Read the data

Store.df <- read.csv("C:/interships/Store24.csv")
View(Store.df)

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

library("psych", lib.loc="~/R/win-library/3.4")
attach(Store.df)
describe(Profit)
##    vars  n     mean       sd median  trimmed   mad    min    max  range
## X1    1 75 276313.6 89404.08 265014 270260.3 90532 122180 518998 396818
##    skew kurtosis       se
## X1 0.62    -0.21 10323.49
#mean=276313.6
#sd= 89404.08

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

describe(MTenure)
##    vars  n mean    sd median trimmed   mad min    max  range skew kurtosis
## X1    1 75 45.3 57.67  24.12   33.58 29.67   0 277.99 277.99 2.01      3.9
##      se
## X1 6.66
#mean=45.3
#sd=57.67

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

 describe(CTenure)
##    vars  n  mean   sd median trimmed  mad  min    max  range skew kurtosis
## X1    1 75 13.93 17.7   7.21    10.6 6.14 0.89 114.15 113.26 3.52       15
##      se
## X1 2.04
#mean=13.93
#sd=17.7

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

hello<-Store.df[order(-Profit),]
hello1<-hello[1:10,1:5]
View(hello1)
print(hello1)
##    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

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

newdata2 <- Store.df[order(Profit),]
newdata3<-newdata2[1:10,1:5]
View(newdata3)
print(newdata3)
##    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

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

plot(x=Store.df$MTenure, y=Store.df$Profit, col="black", xlim =c(0,100), ylim=c(0,600000), xlab="Mtenure", ylab="Profit", main = "scatterplot of profit vs mtenure")
abline(h=mean(Store.df$Profit), col="dark blue")
abline(v=mean(Store.df$MTenure), col="red")
abline(lm(Store.df$Profit ~ Store.df$MTenure))

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

plot(x=Store.df$CTenure, y=Store.df$Profit, col="black", xlim =c(0,100), ylim=c(0,600000), xlab="CTenure", ylab="Profit", main = "scatterplot of profit vs ctenure")
abline(h=mean(Store.df$Profit), col="dark blue")
abline(v=mean(Store.df$CTenure), col="red")
abline(lm(Store.df$Profit ~ Store.df$CTenure))

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

cor(Store.df[,1:14])
##                  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
round(cor(Store.df[,1:14]),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

9. 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

10. 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

12. 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
#p-value = 8.193e-05

13. 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
# p-value = 0.02562

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

regg<- lm(Profit~MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility)
regg
## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount + 
##     Res + Hours24 + Visibility)
## 
## Coefficients:
## (Intercept)      MTenure      CTenure         Comp          Pop  
##    7610.041      760.993      944.978   -25286.887        3.667  
##    PedCount          Res      Hours24   Visibility  
##   34087.359    91584.675    63233.307    12625.447
summary(regg)
## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount + 
##     Res + Hours24 + 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    
## 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

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

summary(regg)
## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount + 
##     Res + Hours24 + 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    
## 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("*")
## [[1]]
## [1] "*"
#list1
#MTenure
#Comp
#Pedcount
#CTenure
#Pop
#Res
#Hours24

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

summary(regg)
## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount + 
##     Res + Hours24 + 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    
## 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
#list2
#Visibility

17. 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 summary statistic helps us to see that for an increase in Manager’s Tenure by one month, Profit increases by $760.993.

18. 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?

The summary statistic helps us to see that for an increase in Crew’s Tenure by one month, Profit increases by $944.978.

##19. Summary Executive:

  1. Based on this analysis, specially the regression analysis, it can be concluded that different other parameters can also be considered to make the analysis more intersting as it really shows how related or unrelated the variables are.

  2. The model accounts for only 64% of the variance in profit. So it can be conluded that it isn’t that good as a model. Can also be referred to as goodness of fit where it is seen how a good a model is in predicting the reality.

  3. The stars next the the p-values against each variable shows that pvalues of the estimate data are statiscally significant, i.e. the regression co-efficients of each independent variable whose p-value has stars(p<0.05) are significant statistically.

  4. There is an expected increase in the Profit in terms of Dollars depending on respective significant variable regression co-effiecients, given their own units.

Managerial Insights:

For a manager, getting the mean values of manager’s and crew’s tenure gives him a really good idea to see averagely how the tenure is spread among employees.The command when we found that the top 10 most and least 10 bottom sales, store, mtenure and and Ctenure based on profit, it literally helps to decide which stores to give good employees to and which stores to not. Employee retention has an impact in creating customer satisfaction and thus leading to profitability as the employees with more tenure are more likely to have great knowledge about the customer, process and the culture of the organization. Moreover, not managing employee retention is a loss of productivity and customer satisfaction and thus profitability of the store.