QAM Project Modified

8/3/2019 QAM Project Modified

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PRESENTED BY - GROUP 10

ANJU EKKA (PGP27202)NIKHIL M (PGP27226)

R MAHESWARAN (PGP27238)

SHISHIR POONACHA S (PGP27251)

VENKATESH S R (PGP27259)

VIGNESH N (PGP27260)

Regression Analysis of factorsInfluencing GDP of India

Quantitative Analysis for Management Project


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Agenda

Introduction

Data Analysis

Assumptions

Univariate Regression Model

Multivariate Regression model


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Data Analyzed

Data Source:

www.indiastat.com

www.rbi.org.in

Dependent Variable:

Y: GDP of India

Independent Variable:

X1: Agricultural output in India

X2: Global crude oil prices

X3: Money supply in India

X4: Area under agricultural production


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Assumptions

Random Errors are independent

Homoscedasticity: Random Errors have the same variance Var(Ii)=W

2

Mean effect of Random Errors is Zero

E(Ii)= 0


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GDP v/s Agricultural Output

Good linear fit

R squared = 0.670 Adjusted R squared = 0.662


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GDP v/s Agricultural Output

-500000

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

82061

.8

80131

.2

89409.27

74253

.2

94055.

83

108407.04

96974.

64

99769.92

111168

.9

131848.22

129583.41

129468.15

145543.83

143481

.6

169928.

77

176419

.2

179429.

55

191255.6

6

199458.12

203635.32

196827

.3

174790.45

198372.16

Y

Agricultural output

Agricultural output

Y

Predicted Y

-200000

-150000

-100000

-50000

0

50000

100000

150000

0 50000 100000 150000 200000 250000

Residuals

Agricultural output

Agricultural output

Predicted Y closely mirrors actual Y


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GDP v/s Crude Oil

Bad linear fit ; R squared and adjusted R squared very low


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GDP v/s Crude Oil

-200000

-150000

-100000

-50000

0

50000

100000

150000

0 20 40 60 80 100 120Residuals

global crude oil prices

Global crude oil prices

Errors not normally distributed


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GDP (last 10 years) vs Global crude oil

(last 10 years)

Better linear fit over last 10 years ; variable to watch out for in the future


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GDP v/s Money Supply

High linear fit ; R squared = 0.994 ; adjusted R squared = 0.987


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GDP v/s Money Supply

-500000

0

500000

1000000

1500000

2000000

2500000

30000003500000

6244

7136

8510

10331

12406

15968

21230

27432

37873

54632

75696

97088

138757

187662

257547

355167

476535

697494

897933

1243411

1621557

2

094670

2

740681

Money supply

Money supply

Y

Predicted Y

-200000

-150000

-100000

-50000

050000

100000

150000

0 500000 1000000 1500000 2000000 2500000 3000000Residuals

money supply

Money supply

Predicted Y follows actual Y


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GDP v/s Area Under Production

Bad linear fit ; R squared = 0.200 adjusted R squared = 0.181


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GDP v/s Area Under Production

-200000

-150000

-100000

-50000

0

50000

100000

150000

0 20 40 60 80 100 120 140Residuals

Agricultural area

Area under agricultural production

Unequal variance ; Assumptions invalidated for this variable


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Multivariate Regression 4 variables

Good linear fit ; R squared = 0.996 ; Adjusted R squared = 0.995


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VIF high for agricultural output indicating multi-collinearity Corresponding eigen value is also very low


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Multivariate Regression step wise


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Area under agricultural production has been excluded due to multi collinearityDurbin Watson = 0.888 ; indicates positively correlated residuals


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Conclusion

Regression model gives a good linear fit for

GDP

GDP very closely related to money supply ineconomy

GDP also depends heavily on agricultural

output

Global crude oil prices impacting GDP inrecent times


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Thank You

QAM Project Modified

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