Store 24
Prashant
January 26, 2018
- Reading and summary generation of the file Store24.csv
store24 <- read.csv(paste("Store24.csv", sep=""))
View(store24)
library(psych)
describe(store24)## 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 1.46- mean and standard deviation of Profit
describe(store24$Profit)## vars n mean sd median trimmed mad min max range
## X1 1 75 276313.6 89404.08 265014 270260.3 90532 122180 518998 396818
## skew kurtosis se
## X1 0.62 -0.21 10323.49- mean and standard deviation of MTenure
describe(store24$MTenure)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 75 45.3 57.67 24.12 33.58 29.67 0 277.99 277.99 2.01 3.9
## se
## X1 6.66- mean and standard deviation of CTenure
describe(store24$CTenure)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 75 13.93 17.7 7.21 10.6 6.14 0.89 114.15 113.26 3.52 15
## se
## X1 2.04- printing the {StoreID, Sales, Profit, MTenure, CTenure} of the Top 10 most profitable stores
Top10 <- store24[order(-store24$Profit),]
Top10[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- printing the {StoreID, Sales, Profit, MTenure, CTenure} of the Bottom 10 least profitable stores
Bottom10 <- store24[order(-store24$Profit),]
Bottom10[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- drawing a scatter plot of Profit vs. MTenure
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(Profit ~ MTenure, data=store24,
main="Scatterplot Profit vs MTenure",
xlab="MTenure (in months)",
ylab="Profit")
- drawing a scatter plot of Profit vs CTenure
scatterplot(Profit ~ CTenure, data=store24,
main="Scatterplot Profit vs CTenure",
xlab="CTenure (in months)",
ylab="Profit")
- Measurement of Correlation using Correlation Matrix
round(cor(store24), 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## function (x, digits = 0) .Primitive("round")- Measurement of Correlation between Profit and MTenure
round(cor(store24$Profit, store24$MTenure), 2)## [1] 0.44- Measurement of Correlation between Profit and CTenure
round(cor(store24$Profit, store24$CTenure), 2)## [1] 0.26- Constructing Corrgram for the dataset
library(corrgram)
corrgram(store24, order=FALSE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of store variables")
-> Studying the above correlation matrix , we can see that there is a strong positive relationship between Profit and MTenure with a correlation coefficient,r value = 0.44 . Also, Profit is having a weak positive correlation with CTenure having r value of 0.26 . Profit is having a strong positive correlation with Sales having r value of 0.92 . Profit is having a strong positive correlation with Pop having r value of 0.43 . Profit is having a weak negative correlation with Comp having r value of -0.33
- Pearson’s Correlation test on the correlation between Profit and MTenure
cor.test(store24$Profit, store24$MTenure)##
## Pearson's product-moment correlation
##
## data: store24$Profit and store24$MTenure
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692-> here the p value is 8.19e-05 which comes out to be less than 0.05, null hypothesis is rejected and alternate hypothesis is accepted. This means correlation exists.
- Pearson’s Correlation test on the correlation between Profit and CTenure
cor.test(store24$Profit, store24$CTenure)##
## Pearson's product-moment correlation
##
## data: store24$Profit and store24$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-> here the p value is 0.0256 which comes out to be less than 0.05, null hypothesis is rejected and alternate hypothesis is accepted. This means correlation exists.
- Regression Analysis
regress <- lm(Profit ~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility, data= store24)
summary(regress)##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = store24)
##
## 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-> The p value comes out to 5.382e-12 which is smaller than 0.05 and hence we get to know that the developed model is statistically significant and the adjusted r square value is 0.594, which implies the 60% variance is explained by the model.
explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05) -> 1. MTenure 2. CTenure 3. Comp 4.Pop 5. PedCount 6. Res 7. Hours24
explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05) -> 1. Visibility
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? -> Through the regression analysis, we can see that if Manager’s tenure increases by one month, then the Profit increases by $760.99
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? -> Through the regression analysis, if Crew’s tenure increases by one month, then the Profit increases by $944.98
Executive Summary -> The Managers Tenure has a mean value of 45.30~ 45 months and a standard deviation of57.67~58 days. An increase in MTenure of 1 month, increases Profit by approx. $761 .
-> The Crew Tenure has a mean value of 14 months and a standard deviation of 18 days. An increase in one month in Crew’s Tenure, causes increase in Profit by $945.
-> The manager tenure is more signifivant as compared with the Crew Tenure in terms of Profit Contribution.
-> If the pedestrian count rises by 1 point, then the profit rises by $34087 which proves it to be a major contributor and if the store location is situated in the residential area,it increases profit of $91585.
-> If the store is operated 24 hours, it raises the profit by $63233.