store24
nazneen
19 December 2017
setting working directory to file location
setwd(“C:/Users/Taiyyab Ali/Desktop/R language”)
store <- read.csv(paste("Store24.csv",sep=""))
View(store)a quick summary of data - “store”
library(psych)
overview <- describe(store)[2:14,3:9]
round(overview, digits=2)## mean sd median trimmed mad min
## Sales 1205413.12 304531.31 1127332.00 1182031.25 288422.04 699306.00
## Profit 276313.61 89404.08 265014.00 270260.34 90532.00 122180.00
## MTenure 45.30 57.67 24.12 33.58 29.67 0.00
## CTenure 13.93 17.70 7.21 10.60 6.14 0.89
## Pop 9825.59 5911.67 8896.00 9366.07 7266.22 1046.00
## Comp 3.79 1.31 3.63 3.66 0.82 1.65
## Visibility 3.08 0.75 3.00 3.07 0.00 2.00
## PedCount 2.96 0.99 3.00 2.97 1.48 1.00
## Res 0.96 0.20 1.00 1.00 0.00 0.00
## Hours24 0.84 0.37 1.00 0.92 0.00 0.00
## CrewSkill 3.46 0.41 3.50 3.47 0.34 2.06
## MgrSkill 3.64 0.41 3.59 3.62 0.45 2.96
## ServQual 87.15 12.61 89.47 88.62 15.61 57.90
## max
## Sales 2113089.00
## Profit 518998.00
## MTenure 277.99
## CTenure 114.15
## Pop 26519.00
## Comp 11.13
## Visibility 5.00
## PedCount 5.00
## Res 1.00
## Hours24 1.00
## CrewSkill 4.64
## MgrSkill 4.62
## ServQual 100.00summary of Profit,Mtenure,Ctenure column
describe(store)[3:5,3:4]## mean sd
## Profit 276313.61 89404.08
## MTenure 45.30 57.67
## CTenure 13.93 17.70arrannging data of mtcars
library(rio)
attach(mtcars)
View(mtcars)
newdata1 <- mtcars[order(mpg),] # in ascending order
View(newdata1)
newdata1[1:5, ]## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
## Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4newdata2 <- mtcars[order(-mpg),] # in decending order
View(newdata2)
detach(mtcars)Top 10 most profitable stores
attach(store)## The following object is masked _by_ .GlobalEnv:
##
## storenewdata <- store[order(-Profit),]
names(newdata)[1] <- "storeID"
View(newdata)
newdata[1:10,1:5]## storeID 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 6.636550bottom 10 least profitable stores
attach(store)## The following object is masked _by_ .GlobalEnv:
##
## store## The following objects are masked from store (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibilitynewdata3<- store[order(Profit),]
names(newdata3)[1] <- "storeID"
View(newdata3)
newdata3[1:10,1:5]## storeID 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 1.347023Scatter plot Profit vs MTenure
attach(store)## The following object is masked _by_ .GlobalEnv:
##
## store## The following objects are masked from store (pos = 3):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility## The following objects are masked from store (pos = 4):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit scatterplot(MTenure, Profit,main="Profit vs MTenure")
# scatterplot Profit Vs Ctenure
attach(store)## The following object is masked _by_ .GlobalEnv:
##
## store## The following objects are masked from store (pos = 4):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility## The following objects are masked from store (pos = 5):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility## The following objects are masked from store (pos = 6):
##
## Comp, CrewSkill, CTenure, Hours24, MgrSkill, MTenure,
## PedCount, Pop, Profit, Res, Sales, ServQual, store, Visibility library(car)
scatterplot(CTenure, Profit,main="Profit vs CTenure")
# creating correlation matrix
CorMat <- cor(store)
round(CorMat, digits = 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 1.00Correlation between profit and MTenure and CTenute
Cor1 <- cor(store$Profit,store$MTenure)
round(Cor1, digits = 2)## [1] 0.44Cor2 <- cor(store$Profit,store$CTenure)
round(Cor2, digits = 2)## [1] 0.26Profit has higher correlation with MTenure(0.44) than with CTenure(0.26)
library(corrgram)
library(ellipse)##
## Attaching package: 'ellipse'## The following object is masked from 'package:car':
##
## ellipsecorrgram(store, upper.panel=panel.pie,main= "Corrgram of store variables" )
A postive correlation exists between profit and MTenure, profit and pop, profit and PedCount(the dark blue one in profit row). A weak postive correlation exists between Profit and CTenure, profit and CrewSkill ,profit and MgrSkill and profit and ServQual(light blue one profit row). A negative correlation exists between profit and Comp,Res and Hours24(red one in profit row).
Correlation between profit and MTenure is stronger than the correlation between profit and CTenure. So keeping this in mind, decisions can be taken accordingly to extend tenure in order to improve store performance or not.But before making any decision also note that correlation is not the cause so as a manager our task should be to find root cause of this correlation before making any decision.
pearson’s correlation test between Profit and MTenure
cor.test(store$Profit,store$MTenure)##
## Pearson's product-moment correlation
##
## data: store$Profit and store$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
## 0.4388692Pearson test p-value = 8.19e-05 #pearson’s correlation test between Profit and CTenure
cor.test(store$Profit,store$CTenure)##
## Pearson's product-moment correlation
##
## data: store$Profit and store$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
## 0.2576789Pearson test p-value = 0.026
fit <- lm(Profit ~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility,data = store )
summary(fit)##
## 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-12Explanatory variables whose beta-coefficient are statistically significant(p-value<0.05) : MTenure,CTenure,Comp,Pop,PedCount,Res,Hours24. Explanatory variables whose beta-coefficient are not statistically significant(p-value>0.05): Visibility (does not matter, Ahh!)
Expected increase in Profit by increasing Manager’s tenure by one month in Store24: 760.993 Expected increase in Profit by increasing Crew’s tenure by one month in Store24: 944.978 (it’s surprising that correlation between profit and MTenure is higher than Profit and CTenure but beta value of MTenure less than CTenure)
Executive summary
From the above analysis and regression, In Store24, mean sale = 1205413.12 and median sale = 1227332.0, not to much diffrence in mean and median, so there is possiblity that data is uniformly distributed(not to much outliers). One of the explaination for CTenure has higher beta-coefficient with profit than MTtenure is it’s standard deviation is 17.7 which is less than MTenure’s standard deviation = 57.67. If we see the corrgram plot, we find higher correlation between profit and Manager’s Tenure, Population and Pedesterian Count while Visibility of store have negative correlation and also not statistically significant, which is very surprising and need a real world analysis to find root cause of occurance. R-square(coefficient of determination) is more than 50%, which means all the variables taken to explain variation in profit are significant.While adjusted R-square is less than R-square, so adding more variable is not right way to reduce error.May be there is some other factors that are affecting sales. F-statistic value is not so high so taking 8 variable all together is not fruitful inspite of that indivisual analysis need to done. Opening store for 24 hours is profitable for stores. Although p-value of visibility is statistically significant but beta coefficient is high so it really matter for sales.