Store24
Harsh Sandhu
December 18, 2017
setwd("C:/Users/harsh/Desktop/r")
store.df<- read.csv("Store24.csv")Mean Profit and its Standard Deviation
mean(store.df$Profit)## [1] 276313.6sd(store.df$Profit)## [1] 89404.08Mean Manager Tenure and the Standard Deviation
mean(store.df$MTenure )## [1] 45.29644sd(store.df$MTenure)## [1] 57.67155Mean Crew Tenure and the Standard Deviation
mean(store.df$CTenure)## [1] 13.9315sd(store.df$CTenure)## [1] 17.69752First Ten Most Profitable Stores
newdata <-store.df[order(-store.df$Profit),]
newdata[1:10,1:5]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550Bottom 10 Least profitable stores
newdata <- store.df[order(store.df$Profit ),]
newdata[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 1.347023Drawing a scatter plot of Profit vs. CTenure
library(car)
scatterplot(store.df$CTenure, store.df$Profit, xlab = "CTenure", ylab = "Profit", main = "CTenure vs Profit")
Drawing a scatter plot of Profit vs. MTenure
library(car)
scatterplot(store.df$MTenure, store.df$Profit, xlab = "MTenure", ylab = "Profit", main = "MTenure vs Profit")
Constructing a Correlation Matrix for all the variables in the dataset.
correlationmatrix <-cor(store.df)
round(correlationmatrix,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 1.00correlation between Profit and MTenure.
corPMtenure <- cor(store.df$Profit,store.df$MTenure)
round(corPMtenure , digits = 2)## [1] 0.44correlation between Profit and CTenure.
corPCtenure <- cor(store.df$Profit,store.df$CTenure)
round(corPCtenure , digits = 2)## [1] 0.26Corrgram based on store variables
library(corrgram)
corrgram(store.df,upper.panel = panel.pie, main="Store Corrgram of all the intercorrealtions ")
Pearson’s correlation test on correlation between Profit and MTenure , and 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
## 0.4388692The p-value is: 8.193e-05
Pearson’s correlation test on correlation between Profit and CTenure , and 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
## 0.2576789The p-value is: 0.02562
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-12Coefficients
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 12625.447050Model : 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 30769.464999List of explanatory variables whose beta coefficients are statistically significant
1)MTenure 2)CTenure 3)Comp 4)Pop 5)PedCount 6)Res 7)Hours24
List of explanatory variables whose beta coefficients are not statistically significant
1)Visibility
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-05Model : Profit = b0 + b1*MTenure b0= 245496.3 , b1= 680.3 So, There is an expected increase of 680.3 units of Profit for an increase of a month’s experience of the managers.
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: 0.02562Model : Profit = b0 + b1*CTenure b0= 258178.4 , b1= 1301.7 So, There is an expected increase of 1301.7 units of Profit for an increase of a month’s experience of the Crew at Store24.
Executive Summary
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. More the population around the store, more the profit. More the number of stores, more the profit. There exists a positive correlation with the manager’s tenure and profit and sales. The positive correlation between crew’s tenure and profit and sales is less than that of the correlation in manager’s case.