0.11 -0.04 0.06
## CrewSkill 0.21 -0.15 0.11 1.00 -0.02 -0.03
## MgrSkill 0.09 -0.03 -0.04 -0.02 1.00 0.36
## ServQual -0.01 0.09 0.06 -0.03 0.36 1.00round(cor(store$Profit,store$MTenure),digits = 2)## [1] 0.44round(cor(store$Profit,store$CTenure),digits = 2)## [1] 0.26The correlation between Profit and MTenure = 0.44
The correlation between Profit and CTenure = 0.26
Corrgram based on all variables in the dataset:
library(corrgram)
corrgram(store, order=FALSE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Store Variables")
Managerially relevant correlations:
Profit is directly proportionate to MTenure and CTenure. This means that experienced managers and crew lead to an increase in profit.
Profit is inversely proportionate to competition. When there is more competition for a particular product, sales decrease thus affecting profits.
Profit and population are directly proportionate. An increase in people leads to an increase in sales which leads to more profit.
Profit can be increased by working 24*7.
Pearson’s Correlation Tests
Test on the correlation between Profit and MTenure
p-value: 8.193e-05
Thus, we neglect the null hypothesis since the p value < 0.05. There exists a relation between Profit and MTenure.
cor.test(store$Profit,store$MTenure,method = "pearson")##
## Pearson's product-moment correlation
##
## data: store$Profit and store$MTenure
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692Test on the correlation between Profit and CTenure
p-value: 0.02562
Thus, we neglect the null hypothesis since the p value < 0.05. There exists a relation between Profit and CTenure.
cor.test(store$Profit,store$CTenure,method = "pearson")##
## Pearson's product-moment correlation
##
## data: store$Profit and store$CTenure
## t = 2.2786, df = 73, p-value = 0.02562
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03262507 0.45786339
## sample estimates:
## cor
## 0.2576789Regression Analysis
reg<-lm(Profit ~MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility, data = store)
reg##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = store)
##
## Coefficients:
## (Intercept) MTenure CTenure Comp Pop
## 7610.041 760.993 944.978 -25286.887 3.667
## PedCount Res Hours24 Visibility
## 34087.359 91584.675 63233.307 12625.447The explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05):
MTenure
CTenure
Comp
Pop
PedCount
summary(reg)$coef[summary(reg)$coef[,4] <= .05, 4]## MTenure CTenure Comp Pop PedCount
## 9.715897e-08 2.839955e-02 1.938381e-05 1.489046e-02 3.664408e-04
## Res Hours24
## 2.262320e-02 1.993586e-03The explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05):
Intercept
Visibility
summary(reg)$coef[summary(reg)$coef[,4] > .05, 4]## (Intercept) Visibility
## 0.9096745 0.1694106Q. What is expected change in the Profit at a store, if the Manager’s tenure i.e. number of months of experience with Store24, increases by one month?
A. The expected change in the Profit at a store, if the Manager’s tenure increases by one month is 760.99 dollars
round(summary(reg)$coefficients["MTenure",1], digits=2)## [1] 760.99Q. 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?
A. The expected change in the Profit at a store, if the Crew’s tenure increases by one month is 944.98 dollars
round(summary(reg)$coefficients["CTenure",1], digits=2)## [1] 944.98Executive Summary
Based on the regression analysis, we can reject the null hypothesis because of the p value. Thus, we know that there is a relation between tenure of manager, tenure of the crew and profits.
From this, we gain that managers should have incentives for employees such as bonuses and should also invest in their employees through skill development programs. This can help in increasing the tenure of the crew.
We can see that tenure of a manager has greater impact on profits than tenure of the crew. Hence, effective policies should be in place in order to retain managers and increase their tenure in order to increase their profits.