Store 24A
Taruna Sudhakar
January 15, 2018
TASK 4C
store.df <-read.csv(paste ("Store24 (1).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
library(psych)
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 1.46mean(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
TOP 10TOP.df<- store.df[order(-store.df$Profit),]
head(TOP.df,10)[,1:5] ## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550 BOTTOM 10
tail(TOP.df,10)[,1:5]## store Sales Profit MTenure CTenure
## 37 37 1202917 187765 23.1985000 1.347023
## 61 61 716589 177046 21.8184200 13.305950
## 52 52 1073008 169201 24.1185600 3.416838
## 54 54 811190 159792 6.6703910 3.876797
## 13 13 857843 152513 0.6571813 1.577002
## 32 32 828918 149033 36.0792600 6.636550
## 55 55 925744 147672 6.6703910 18.365500
## 41 41 744211 147327 14.9180200 11.926080
## 66 66 879581 146058 115.2039000 3.876797
## 57 57 699306 122180 24.3485700 2.956879TASK 4G
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(store.df$Profit ~ store.df$MTenure , main="Profit vs MTenure Scatterplot", xlab="MTenure", ylab="Profit")
TASK 4H
library(car)
scatterplot(store.df$Profit ~ store.df$CTenure , main="Profit vs CTenure Scatterplot", xlab="CTenure", ylab="Profit")
TASK 4I
round(cor(store.df, use = "complete.obs", method = "kendall"),2)## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 1.00 -0.16 -0.14 -0.01 -0.01 -0.19 -0.02 -0.02
## Sales -0.16 1.00 0.78 0.26 0.14 0.20 -0.18 0.15
## Profit -0.14 0.78 1.00 0.25 0.19 0.23 -0.26 0.14
## MTenure -0.01 0.26 0.25 1.00 0.10 -0.04 0.12 0.01
## CTenure -0.01 0.14 0.19 0.10 1.00 -0.13 -0.11 0.05
## Pop -0.19 0.20 0.23 -0.04 -0.13 1.00 -0.11 0.01
## Comp -0.02 -0.18 -0.26 0.12 -0.11 -0.11 1.00 0.07
## Visibility -0.02 0.15 0.14 0.01 0.05 0.01 0.07 1.00
## PedCount -0.14 0.31 0.32 0.00 -0.05 0.46 -0.22 -0.11
## Res -0.03 -0.13 -0.15 0.04 -0.10 -0.17 0.19 0.02
## Hours24 0.02 0.07 0.02 -0.09 0.02 -0.24 0.10 0.04
## CrewSkill -0.03 0.11 0.11 0.12 0.17 0.16 -0.05 -0.18
## MgrSkill -0.06 0.18 0.15 0.19 0.02 0.03 0.17 0.01
## ServQual -0.23 0.28 0.25 0.17 0.06 0.06 0.06 0.16
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store -0.14 -0.03 0.02 -0.03 -0.06 -0.23
## Sales 0.31 -0.13 0.07 0.11 0.18 0.28
## Profit 0.32 -0.15 0.02 0.11 0.15 0.25
## MTenure 0.00 0.04 -0.09 0.12 0.19 0.17
## CTenure -0.05 -0.10 0.02 0.17 0.02 0.06
## Pop 0.46 -0.17 -0.24 0.16 0.03 0.06
## Comp -0.22 0.19 0.10 -0.05 0.17 0.06
## Visibility -0.11 0.02 0.04 -0.18 0.01 0.16
## PedCount 1.00 -0.26 -0.29 0.12 0.05 -0.05
## Res -0.26 1.00 -0.09 -0.16 -0.03 0.09
## Hours24 -0.29 -0.09 1.00 0.14 0.00 0.04
## CrewSkill 0.12 -0.16 0.14 1.00 0.05 -0.01
## MgrSkill 0.05 -0.03 0.00 0.05 1.00 0.24
## ServQual -0.05 0.09 0.04 -0.01 0.24 1.00TASK 4J
"Correlationg between Profit MTenure"## [1] "Correlationg between Profit MTenure"round(cor(store.df$Profit, store.df$MTenure),2)## [1] 0.44"Correlationg between Profit CTenure"## [1] "Correlationg between Profit CTenure"round(cor(store.df$Profit, store.df$CTenure),2)## [1] 0.26TASK 4K
library(corrgram)
corrgram(store.df, lower.panel=panel.shade, upper.panel=panel.pie,diag.pane=panel.minmax,text.panel=panel.txt, main="Corrgram of Store Variables")
TASK 4I
cor.test(store.df$Profit,store.df$MTenure)##
## Pearson's product-moment correlation
##
## data: store.df$Profit and store.df$MTenure
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692cor.test(store.df$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
## 0.2576789TASK 4M
summary(lm(Profit~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility, data=store.df))##
## 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
Explanatory variable whose beta coffecient is statistically not significant is Visibility
explanatory variable(s) whose beta-coefficients are not statistically significant are MTenure, CTenure, Pop, PedCount, Res, Hours24, Comp
TASK 4O
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 = $810.971201
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 = $1016.017324
TASK 4p
->The 10most profitable stores have higher manager tenures and crew tenures than the 10 least profitable stores. Some managers have very hign levels of experience and hence outliers exist.
->A greater increase in Profit can be expected by increasing crew tenure rather than manager tenure.
->Given that: F-statistic of 14.53; 8 datapoints; 66 degrees of freedom and p-value = 5.382e-12 we can say that Profit is closely related to {MTenure, CTenure, Comp, Pop, PedCount, Res, Hours24 and Visibility} all taken together as the p-value is very small.
From Multiple R-squared: 0.6379 we can say that this model accounts for 63.79% of the variances and Adjusted R-squared: 0.594 indicates 59.4% of weighted variances considered.
Profit = 7610.041 + 760.993(MTenure) + 944.978(CTenure) - 25286.887(Comp) + 3.667(Pop) + 34087.359(PedCount) + 91584.675(Res) + 63233.307(Hours24) + 12625.447(Visibility)