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4c

 store.df <- read.csv(paste("Store24.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.   :100.00

4d 1

library(psych)
describe(store.df$Profit)[3:4]
##        mean       sd
## X1 276313.6 89404.08

2

describe(store.df$MTenure)[3:4]
##    mean    sd
## X1 45.3 57.67

3

describe(store.df$CTenure)[3:4]
##     mean   sd
## X1 13.93 17.7

4f 4

attach(store.df)
newdata <- store.df[order(Profit),]
newdata[1:10,1:5]
##    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  1.347023

5

newdata <- store.df[order(-Profit),]
newdata[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

4g 6

plot(store.df$Profit, store.df$MTenure, main = "Scatter plot Profit Vs MTenure",xlab = "Profit", ylab = "MTenure", col="red")

4h 7

 plot(store.df$Profit, store.df$CTenure, main = "Scatter plot Profit Vs CTenure",xlab = "Profit", ylab = "CTenure", col="blue")

4i 8

library(corrplot)
## corrplot 0.84 loaded
corrplot.mixed(corr= cor(store.df[, c(2:14)], use = "complete.obs"), upper="ellipse", tl.pos = "lt")

4j 9

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

10

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

4k 11

corrplot.mixed(corr=cor(store.df[ , c(2:14)], use="complete.obs"), 
upper="pie", lower = "shade",
main="Corrgram of store variables")

The relevant relationships of management are: The relation between profit and tenure of manager. The relation between profit and tenure of crew. The relation between profit and crew skills. The relation between profit and manager skills. The relation between profit and service quality.

4l 12

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 
## 0.4388692

13

cor.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 
## 0.2576789

4m

model <- lm(Profit ~ MTenure + CTenure + Pop + Comp + PedCount + Res + Hours24 + Visibility, data = store.df)
summary(model)
## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Pop + Comp + 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 *  
## Pop              3.667      1.466   2.501 0.014890 *  
## Comp        -25286.887   5491.937  -4.604 1.94e-05 ***
## 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-12

4n 15

The variables whose beta cofficients are statistically significant(p<0.05) are 1)MTenure 2)CTenure 3)Pop 4)Comp 5)PedCount 6)Res 7)Hours24

16 The variables whose beta cofficients are not statistically significant(p>0.05) are 1)Visibility

4o

17 If the managers tenure is increased by one month than profit will incerase by 760.993 units

18

If the Crew’s tenure is increased by one month than profit will increase by 944.978

4p

Executive Summary

The managers need to understand that the tenure of a manager and crew are very important for a store as longer the tenure for familiar is crew with the customers. They company need to contineously focus on the training, development and retention of employees if they want to increase the average profit per store.These differnt factors influncing the profits are briefly described as:

The primary factors that are effecting the profits are tenure of Manager then the tenure of crew and at last the population density in 1/2 sq. mile area

In terms of ped count and visiblity the easy access to pdestrain plays a more crutial role in profit of a store.

The store which are location of store plays a more vital role in determining profit rather than the store is opened 24 hours or not.

The areas where competition intensity is high the level of prfits is significantly low as with increase in one competior the profit can decrease by 25286 points.

The quality of service is also a mojaor factor influncing the profit of store.

The skill sets of manager and crew are very important foctor in increasing or decreasing the profit of a store.