Using R, read the data into a data frame called store. Play close attention to Exhibit 3 - Summary Statistics from Sample Stores from the CASE. Using R, get the summary statistics of the data. Confirm that the summary statistics generated from R are consistent with Exhibit 3 from the Case

setwd("C:/Users/Dell/Downloads/Sameer Mathur")
store.df<- read.csv("Store24.csv")
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

Use R to measure the mean and standard deviation of Profit.

mean(store.df$Profit)
## [1] 276313.6
sd(store.df$Profit)
## [1] 89404.08

Use R to measure the mean and standard deviation of MTenure.

mean(store.df$MTenure)
## [1] 45.29644
sd(store.df$MTenure)
## [1] 57.67155

Use R to measure the mean and standard deviation of CTenure.

mean(store.df$CTenure)
## [1] 13.9315
sd(store.df$CTenure)
## [1] 17.69752

Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.

attach(store.df)
View(store.df)
newdata <- store.df[order(-store.df$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

Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.

newdata <- store.df[order(-store.df$Profit),]
tail(newdata,10)[,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

Use R to draw a scatter plot of Profit vs. MTenure.

plot(store.df$MTenure, store.df$Profit,ylab = "Profit", xlab = "Manager Tenure", col="blue")
abline(lm(store.df$Profit~store.df$MTenure),col="blue",lty="dotted")

Use R to draw a scatter plot of Profit vs. CTenure.

plot(store.df$CTenure, store.df$Profit,ylab = "Profit", xlab = "Crew Tenure", col="blue")
abline(lm(store.df$Profit~store.df$CTenure),col="blue",lty="dotted")

Use R to construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)

round(cor(store.df),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     1.00

Use R to measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)

round(cor(store.df$Profit,store.df$MTenure),2)
## [1] 0.44

Use R to measure the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)

round(cor(store.df$Profit,store.df$CTenure),2)
## [1] 0.26

Use R to construct the following Corrgram based on all variables in the dataset.

library(corrgram)
corrgram(store.df, order=TRUE, lower.panel = panel.shade,upper.panel = panel.pie, text.panel = panel.txt, main="Corrgram of store variables")

Run a Pearson’s Correlation test on the correlation between Profit and MTenure. What is the p-value?

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

Run a Pearson’s Correlation test on the correlation between Profit and CTenure. What is the p-value?

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

Run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.

fit<- lm(Profit~MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility, data=store.df)
summary(fit)
## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + 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 *  
## 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-12

List the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05).

"MTenure, Comp, PedCount"
## [1] "MTenure, Comp, PedCount"

List the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05).

"visibility"
## [1] "visibility"

What is 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?

"The expected change in profit will be approximately $760.993"
## [1] "The expected change in profit will be approximately $760.993"

What is expected 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?

## [1] "The expected change in profit will be approximately $944.978"

Please prepare an “Executive Summary”. Please add this to the end of your Rmd file. Specifically, please create a qualitative summary of Managerial Insights, based on your data analysis, especially your Regression Analysis.

"From the above analysis we can come to the conclusion that:
1) While the visibility of the store doesnt make a difference to the sales of the store, keeping the store open for 24 hours is quite significant to the sal
2)The factors which majorly affect the sales of a particular sale are the tenure of the manager, the competition around the store, and the number of pedastrians walking around the store. 
3)At the same time if the tenure of the crew is increased by just a month the profits of the sales significantly change more than when the tenure of the manager is increased by the same duration.
4)We have also listed the most profitable stores and the least profitable stores using which we can further analyze which factors are working in favour of which store."
## [1] "From the above analysis we can come to the conclusion that:\n1) While the visibility of the store doesnt make a difference to the sales of the store, keeping the store open for 24 hours is quite significant to the sal\n2)The factors which majorly affect the sales of a particular sale are the tenure of the manager, the competition around the store, and the number of pedastrians walking around the store. \n3)At the same time if the tenure of the crew is increased by just a month the profits of the sales significantly change more than when the tenure of the manager is increased by the same duration.\n4)We have also listed the most profitable stores and the least profitable stores using which we can further analyze which factors are working in favour of which store."