RMD Store24
Akshay Jha
December 17, 2017
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store <- read.csv(paste("Store24.csv", sep=""))
summary(store)## 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. :100.00library(psych)
describe(store)## 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 1.462d
mean and standard deviation of Profit.
mean and standard deviation of MTenure.
mean and standard deviation of CTenure.
mean(store$Profit)## [1] 276313.6sd(store$Profit)## [1] 89404.08mean(store$MTenure)## [1] 45.29644sd(store$MTenure)## [1] 57.67155mean(store$CTenure)## [1] 13.9315sd(store$CTenure)## [1] 17.697522e
top 10 most profitable stores.
top 10 least profitable stores.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionattach(store)## The following object is masked _by_ .GlobalEnv:
##
## storestorenew <- select(store, store, Sales, Profit, MTenure, CTenure)
newdata <- storenew[order(-Profit), ]
names(newdata)[1] <- "StoreID"
newdata[1:10, ]## 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 6.636550newdata <- storenew[order(Profit), ]
newdata[1:10, ]## 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 1.347023detach(store)2g
scatter plot of Profit vs. MTenure.
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:psych':
##
## logitscatterplot(store$Profit ~ store$MTenure, main = "Scatterplot of Profit vs. MTenure", xlab = "MTenure", ylab = "Profit")
2h
scatter plot of Profit vs. CTenure.
scatterplot(store$Profit ~ store$CTenure, main = "Scatterplot of Profit vs. CTenure", xlab = "CTenure", ylab = "Profit")
2i
Correlation Matrix
cor(store)## store Sales Profit MTenure CTenure
## store 1.00000000 -0.22693400 -0.19993481 -0.05655216 0.019930097
## Sales -0.22693400 1.00000000 0.92387059 0.45488023 0.254315184
## Profit -0.19993481 0.92387059 1.00000000 0.43886921 0.257678895
## MTenure -0.05655216 0.45488023 0.43886921 1.00000000 0.243383135
## CTenure 0.01993010 0.25431518 0.25767890 0.24338314 1.000000000
## Pop -0.28936691 0.40348147 0.43063326 -0.06089646 -0.001532449
## Comp 0.03194023 -0.23501372 -0.33454148 0.18087179 -0.070281327
## Visibility -0.02648858 0.13065638 0.13569207 0.15651731 0.066506016
## PedCount -0.22117519 0.42391087 0.45023346 0.06198608 -0.084112627
## Res -0.03142976 -0.16672402 -0.15947734 -0.06234721 -0.340340876
## Hours24 0.02687986 0.06324716 -0.02568703 -0.16513872 0.072865022
## CrewSkill 0.04866273 0.16402179 0.16008443 0.10162169 0.257154817
## MgrSkill -0.07218804 0.31163056 0.32284842 0.22962743 0.124045346
## ServQual -0.32246921 0.38638112 0.36245032 0.18168875 0.081156172
## Pop Comp Visibility PedCount Res
## store -0.289366908 0.03194023 -0.02648858 -0.221175193 -0.03142976
## Sales 0.403481471 -0.23501372 0.13065638 0.423910867 -0.16672402
## Profit 0.430633264 -0.33454148 0.13569207 0.450233461 -0.15947734
## MTenure -0.060896460 0.18087179 0.15651731 0.061986084 -0.06234721
## CTenure -0.001532449 -0.07028133 0.06650602 -0.084112627 -0.34034088
## Pop 1.000000000 -0.26828355 -0.04998269 0.607638861 -0.23693726
## Comp -0.268283553 1.00000000 0.02844548 -0.146325204 0.21923878
## Visibility -0.049982694 0.02844548 1.00000000 -0.141068116 0.02194756
## PedCount 0.607638861 -0.14632520 -0.14106812 1.000000000 -0.28437852
## Res -0.236937265 0.21923878 0.02194756 -0.284378520 1.00000000
## Hours24 -0.221767927 0.12957478 0.04692587 -0.275973353 -0.08908708
## CrewSkill 0.282845090 -0.04229731 -0.19745297 0.213672596 -0.15331247
## MgrSkill 0.083554590 0.22407913 0.07348301 0.087475440 -0.03213640
## ServQual 0.123946521 0.01814508 0.20992919 -0.005445552 0.09081624
## Hours24 CrewSkill MgrSkill ServQual
## store 0.02687986 0.04866273 -0.07218804 -0.322469213
## Sales 0.06324716 0.16402179 0.31163056 0.386381121
## Profit -0.02568703 0.16008443 0.32284842 0.362450323
## MTenure -0.16513872 0.10162169 0.22962743 0.181688755
## CTenure 0.07286502 0.25715482 0.12404535 0.081156172
## Pop -0.22176793 0.28284509 0.08355459 0.123946521
## Comp 0.12957478 -0.04229731 0.22407913 0.018145080
## Visibility 0.04692587 -0.19745297 0.07348301 0.209929194
## PedCount -0.27597335 0.21367260 0.08747544 -0.005445552
## Res -0.08908708 -0.15331247 -0.03213640 0.090816237
## Hours24 1.00000000 0.10536295 -0.03883007 0.058325655
## CrewSkill 0.10536295 1.00000000 -0.02100949 -0.033516504
## MgrSkill -0.03883007 -0.02100949 1.00000000 0.356702708
## ServQual 0.05832565 -0.03351650 0.35670271 1.0000000002J
correlation between Profit and MTenure
correlation between Profit and CTenure
cor(store$Profit,store$MTenure)## [1] 0.4388692cor(store$Profit,store$CTenure)## [1] 0.25767892k
Corrgram
library(corrgram)
corrgram(store, order = TRUE, lower.panel = panel.shade, upper.panel = panel.pie, text.panel = panel.txt, main = "Corrgram of Store variables")
2l
Pearson’s Correlation test on the correlation between Profit and MTenure
Pearson’s Correlation test on the correlation between Profit and CTenure
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
## 0.4388692cor.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
## 0.2576789For Profit and M-Tenure,The p alue = 8.193e-05 which is significantly smaller than 0.05 which allows us to reject the null hypothesis which means that there is a significant correlation between these 2 variables .
For Profit and C-Tenure,p-value is 0.02562 which is smaller than 0.05 which allows us to reject the null hypothesis which means that there is a significant correlation between these 2 variables . #2m #Regression Analyis
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-122n
Statistically significant explanatory variables are:MTenure, CTenure, Comp, Pop, PedCount, Res, Hours24
Explanatory variable(s) whose beta-coefficients are not statistically significant is:Visibility
Based on 2m
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
round(summary(fit)$coefficients["MTenure",1], digits=0)## [1] 761Expected change in the Profit at a store, if the Crew’s tenure i.e. number of months of experience with Store24, increases by one month
round(summary(fit)$coefficients["CTenure",1], digits=0)## [1] 945Executive summary
The following analysis can be done from the data:
1.Profit at the store is significantly impacted by the tenure of the manager and crew.
2.The most profitable store is with ID:74 and the least profitable store is :57
3.Coming to the regression part,the p-value of the model is statistically significant.It means it is a good fit model.
4.R square value is:0.6379.It means that 63.79% of variations in the dependent variable is explained by the independent variable.
5.Adjusted R square value is 0.594.It means 59.4% variation in the dependent variable is explained by the independent variable.This values decreases as we add no of independent variables.
6.Variables MTenure, CTenure, Comp, Pop, PedCount, Res, Hours24 are statistically significant as their p-values are < 0.05.Only Visibility variable is insignificant.
7.In a mathematical formula , Profit=7610.041+(761MTenure)+(945CTenure)+((-25286)Comp)+(3.7Pop)+(34087PedCount)+(91585Res)+(63234Hours24)+(12626Visibility)+56970