store24
Prasunn
16 ??????????????? 2018
Reading the data set and calculating summary statistics :
store.df <- read.csv(paste("Store24.csv", sep=""))
View(store.df)
summary(store.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. :100.00TASK 4d Three very important variables in this analysis are the store Profit, the management tenure (MTenure) and the crew tenure (CTenure). 1. Use R to measure the mean and standard deviation of Profit. 2. Use R to measure the mean and standard deviation of MTenure. 3. Use R to measure the mean and standard deviation of CTenure.
mean(store.df$Profit)## [1] 276313.6sd(store.df$Profit)## [1] 89404.08mean(store.df$MTenure)## [1] 45.29644sd(store.df$MTenure)## [1] 57.67155mean(store.df$CTenure)## [1] 13.9315sd(store.df$CTenure)## [1] 17.69752TASK 4f Replicate Exhibit 1 shown in the case, using R 1. Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.
datadesc <- store.df[order(-store.df$Profit),]
datadesc[1:10,1:5]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.6365502.Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.
temp <- tail(datadesc,10)
temp[,1:5]## store Sales Profit MTenure CTenure
## 37 37 1202917 187765 23.1985000 1.347023
## 61 61 716589 177046 21.8184200 13.305950
## 52 52 1073008 169201 24.1185600 3.416838
## 54 54 811190 159792 6.6703910 3.876797
## 13 13 857843 152513 0.6571813 1.577002
## 32 32 828918 149033 36.0792600 6.636550
## 55 55 925744 147672 6.6703910 18.365500
## 41 41 744211 147327 14.9180200 11.926080
## 66 66 879581 146058 115.2039000 3.876797
## 57 57 699306 122180 24.3485700 2.956879TASK 4g - Scatter Plots Use R to draw a scatter plot of Profit vs. MTenure.
plot(store.df$MTenure, store.df$Profit,
col="blue",
main="Scatterplot of Profit vs. MTenure",
xlab="MTenure", ylab="Profit")
abline(lm(store.df$Profit ~ store.df$MTenure),col="green")
TASK 4h - Scatter Plots (contd.) Use R to draw a scatter plot of Profit vs. CTenure.
plot(store.df$CTenure, store.df$Profit,
col="blue",
main="Scatterplot of Profit vs. CTenure",
xlab="CTenure", ylab="Profit")
abline(lm(store.df$Profit ~ store.df$CTenure),col="green")
TASK 4i - Correlation Matrix Use R to construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)
round(cor(store.df),2)## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 1.00 -0.23 -0.20 -0.06 0.02 -0.29 0.03 -0.03
## Sales -0.23 1.00 0.92 0.45 0.25 0.40 -0.24 0.13
## Profit -0.20 0.92 1.00 0.44 0.26 0.43 -0.33 0.14
## MTenure -0.06 0.45 0.44 1.00 0.24 -0.06 0.18 0.16
## CTenure 0.02 0.25 0.26 0.24 1.00 0.00 -0.07 0.07
## Pop -0.29 0.40 0.43 -0.06 0.00 1.00 -0.27 -0.05
## Comp 0.03 -0.24 -0.33 0.18 -0.07 -0.27 1.00 0.03
## Visibility -0.03 0.13 0.14 0.16 0.07 -0.05 0.03 1.00
## PedCount -0.22 0.42 0.45 0.06 -0.08 0.61 -0.15 -0.14
## Res -0.03 -0.17 -0.16 -0.06 -0.34 -0.24 0.22 0.02
## Hours24 0.03 0.06 -0.03 -0.17 0.07 -0.22 0.13 0.05
## CrewSkill 0.05 0.16 0.16 0.10 0.26 0.28 -0.04 -0.20
## MgrSkill -0.07 0.31 0.32 0.23 0.12 0.08 0.22 0.07
## ServQual -0.32 0.39 0.36 0.18 0.08 0.12 0.02 0.21
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store -0.22 -0.03 0.03 0.05 -0.07 -0.32
## Sales 0.42 -0.17 0.06 0.16 0.31 0.39
## Profit 0.45 -0.16 -0.03 0.16 0.32 0.36
## MTenure 0.06 -0.06 -0.17 0.10 0.23 0.18
## CTenure -0.08 -0.34 0.07 0.26 0.12 0.08
## Pop 0.61 -0.24 -0.22 0.28 0.08 0.12
## Comp -0.15 0.22 0.13 -0.04 0.22 0.02
## Visibility -0.14 0.02 0.05 -0.20 0.07 0.21
## PedCount 1.00 -0.28 -0.28 0.21 0.09 -0.01
## Res -0.28 1.00 -0.09 -0.15 -0.03 0.09
## Hours24 -0.28 -0.09 1.00 0.11 -0.04 0.06
## CrewSkill 0.21 -0.15 0.11 1.00 -0.02 -0.03
## MgrSkill 0.09 -0.03 -0.04 -0.02 1.00 0.36
## ServQual -0.01 0.09 0.06 -0.03 0.36 1.00TASK 4j - Correlations Use R to measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)
round(cor(store.df$MTenure, store.df$Profit),2)## [1] 0.44Use R to measure the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)
round(cor(store.df$CTenure, store.df$Profit),2)## [1] 0.26TASK 4k Use R to construct the following Corrgram based on all variables in the dataset.
library(corrgram)
corrgram(store.df, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of store variables")
ASK 4l - Pearson???s Correlation Tests Run a Pearson’s Correlation test on the correlation between Profit and MTenure. What is the 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
## 0.4388692p-value = 8.193e-05
Run a Pearson’s Correlation test on the correlation between Profit and CTenure. What is the 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
## 0.2576789p-value = 0.02562
TASK 3m - Regression Analysis Run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.
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-12TASK 4n Based on TASK 3m, answer the following questions: 1. List the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05). MTenure, CTenure, Comp, Pop, PedCount, Res, Hours24
List the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05). Visibility TASK 4o Based on TASK 2m, answer the following questions: 1. 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?
The expected increase in Profit at the store if the Manager’s tenure increases by one month is : 760.993
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? The expected increase in Profit at the store if the Crew’s tenure increases by one month is : 944.978
SUMMARY
As we can now see from the analysis which we have done above, it is clear that, the managerial tenure and crew tenure results in financial gain. While site-location factors such as population, number of competitors, and pedestrian access are considered the primary get going of store success, our case study shows that increased skills in managers and the crew as well as the service quality has resulted in greater profits. We can also see from the Corrgram that there is a very strong correlation between Profit and Managerial tenure as well as Crew Tenure. From the regression analysis, we observe that p-value: 5.382e-12. Hence there is a chance we can predict the Profit if we are provided with the other variables. We also see that Visibility is a factor that does not affect the Profit that much since its p-value = 0.169411 (>0.05). Hence Visibility should be excluded. The result also shows us that there is a need for more variables in order to have a better prediction. We also see that with the increase in tenure of managers by one month, the Profits will increase by 760.993. And, with the increase in tenure of crew by one month, the Profits will increase by 944.978.