Project: Analysis of Biscuit & Confectionery Sectors in Indian Market
Group 5
Reading Data
setwd("C:/Users/Anshumaan/Desktop/2018 DAM/PROJECT")
salesdata.df <- read.csv("DAM Project final data.csv")
attach(salesdata.df)
head(salesdata.df)
OutletCode TownClass Town State
1 1 TITANIUM Pune Maharashtra
2 2 GOLD Gwalior Madhya Pradesh
3 3 TITANIUM Nerul Maharashtra
4 4 SILVER KORBA Chattisgarh
5 5 TITANIUM Kalyan Maharashtra
6 7 SILVER KANKAVLI Maharashtra
RE RE_new
1 ERETAIL OTHERS
2 HIGH END GROCER HIGH END GROCER
3 HIGH END GROCER HIGH END GROCER
4 HIGH END GROCER HIGH END GROCER
5 PANPLUS OTHERS
6 CASH AND CARRY OTHERS
Fridge.Volume Has.Fridge Annual.Sales Parent.Firm CHOCO1 CHOCO2 CHOCO3
1 340 1 123860.13 71.60% 7.50% 10.90% 1.80%
2 50 1 79153.05 71.10% 6.40% 11.40% 0.50%
3 35 1 227851.06 71.60% 7.50% 10.90% 1.80%
4 35 1 31397.78 62.70% 6.60% 15.10% 0.00%
5 50 1 24210.18 71.60% 7.50% 10.90% 1.80%
6 0 0 20226.00 71.60% 7.50% 10.90% 1.80%
CHOCO4 Sales.New
1 1.60% 123860.13
2 3.20% 79153.05
3 1.60% 227851.06
4 7.20% 31397.78
5 1.60% 24210.18
6 1.60% 20226.00
summary(salesdata.df$Sales.New)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 6333 18058 46288 48606 454670
Model 1: To check whether sales is affected by availability of fridge or not
fit <- lm(salesdata.df$Sales.New ~ salesdata.df$Has.Fridge)
summary(fit)
Call:
lm(formula = salesdata.df$Sales.New ~ salesdata.df$Has.Fridge)
Residuals:
Min 1Q Median 3Q Max
-88047 -25221 -8762 4419 440030
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14640.1 185.6 78.87 <2e-16 ***
salesdata.df$Has.Fridge 73406.6 282.7 259.68 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 68950 on 242593 degrees of freedom
Multiple R-squared: 0.2175, Adjusted R-squared: 0.2175
F-statistic: 6.743e+04 on 1 and 242593 DF, p-value: < 2.2e-16
Removing Outliers
Model 2
fit <- lm(salesdata.df$Sales.New ~ salesdata.df$Fridge.Volume + salesdata.df$TownClass+ salesdata.df$RE_new + salesdata.df$State)
summary(fit)
Call:
lm(formula = salesdata.df$Sales.New ~ salesdata.df$Fridge.Volume +
salesdata.df$TownClass + salesdata.df$RE_new + salesdata.df$State)
Residuals:
Min 1Q Median 3Q Max
-222745 -21202 -9116 9748 442371
Coefficients:
Estimate Std. Error
(Intercept) 37349.896 824.868
salesdata.df$Fridge.Volume 208.280 1.476
salesdata.df$TownClassREST OF URBAN -5281.205 516.728
salesdata.df$TownClassSilver -15764.138 1640.069
salesdata.df$TownClassSILVER -4505.138 427.396
salesdata.df$TownClassTITANIUM 4171.070 334.984
salesdata.df$RE_newFOOD STORE 109467.875 681.865
salesdata.df$RE_newHIGH END GROCER 84741.221 604.127
salesdata.df$RE_newLOW END GROCER -16152.731 390.243
salesdata.df$RE_newOTHERS -5342.958 565.947
salesdata.df$StateGujarat -4392.833 789.323
salesdata.df$StateMadhya Pradesh -5565.949 803.649
salesdata.df$StateMaharashtra 2078.860 762.080
t value Pr(>|t|)
(Intercept) 45.280 < 2e-16 ***
salesdata.df$Fridge.Volume 141.115 < 2e-16 ***
salesdata.df$TownClassREST OF URBAN -10.220 < 2e-16 ***
salesdata.df$TownClassSilver -9.612 < 2e-16 ***
salesdata.df$TownClassSILVER -10.541 < 2e-16 ***
salesdata.df$TownClassTITANIUM 12.452 < 2e-16 ***
salesdata.df$RE_newFOOD STORE 160.542 < 2e-16 ***
salesdata.df$RE_newHIGH END GROCER 140.270 < 2e-16 ***
salesdata.df$RE_newLOW END GROCER -41.391 < 2e-16 ***
salesdata.df$RE_newOTHERS -9.441 < 2e-16 ***
salesdata.df$StateGujarat -5.565 2.62e-08 ***
salesdata.df$StateMadhya Pradesh -6.926 4.35e-12 ***
salesdata.df$StateMaharashtra 2.728 0.00637 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 62320 on 232132 degrees of freedom
Multiple R-squared: 0.3786, Adjusted R-squared: 0.3786
F-statistic: 1.179e+04 on 12 and 232132 DF, p-value: < 2.2e-16
Model 3
fit <- lm(log(salesdata.df$Sales.New) ~ salesdata.df$Fridge.Volume + salesdata.df$TownClass+ salesdata.df$RE_new + salesdata.df$State + salesdata.df$TownClass*salesdata.df$Has.Fridge+salesdata.df$RE_new*salesdata.df$Has.Fridge)
#salesdata.df$State*salesdata.df$Has.Fridge
summary(fit)
Call:
lm(formula = log(salesdata.df$Sales.New) ~ salesdata.df$Fridge.Volume +
salesdata.df$TownClass + salesdata.df$RE_new + salesdata.df$State +
salesdata.df$TownClass * salesdata.df$Has.Fridge + salesdata.df$RE_new *
salesdata.df$Has.Fridge)
Residuals:
Min 1Q Median 3Q Max
-4.8892 -0.6048 0.0563 0.6561 4.0255
Coefficients:
Estimate
(Intercept) 9.149e+00
salesdata.df$Fridge.Volume -1.921e-04
salesdata.df$TownClassREST OF URBAN 2.813e-01
salesdata.df$TownClassSilver 3.385e-01
salesdata.df$TownClassSILVER 3.153e-02
salesdata.df$TownClassTITANIUM 1.080e-01
salesdata.df$RE_newFOOD STORE 6.400e-01
salesdata.df$RE_newHIGH END GROCER 1.135e+00
salesdata.df$RE_newLOW END GROCER -1.086e-01
salesdata.df$RE_newOTHERS -1.261e-01
salesdata.df$StateGujarat -2.112e-02
salesdata.df$StateMadhya Pradesh -1.330e-01
salesdata.df$StateMaharashtra 8.226e-03
salesdata.df$Has.Fridge 1.785e+00
salesdata.df$TownClassREST OF URBAN:salesdata.df$Has.Fridge -3.743e-01
salesdata.df$TownClassSilver:salesdata.df$Has.Fridge -7.386e-01
salesdata.df$TownClassSILVER:salesdata.df$Has.Fridge -5.891e-02
salesdata.df$TownClassTITANIUM:salesdata.df$Has.Fridge -3.564e-02
salesdata.df$RE_newFOOD STORE :salesdata.df$Has.Fridge 1.613e-01
salesdata.df$RE_newHIGH END GROCER :salesdata.df$Has.Fridge -4.677e-01
salesdata.df$RE_newLOW END GROCER :salesdata.df$Has.Fridge -3.555e-01
salesdata.df$RE_newOTHERS:salesdata.df$Has.Fridge -8.576e-02
Std. Error
(Intercept) 1.418e-02
salesdata.df$Fridge.Volume 2.636e-05
salesdata.df$TownClassREST OF URBAN 9.962e-03
salesdata.df$TownClassSilver 3.295e-02
salesdata.df$TownClassSILVER 8.487e-03
salesdata.df$TownClassTITANIUM 7.176e-03
salesdata.df$RE_newFOOD STORE 3.074e-02
salesdata.df$RE_newHIGH END GROCER 2.792e-02
salesdata.df$RE_newLOW END GROCER 8.173e-03
salesdata.df$RE_newOTHERS 1.128e-02
salesdata.df$StateGujarat 1.237e-02
salesdata.df$StateMadhya Pradesh 1.258e-02
salesdata.df$StateMaharashtra 1.194e-02
salesdata.df$Has.Fridge 1.372e-02
salesdata.df$TownClassREST OF URBAN:salesdata.df$Has.Fridge 1.744e-02
salesdata.df$TownClassSilver:salesdata.df$Has.Fridge 5.241e-02
salesdata.df$TownClassSILVER:salesdata.df$Has.Fridge 1.391e-02
salesdata.df$TownClassTITANIUM:salesdata.df$Has.Fridge 1.025e-02
salesdata.df$RE_newFOOD STORE :salesdata.df$Has.Fridge 3.314e-02
salesdata.df$RE_newHIGH END GROCER :salesdata.df$Has.Fridge 3.012e-02
salesdata.df$RE_newLOW END GROCER :salesdata.df$Has.Fridge 1.232e-02
salesdata.df$RE_newOTHERS:salesdata.df$Has.Fridge 1.818e-02
t value
(Intercept) 645.378
salesdata.df$Fridge.Volume -7.290
salesdata.df$TownClassREST OF URBAN 28.233
salesdata.df$TownClassSilver 10.276
salesdata.df$TownClassSILVER 3.715
salesdata.df$TownClassTITANIUM 15.056
salesdata.df$RE_newFOOD STORE 20.820
salesdata.df$RE_newHIGH END GROCER 40.633
salesdata.df$RE_newLOW END GROCER -13.288
salesdata.df$RE_newOTHERS -11.176
salesdata.df$StateGujarat -1.707
salesdata.df$StateMadhya Pradesh -10.572
salesdata.df$StateMaharashtra 0.689
salesdata.df$Has.Fridge 130.110
salesdata.df$TownClassREST OF URBAN:salesdata.df$Has.Fridge -21.461
salesdata.df$TownClassSilver:salesdata.df$Has.Fridge -14.091
salesdata.df$TownClassSILVER:salesdata.df$Has.Fridge -4.237
salesdata.df$TownClassTITANIUM:salesdata.df$Has.Fridge -3.478
salesdata.df$RE_newFOOD STORE :salesdata.df$Has.Fridge 4.868
salesdata.df$RE_newHIGH END GROCER :salesdata.df$Has.Fridge -15.527
salesdata.df$RE_newLOW END GROCER :salesdata.df$Has.Fridge -28.849
salesdata.df$RE_newOTHERS:salesdata.df$Has.Fridge -4.717
Pr(>|t|)
(Intercept) < 2e-16
salesdata.df$Fridge.Volume 3.12e-13
salesdata.df$TownClassREST OF URBAN < 2e-16
salesdata.df$TownClassSilver < 2e-16
salesdata.df$TownClassSILVER 0.000203
salesdata.df$TownClassTITANIUM < 2e-16
salesdata.df$RE_newFOOD STORE < 2e-16
salesdata.df$RE_newHIGH END GROCER < 2e-16
salesdata.df$RE_newLOW END GROCER < 2e-16
salesdata.df$RE_newOTHERS < 2e-16
salesdata.df$StateGujarat 0.087731
salesdata.df$StateMadhya Pradesh < 2e-16
salesdata.df$StateMaharashtra 0.490941
salesdata.df$Has.Fridge < 2e-16
salesdata.df$TownClassREST OF URBAN:salesdata.df$Has.Fridge < 2e-16
salesdata.df$TownClassSilver:salesdata.df$Has.Fridge < 2e-16
salesdata.df$TownClassSILVER:salesdata.df$Has.Fridge 2.27e-05
salesdata.df$TownClassTITANIUM:salesdata.df$Has.Fridge 0.000505
salesdata.df$RE_newFOOD STORE :salesdata.df$Has.Fridge 1.13e-06
salesdata.df$RE_newHIGH END GROCER :salesdata.df$Has.Fridge < 2e-16
salesdata.df$RE_newLOW END GROCER :salesdata.df$Has.Fridge < 2e-16
salesdata.df$RE_newOTHERS:salesdata.df$Has.Fridge 2.39e-06
(Intercept) ***
salesdata.df$Fridge.Volume ***
salesdata.df$TownClassREST OF URBAN ***
salesdata.df$TownClassSilver ***
salesdata.df$TownClassSILVER ***
salesdata.df$TownClassTITANIUM ***
salesdata.df$RE_newFOOD STORE ***
salesdata.df$RE_newHIGH END GROCER ***
salesdata.df$RE_newLOW END GROCER ***
salesdata.df$RE_newOTHERS ***
salesdata.df$StateGujarat .
salesdata.df$StateMadhya Pradesh ***
salesdata.df$StateMaharashtra
salesdata.df$Has.Fridge ***
salesdata.df$TownClassREST OF URBAN:salesdata.df$Has.Fridge ***
salesdata.df$TownClassSilver:salesdata.df$Has.Fridge ***
salesdata.df$TownClassSILVER:salesdata.df$Has.Fridge ***
salesdata.df$TownClassTITANIUM:salesdata.df$Has.Fridge ***
salesdata.df$RE_newFOOD STORE :salesdata.df$Has.Fridge ***
salesdata.df$RE_newHIGH END GROCER :salesdata.df$Has.Fridge ***
salesdata.df$RE_newLOW END GROCER :salesdata.df$Has.Fridge ***
salesdata.df$RE_newOTHERS:salesdata.df$Has.Fridge ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9753 on 232123 degrees of freedom
Multiple R-squared: 0.4749, Adjusted R-squared: 0.4748
F-statistic: 9996 on 21 and 232123 DF, p-value: < 2.2e-16
Model 4
setwd("C:/Users/Anshumaan/Desktop/2018 DAM/PROJECT")
numerical_data<- read.csv("DAM_Numerical_data.csv")
numerical_data= numerical_data[c(numerical_data$Sales.New>=1000),]
m <- lm(log(Sales.New) ~ Has.Fridge +RE_new +State + TownClass +Has.Fridge*TownClass+Has.Fridge*RE_new, data = numerical_data)
summary(m)
Call:
lm(formula = log(Sales.New) ~ Has.Fridge + RE_new + State + TownClass +
Has.Fridge * TownClass + Has.Fridge * RE_new, data = numerical_data)
Residuals:
Min 1Q Median 3Q Max
-4.5831 -0.6505 0.0296 0.6691 4.0785
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.948786 0.010538 849.177 <2e-16 ***
Has.Fridge 0.888639 0.014389 61.758 <2e-16 ***
RE_new 0.052954 0.003972 13.330 <2e-16 ***
State 0.067595 0.002576 26.242 <2e-16 ***
TownClass -0.026170 0.002717 -9.633 <2e-16 ***
Has.Fridge:TownClass 0.121734 0.004440 27.418 <2e-16 ***
Has.Fridge:RE_new 0.240614 0.004985 48.264 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.016 on 232138 degrees of freedom
Multiple R-squared: 0.4297, Adjusted R-squared: 0.4297
F-statistic: 2.915e+04 on 6 and 232138 DF, p-value: < 2.2e-16