Read the data using read.csv

setwd("C:/Users/Prabha Shankar/Desktop/Winter Internship/R file")
store24.df <- read.csv("Store24.csv")

View the data frame in R

View(store24.df)

Task 2C

To find the Means for Profit , Mtenure and CTenure

apply(store24.df[,3:5],2,mean)
##       Profit      MTenure      CTenure 
## 276313.61333     45.29644     13.93150

To find the Standard Deviation for Profit , Mtenure and CTenure

apply(store24.df[,3:5],2,sd)
##      Profit     MTenure     CTenure 
## 89404.07634    57.67155    17.69752

Task 2F

To print the top 10 most profitable store

var1.df <- store24.df[order(-store24.df$Profit),]
var1.df[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

To print the top 10 least profitable store

var2.df <- store24.df[order(store24.df$Profit),]
var2.df[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

Task 2G

R To Draw scateer plot of profit vs MTenure

library(car)
## Warning: package 'car' was built under R version 3.3.3
scatterplot(store24.df$MTenure ,store24.df$Profit,
            xlab = "MTenure", ylab = "Profit", 
            main = "Scatterplot of Profit vs. MTenure")

##Task 2H #R To Draw scateer plot of profit vs CTenure

library(car)
scatterplot(store24.df$CTenure ,store24.df$Profit,
            xlab = "CTenure", ylab = "Profit", 
            main = "Scatterplot of Profit vs. CTenure")

##Task 2I #R To construct the corelation matrix

round(digits=2,cor(store24.df))
##            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

Task 2J

R to measure the corelation between Profit and MTenure

round(digits=2,cor(store24.df$Profit,store24.df$MTenure))
## [1] 0.44

R To measure the corelation between Profit aand CTenure

round(digits=2,cor(store24.df$Profit,store24.df$CTenure))
## [1] 0.26

Task 2K

R to construct the corrgram based on all variable of dataset

library(corrgram)
## Warning: package 'corrgram' was built under R version 3.3.3
corrgram(store24.df, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="Corrgram of Store Variables")

##Task 2L #Pearson’s Correlation test on the correlation between Profit and MTenure.

cor.test(store24.df$Profit,store24.df$MTenure)
## 
##  Pearson's product-moment correlation
## 
## data:  store24.df$Profit and store24.df$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

Pearson’s Correlation test on the correlation between Profit and CTenure

cor.test(store24.df$Profit,store24.df$CTenure)
## 
##  Pearson's product-moment correlation
## 
## data:  store24.df$Profit and store24.df$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

Task 2M

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

var3 <- lm(store24.df$Profit ~ store24.df$MTenure + store24.df$CTenure 
                          + store24.df$Comp + store24.df$Pop
                          + store24.df$PedCount + store24.df$Res
                          + store24.df$Hours24 + store24.df$Visibility, 
          data=store24.df)
summary(var3)
## 
## Call:
## lm(formula = store24.df$Profit ~ store24.df$MTenure + store24.df$CTenure + 
##     store24.df$Comp + store24.df$Pop + store24.df$PedCount + 
##     store24.df$Res + store24.df$Hours24 + store24.df$Visibility, 
##     data = store24.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    
## store24.df$MTenure       760.993    127.086   5.988 9.72e-08 ***
## store24.df$CTenure       944.978    421.687   2.241 0.028400 *  
## store24.df$Comp       -25286.887   5491.937  -4.604 1.94e-05 ***
## store24.df$Pop             3.667      1.466   2.501 0.014890 *  
## store24.df$PedCount    34087.359   9073.196   3.757 0.000366 ***
## store24.df$Res         91584.675  39231.283   2.334 0.022623 *  
## store24.df$Hours24     63233.307  19641.114   3.219 0.001994 ** 
## store24.df$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

Task 2N

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

MTenure, CTenure, Comp, Pop, PedCount, Res, Hours24

Explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05)

Visibility

Regression Summary

A.With one month increase in Manager’s tenure of work experience, a Store is statistically expected to increase it’s profits by 760.993. B.With one month increase in Crew’s tenure of work experience, a Store is statistically expected to increase it’s profits 944.978 .

Executive Summary

  1. Increase in tenure of Manager and Crew will lead to increase in Profit.
  2. Population growth also results in increase in profit .
  3. An increase in Manager’s and crew’s tenure will result in increase in Managerail and crew’s skills.