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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

library(psych)
describe(store.df$Profit)[3:4]
##        mean       sd
## X1 276313.6 89404.08
describe(store.df$MTenure)[3:4]
##    mean    sd
## X1 45.3 57.67
describe(store.df$CTenure)[3:4]
##     mean   sd
## X1 13.93 17.7

4f

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
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

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

4h

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

4i

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")

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

19

Executive Summary

In the Case:Store 24(A), on a Friday of May 2001 President and CEO Bob Gordon (MBA ’62) met with CFO Paul Doucette and COO Tom Hart for the preparation for the upcoming Monday morning executive committee meeting. The agenda focussed solely on discussing strategies for increasing store level employees retention. As per our analysis & data available, the variables that are statistically significant are Average Manager Tenure, Average Crew Tenure, Population & No. of competitors within a half miles, Pedestrian foot Count, Residential or Industrial area, Hours of open.Among these results we can observe that increasing Crew’s tenure by One month increases the profit higher as compared to increasing manager’s tenure.