Store24
Rahul Poddar
15 January 2018
Summary of Data
describe(store)## 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 1.46Mean and Standard Deviation
Mean of Profit
mean(store$Profit)## [1] 276313.6Standard Deviation of Profit
sd(store$Profit)## [1] 89404.08Mean of MTenure
mean(store$MTenure)## [1] 45.29644Standard Deviation of MTenure
sd(store$MTenure)## [1] 57.67155Mean of CTenure
mean(store$CTenure)## [1] 13.9315Standard Deviation of CTenure
sd(store$CTenure)## [1] 17.69752Most Profitable
top<-store[order(-store$Profit),]
head(top[,1:5],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 6.636550Least Profitable
tail(top[,1:5],10)## 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 2.956879Scatterplot of Profit vs Mtenure
scatterplot(Profit~ MTenure, data=store, main="Scatterplot of Profit vs MTenure")
Scatterplot of Profit vs Ctenure
scatterplot(Profit~ CTenure, data=store, main="Scatterplot of Profit vs CTenure")
Correllation of Variables
cor1<-cor(store[, c(1:14)])
format(round(cor1, 2), nsmall = 2)## store Sales Profit MTenure CTenure Pop Comp
## store " 1.00" "-0.23" "-0.20" "-0.06" " 0.02" "-0.29" " 0.03"
## Sales "-0.23" " 1.00" " 0.92" " 0.45" " 0.25" " 0.40" "-0.24"
## Profit "-0.20" " 0.92" " 1.00" " 0.44" " 0.26" " 0.43" "-0.33"
## MTenure "-0.06" " 0.45" " 0.44" " 1.00" " 0.24" "-0.06" " 0.18"
## CTenure " 0.02" " 0.25" " 0.26" " 0.24" " 1.00" " 0.00" "-0.07"
## Pop "-0.29" " 0.40" " 0.43" "-0.06" " 0.00" " 1.00" "-0.27"
## Comp " 0.03" "-0.24" "-0.33" " 0.18" "-0.07" "-0.27" " 1.00"
## Visibility "-0.03" " 0.13" " 0.14" " 0.16" " 0.07" "-0.05" " 0.03"
## PedCount "-0.22" " 0.42" " 0.45" " 0.06" "-0.08" " 0.61" "-0.15"
## Res "-0.03" "-0.17" "-0.16" "-0.06" "-0.34" "-0.24" " 0.22"
## Hours24 " 0.03" " 0.06" "-0.03" "-0.17" " 0.07" "-0.22" " 0.13"
## CrewSkill " 0.05" " 0.16" " 0.16" " 0.10" " 0.26" " 0.28" "-0.04"
## MgrSkill "-0.07" " 0.31" " 0.32" " 0.23" " 0.12" " 0.08" " 0.22"
## ServQual "-0.32" " 0.39" " 0.36" " 0.18" " 0.08" " 0.12" " 0.02"
## Visibility PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store "-0.03" "-0.22" "-0.03" " 0.03" " 0.05" "-0.07" "-0.32"
## Sales " 0.13" " 0.42" "-0.17" " 0.06" " 0.16" " 0.31" " 0.39"
## Profit " 0.14" " 0.45" "-0.16" "-0.03" " 0.16" " 0.32" " 0.36"
## MTenure " 0.16" " 0.06" "-0.06" "-0.17" " 0.10" " 0.23" " 0.18"
## CTenure " 0.07" "-0.08" "-0.34" " 0.07" " 0.26" " 0.12" " 0.08"
## Pop "-0.05" " 0.61" "-0.24" "-0.22" " 0.28" " 0.08" " 0.12"
## Comp " 0.03" "-0.15" " 0.22" " 0.13" "-0.04" " 0.22" " 0.02"
## Visibility " 1.00" "-0.14" " 0.02" " 0.05" "-0.20" " 0.07" " 0.21"
## PedCount "-0.14" " 1.00" "-0.28" "-0.28" " 0.21" " 0.09" "-0.01"
## Res " 0.02" "-0.28" " 1.00" "-0.09" "-0.15" "-0.03" " 0.09"
## Hours24 " 0.05" "-0.28" "-0.09" " 1.00" " 0.11" "-0.04" " 0.06"
## CrewSkill "-0.20" " 0.21" "-0.15" " 0.11" " 1.00" "-0.02" "-0.03"
## MgrSkill " 0.07" " 0.09" "-0.03" "-0.04" "-0.02" " 1.00" " 0.36"
## ServQual " 0.21" "-0.01" " 0.09" " 0.06" "-0.03" " 0.36" " 1.00"Correlation b/w Profit and Mtenure
cor2<-cor(store$Profit,store$MTenure)
format(round(cor2,2),nsmall = 2)## [1] "0.44"Correlation b/w Profit and Ctenure
cor2<-cor(store$Profit,store$CTenure)
format(round(cor2,2),nsmall = 2)## [1] "0.26"Corrgram of Variables
corrgram(store, order=FALSE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of store variables")
Pearson’s Correlation Tests
For Profits and MTenure
table1<-xtabs(~Profit+MTenure, data=store)
chisq.test(table1)##
## Pearson's Chi-squared test
##
## data: table1
## X-squared = 4425, df = 4366, p-value = 0.2625For Profits and CTenure
table2<-xtabs(~Profit+CTenure, data=store)
chisq.test(table1)##
## Pearson's Chi-squared test
##
## data: table1
## X-squared = 4425, df = 4366, p-value = 0.2625Regression Analysis
reg<-lm(formula = Profit~ MTenure+ CTenure +Comp+ Pop+ PedCount+ Res+ Hours24+ Visibility,data = store)
summary(reg)##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = store)
##
## 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-12The explanatory variables whose beta-coefficients are statistically significant (p < 0.05) include:
- MTenure 2. CTenure 3. Comp 4. Pop 5. PedCount 6. Res 7. Hours24
The explanatory variables whose beta-coefficients are statistically significant (p < 0.05) include:
- Visibility
The expected change in Profits with every one month increase in Manager’s Tenure is Rs 760.993 millions.
The expected change in Profits with every one month increase in Crew’s Tenure is Rs 944.978 millions.
Executive Summary
From the above analysis, light can be put upon some facts.
The mean tenure of managers in the store is about 45 months which is appreciable enough. The regressional analysis shows significant increase of 761 millions (INR) in profits with every 1 month increase in their tenure.
The mean tenure of the crew in the store is about 14 months which is not at all long. The regressional analysis shows an even significant increase of 945 millions (INR) in profits with every 1 month increase in their tenure.
This clears one point. Since, usually the managers tend to stay for a longer time, an increase in their tenure doesn’t affect the profits of the store as much as the crew does which usually tends to stay for a shorter period of time.
The scatterplots between profits and respective tenures support the analysis.
The location of the stores affect the sales and profits immensely.
The analysis proves that as the competition in half a mile radius increases the profits drop by 25300 million (INR).
The population around the store has some affects on the profits of the store but not as great as the other factors.
The pedestrian count int the region has great affects on the profits. With every one unit increase in the pedestrian count inthe region of the store, teh profits increase by around 34000 millions (INR).
Same goes for the volume of residents in the vicinity of the store. Its clear that as residents increace, the profits increase exponentially.
A 24 hours store speaks for itself. Its clear that if a store is open 24*7, the profits of the store is improved greatly.
Although visibility of the store seems to increase the sales of the store,the analysis proves that its not an important factor.