Store24
Karan Yadav
18 December 2017
Calling the summary function and checking it with Exhibit 3
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. :100.00Now calculating the mean and Standard Deviation of Profit
mean(store$Profit)## [1] 276313.6sd(store$Profit)## [1] 89404.08Mean profit comes out to be 276313.6 and SD is 89404.08
mean(store$MTenure)## [1] 45.29644sd(store$MTenure)## [1] 57.67155Mean Mtenure comes out to be 45.296 months and SD is 57.57 months
mean(store$CTenure)## [1] 13.9315sd(store$CTenure)## [1] 17.69752Mean Ctenure is nearly 14 months and SD is 17.697 months
attach(store)## The following object is masked _by_ .GlobalEnv:
##
## storenewdata <- store[order(-Profit), ]
data.frame(newdata$store, newdata$Sales, newdata$Profit, newdata$MTenure, newdata$CTenure)[1:10,]## newdata.store newdata.Sales newdata.Profit newdata.MTenure
## 1 74 1782957 518998 171.09720
## 2 7 1809256 476355 62.53080
## 3 9 2113089 474725 108.99350
## 4 6 1703140 469050 149.93590
## 5 44 1807740 439781 182.23640
## 6 2 1619874 424007 86.22219
## 7 45 1602362 410149 47.64565
## 8 18 1704826 394039 239.96980
## 9 11 1583446 389886 44.81977
## 10 47 1665657 387853 12.84790
## newdata.CTenure
## 1 29.519510
## 2 7.326488
## 3 6.061602
## 4 11.351130
## 5 114.151900
## 6 6.636550
## 7 9.166325
## 8 33.774130
## 9 2.036961
## 10 6.636550This is the table showing the top 10 most profitable store
newdata <- store[order(Profit), ]
data.frame(newdata$store, newdata$Sales, newdata$Profit, newdata$MTenure, newdata$CTenure)[1:10,]## newdata.store newdata.Sales newdata.Profit newdata.MTenure
## 1 57 699306 122180 24.3485700
## 2 66 879581 146058 115.2039000
## 3 41 744211 147327 14.9180200
## 4 55 925744 147672 6.6703910
## 5 32 828918 149033 36.0792600
## 6 13 857843 152513 0.6571813
## 7 54 811190 159792 6.6703910
## 8 52 1073008 169201 24.1185600
## 9 61 716589 177046 21.8184200
## 10 37 1202917 187765 23.1985000
## newdata.CTenure
## 1 2.956879
## 2 3.876797
## 3 11.926080
## 4 18.365500
## 5 6.636550
## 6 1.577002
## 7 3.876797
## 8 3.416838
## 9 13.305950
## 10 1.347023This is the table showing data of 10 least profitable store
library(car)
scatterplot(store$MTenure, store$Profit, xlab = "MTenure", ylab = "Profit", main = "Scatter plot between Mtenure and Profit")
This is the plot of profit vs Mtenure
scatterplot(store$CTenure, store$Profit, xlab = "Ctenure", ylab = "Profit", main = "Scatter plot between Ctenure and Profit")
This is scatter plot of Profit vs Ctenure
round(cor(store),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 1.00Here we have obtained correlation matrix between variables upto 2 decimal places
round(cor(store$Profit, store$MTenure), 2)## [1] 0.44round(cor(store$Profit, store$CTenure), 2)## [1] 0.26Here we obtain correlation between i) Profit and Mtenure ii) Profit and Ctenure
library(corrgram)
corrgram(store, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of correlations between store variables")
Here is the correlation plot using the corrgram library showing the correlation between variables of the store dataset
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
## 0.4388692Here is the Pearson’s correlation test performed for profit vs Mtenure
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
## 0.2576789Here is the Pearson’s correlation test performed for profit vs Ctenure
myfit <- lm(Profit ~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility ,data= store)
summary(myfit)##
## 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-12Here are the coeffcients of all the variables with corresponding p values
fitted(myfit)## 1 2 3 4 5 6 7 8
## 282884.6 311616.6 247387.2 188867.1 308773.0 379779.2 392304.9 371985.2
## 9 10 11 12 13 14 15 16
## 443237.0 300474.6 390414.7 420779.0 210319.6 268639.8 279296.3 202381.0
## 17 18 19 20 21 22 23 24
## 352534.2 455293.3 256081.6 275088.3 277490.0 271166.4 309003.2 214340.6
## 25 26 27 28 29 30 31 32
## 246051.2 219299.0 258929.7 280699.0 210844.3 260034.8 197082.6 191247.4
## 33 34 35 36 37 38 39 40
## 207234.6 370486.2 318628.6 232328.1 240430.8 199026.7 260630.9 173787.2
## 41 42 43 44 45 46 47 48
## 237766.0 277755.6 375932.0 475485.8 350220.8 279391.3 399517.8 208750.4
## 49 50 51 52 53 54 55 56
## 215972.9 307812.7 282907.8 212113.7 252711.1 195979.6 214674.3 167063.9
## 57 58 59 60 61 62 63 64
## 227968.7 218550.3 265067.8 331875.7 192084.1 218925.7 238526.9 318618.1
## 65 66 67 68 69 70 71 72
## 293397.2 218979.5 261546.3 240964.4 280082.4 282110.4 205893.0 262434.7
## 73 74 75
## 269862.0 412871.4 252828.2residuals(myfit)## 1 2 3 4 5
## -17870.5566 112390.4448 -24652.2001 21254.9195 -8292.9911
## 6 7 8 9 10
## 89270.7785 84050.0803 -10870.2458 31488.0240 -21849.6437
## 11 12 13 14 15
## -528.7222 -91759.0426 -57806.5916 -7068.7877 -75345.2520
## 16 17 18 19 20
## -6104.0234 -86950.2209 -61254.3355 5413.3764 -5853.2921
## 21 22 23 24 25
## 5094.0156 95869.5511 -31589.1824 53013.4120 36072.8170
## 26 27 28 29 30
## -7386.9737 -28735.7167 -7662.9590 53111.6789 73572.1725
## 31 32 33 34 35
## 14802.3715 -42214.3885 85510.4023 11712.8399 3995.3758
## 36 37 38 39 40
## -13036.0714 -52665.8054 4157.3337 -39500.8957 49125.8347
## 41 42 43 44 45
## -90438.9845 -13683.5771 -38699.0221 -35704.8151 59928.2412
## 46 47 48 49 50
## 36388.7486 -11664.7868 75418.5740 -20696.9041 -56799.7045
## 51 52 53 54 55
## -45563.7849 -42912.6649 112306.9000 -36187.6388 -67002.3454
## 56 57 58 59 60
## 22171.0985 -105788.7112 9050.7093 38001.1613 24195.2780
## 61 62 63 64 65
## -15038.1182 -16284.7396 509.1427 -97461.0600 8243.7616
## 66 67 68 69 70
## -72921.5481 100520.7352 -4625.3831 95310.5717 -27907.3903
## 71 72 73 74 75
## -7364.0084 -65662.7214 9331.0473 106126.6026 43997.8062SO here is the fitted data according to the myfit linear model and the difference with actual data.
Executive Summary
The main insights from the analysis are:
- The correlation of PRofit with manager tenure is 0.44 which means there is a strong relation with profit generation capacity of the firm with the tenure of the manager. The low p-value also suggests that.
- The correlation between profit with Crew tenure is 0.26 which means the relation is good but not as strong enough as that of the profit - Manager tenure relation. The p-value of 0.03 is also not too low to indicate a strong relationship.
- For every monthly increment in manager tenure the observed increase in profit is 760 and for every increase in monthly crew tenure the profit goes up by 945. However the pr value of the regression analysis of Mtenure is 9.72 e- 08 which is very ow and indcates a very strong relation as compared to 0.0284 value of C tenure
- Besides tenure analysis of managers and crew there is a positive and strong correlation of profit Population, pedestrian Count, Managerial Skill and Service Quality.
Key Suggestion
The firm should focus on increasing the manager tenure as it indicates a higher correlation with profit then the crew tenure. However negligence of crew tenure is not appreciated. Also the firm can look upto analyse other variables like Managerial skill, Service quality population which have shown a positive correlation with profit.