store.df <- read.csv(paste("store24.csv", sep=""))
library(psych)
View(store.df)
#To find mean and standard deviation of store Profit, the management tenure (MTenure) and the crew tenure (CTenure)
describe(store.df)
## 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
#Task 4d
#mean and standard deviation of Profit.
mean(store.df$Profit)
## [1] 276313.6
sd(store.df$Profit)
## [1] 89404.08
#mean and standard deviation of MTenure.
mean(store.df$MTenure)
## [1] 45.29644
sd(store.df$MTenure)
## [1] 57.67155
##mean and standard deviation of CTenure.
mean(store.df$CTenure)
## [1] 13.9315
sd(store.df$CTenure)
## [1] 17.69752
#To print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores
attach(store.df)
storeasc <- store.df[order(-Profit),]
storeasc[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
#To print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 least profitable stores
attach(store.df)
## The following objects are masked from store.df (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility
storedec <- store.df[order(Profit),]
storedec[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 1.347023
#To draw a scatter plot of Profit vs. MTenure
library(car)
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
scatterplot(Profit ~ MTenure,data = store.df)
#To draw a scatter plot of Profit vs. CTenure
library(car)
scatterplot(Profit ~ CTenure,data = store.df)
#To measure the correlation between Profit and MTenure
round(cor(store.df$Profit,store.df$MTenure),digits = 2)
## [1] 0.44
#To measure the correlation between Profit and CTenure
round(cor(store.df$Profit,store.df$CTenure),digits = 2)
## [1] 0.26
#To construct the following Corrgram based on all variables in the dataset
library(corrgram)
corrgram(store.df, order=FALSE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Store Variables")
#We see that profit and sales are colsely related with each other which is an obious fact.Also profict and Mtenure are also related to about 43% whereas Ctenure and profit are less corelated around 25%.Also Managerskill is related to profit around 35% whereas Crewskill is less related to profit around 20%.We also see Pedcount related to profit around 45%. The managerially relevant correlations are Profit~MTenure and Profit~PedCount. This reflects that More experienced managers have more profit in their stores and also profit also depends equally on PedCount outside the store which intern related to population in half a mile radius.
#To run a Pearson's Correlation test on the correlation between Profit and MTenure.
cor.test(store.df$Profit,store.df$MTenure,method = "pearson")
##
## 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
## 0.4388692
#p-vlaue= 8.193e-05 .This implies that p-value is much smaller(<0.05).So we can easily reject the null hypothesis,This means Profit and MTenure have a relation.
#To run a Pearson's Correlation test on the correlation between Profit and CTenure.
cor.test(store.df$Profit,store.df$CTenure,method = "pearson")
##
## 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
## 0.2576789
#p-vlaue= 0.02562 .This implies that p-value is smaller(<0.05).So we can reject the null hypothesis,This means Profit and CTenure have a relation.
#To run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}
regprofit <- lm(Profit ~ MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility, data = store.df)
summary(regprofit)
##
## 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-12
#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?
#Answer-For every unit increase in MTenure , There is an increase of 760.993 in Profit
#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?
#Answer-For every unit increase in CTenure , There is an increase of 944.978 in Profit
#Executive Summary
#The important Insights of this analysis are-
#1 Our Regression model confirmed that there is a correlation between Managers's tenure at Store24 with the Store's annual profits.This implies that more experienced managers are likely to increase the profit of the stores.
#2 Our Regression model confirmed that there is a correlation between Crew's tenure at Store24 with the Store's annual profits.This implies that more experienced Crew are likely to increase the profit of the stores.
#3 Our Regression model confirmed that there is a correlation between Managers's tenure at Store24 with the Store's annual profits.This implies that more skilled managers are likely to increase the profit of the stores.
#4 Our Regression model confirmed that there is a correlation between Managers's tenure at Store24 with the Store's annual profits.This implies that more skilled Crew are likely to increase the profit of the stores.
#These four important insights shows that Company's profit can be increased by providing training and skill development courses to the crew and managers of the store along with providing bonuses and other attractions in order to increase the tenure and experience of the crew and managers at Store24 which will ultimately increase the profits of the store.