Read & View

store.df=read.csv(paste("store24.csv",sep=""))
View(store.df)

Mean & Standard Deviation of Profit, MTenure, CTenure respectively

library(psych)
## Warning: package 'psych' was built under R version 3.4.3
# PROFIT
p=describe(store.df$Profit)
p[,3:4]
##        mean       sd
## X1 276313.6 89404.08
# MTenure
mt=describe(store.df$MTenure)
mt[,3:4]
##    mean    sd
## X1 45.3 57.67
# CTenure
ct=describe(store.df$CTenure)
ct[,3:4]
##     mean   sd
## X1 13.93 17.7

Sorting

sortstore=store.df[order(-store.df$Profit),]
#View(sortstore)
sortstore[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   6.636550
sortstore[66:75,1:5]
##    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  2.956879

ScatterPlot

library(car)
## Warning: package 'car' was built under R version 3.4.3
## 
## Attaching package: 'car'
## The following object is masked from 'package:psych':
## 
##     logit
scatterplot(store.df$MTenure ,store.df$Profit, main="ScatterPlot of Profit vs MTenure", xlab="MTenure", ylab="Profit" )

scatterplot(store.df$CTenure ,store.df$Profit, main="ScatterPlot of Profit vs CTenure")

Correlation Matrix

cor(store.df)
##                  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  1.000000000

Correlation Coefficient

cor(store.df$Profit,store.df$MTenure)
## [1] 0.4388692
cor(store.df$Profit,store.df$CTenure)
## [1] 0.2576789

Correlogram

library(corrgram)
## Warning: package 'corrgram' was built under R version 3.4.3
corrgram(store.df, upper.panel=panel.pie)

Correlation Test

ct=cor.test(store.df$Profit,store.df$MTenure)
#ct
ct[3]
## $p.value
## [1] 8.193133e-05
ct=cor.test(store.df$Profit,store.df$CTenure)
#ct
ct[3]
## $p.value
## [1] 0.0256203

Regression

model=lm(formula=store.df$Profit~store.df$MTenure+store.df$CTenure+store.df$Comp+store.df$Pop+store.df$PedCount+store.df$Res+store.df$Hours24+store.df$Visibility)
summary(model)
## 
## Call:
## lm(formula = store.df$Profit ~ store.df$MTenure + store.df$CTenure + 
##     store.df$Comp + store.df$Pop + store.df$PedCount + store.df$Res + 
##     store.df$Hours24 + store.df$Visibility)
## 
## 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    
## store.df$MTenure       760.993    127.086   5.988 9.72e-08 ***
## store.df$CTenure       944.978    421.687   2.241 0.028400 *  
## store.df$Comp       -25286.887   5491.937  -4.604 1.94e-05 ***
## store.df$Pop             3.667      1.466   2.501 0.014890 *  
## store.df$PedCount    34087.359   9073.196   3.757 0.000366 ***
## store.df$Res         91584.675  39231.283   2.334 0.022623 *  
## store.df$Hours24     63233.307  19641.114   3.219 0.001994 ** 
## store.df$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-12

Variables who’s Beta-coefficients are statistically significant:

MTenure CTenure Comp Pop PedCount Res Hours24

Variables who’s Beta coeffients are statistically insignificant:

Visibililty

CONCLUSION

Increasing MTenure by 1 month increases Profit by $760.933 Increasing CTenure by 1 month increases Profit by $944.978

MANAGERIAL INSIGHTS

Since the tenure of Managers and Crew members is positively correlated with the Profits made by a store, impetus must be given to retention of employees. Based on the increase in amount of profit made by increasing their tenure by 1 month, certain strategies within that range can be taken up to enhance employee retention.

Furhtermore, it is clear from the regression model that stores which are functioning 24 hours make more profits, so most of the stores can be made 24 hours avaliable if feasible.

The manager can also conclude that if they want to setup a new store, they must look for a location where the number of pedestrians is high and it is in a residential area, because these two factors are also significantly positively correlated with profit.