setwd("C:/Users/Dell/Downloads/Sameer Mathur")
Store24.df<-read.csv("Store24.csv")
attach(Store24.df)
View(Store24.df)Use R to measure the mean and standard deviation of Profit.
mean(Profit)## [1] 276313.6sd(Profit)## [1] 89404.08Use R to measure the mean and standard deviation of MTenure.
mean(MTenure)## [1] 45.29644sd(MTenure)## [1] 57.67155Use R to measure the mean and standard deviation of CTenure.
mean(CTenure)## [1] 13.9315sd(CTenure)## [1] 17.69752Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.
attach(Store24.df)## The following objects are masked from Store24.df (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibilitynewdata<-Store24.df[order(-Store24.df$Profit),]
newdata[1:10,1:5]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.
attach(Store24.df)## The following objects are masked from Store24.df (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility## The following objects are masked from Store24.df (pos = 4):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibilitynewdata<-Store24.df[order(Store24.df$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 1.347023Use R to draw a scatter plot of Profit vs. MTenure.
plot(y=Profit,x=MTenure,main="Profit vs. MTenure",col="black")
abline(lm(Store24.df$Profit~Store24.df$MTenure),col="red",lty="dotted")
Use R to draw a scatter plot of Profit vs. CTenure.
plot(y=Profit,x=CTenure,main="Profit vs. CTenure",col="black")
abline(lm(Store24.df$Profit~Store24.df$CTenure),col="red",lty="dotted")
Use R to construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)
round(cor(Store24.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 1.00Use R to measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)
round(cor(Store24.df$Profit,Store24.df$MTenure),2)## [1] 0.44Use R to measure the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)
round(cor(Store24.df$Profit,Store24.df$CTenure),2)## [1] 0.26Use R to construct the following Corrgram based on all variables in the dataset
library(corrgram)
corrgram(Store24.df,order=TRUE,lower.panel=panel.shade,upper.panel=panel.pie,text.panel=panel.txt,main="Corrgram of Store24")
Run a Pearson’s Correlation test on the correlation between Profit and MTenure. What is the p-value?
cor.test(Store24.df$Profit,Store24.df$MTenure)##
## Pearson's product-moment correlation
##
## data: Store24.df$Profit and Store24.df$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
## 0.4388692Run a Pearson’s Correlation test on the correlation between Profit and CTenure. What is the p-value?
cor.test(Store24.df$Profit,Store24.df$CTenure)##
## Pearson's product-moment correlation
##
## data: Store24.df$Profit and Store24.df$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
## 0.2576789Run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.
fit<-lm(Profit~MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility,Store24.df)
summary(fit)##
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount +
## Res + Hours24 + Visibility, data = Store24.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-12List the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05).
Mtenure, Comp,PedCount
List the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05).
Visibility
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?
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?
944.978
Executive Summary 1)Effect of Tenure of managers and crew on profits was observed. It was found that tenure of managers in top 10 shops was 4 times 10 least profitable store 2)Pearson’s Correlation test proves that p value is 8.193e-05 for MTenure and 0.02562 for CTenure. Hence Mtenure is strongly correlated to profits . Hence Managers affect the profit at a store more. 3)Corrgram shows visual correlation between all variables and hence makes understanding simple 4) Regression Ananlysis shows that M tenure ie tenure of manager, Number of competitors per 10,000 people within a1/2 mile radius and Population in that radius affect Profit more . It had predicted model using which we can predict profit of any store.“”