摘要:本文是一篇留学生GDP国民经济分析的报告,在报告中,我们分析了影响印度的国内生产总值的因素。该报告将涉及回归分析,假设检验,均值,中位数,这些因素都是独立变量及其对GDP是一个因变量的影响模式等。
s positive and is .849
Now is .46 which shows that 46 % variance in GDP is explained by employment ratio.
Now if we talk about this model whether it is good or bad, we have check two condition.
should be high
In this is high.
Hypothesis test:
: β = 0 (no linear relationship between X and Y)
: β ≠ 0 (linear relationship between X and Y)
This is conclude by t statistics
Now, = -3.964
– Standard error
?value is .001 and we assume α is .05 which is greater than p-value.
Hence we reject .
So we conclude that it is a good regression model.
6.4) Population Regression (Appendix 9.3.4)
In this regression model,
Population is an independent variable and on X-axis.
GDP is a dependent variable and on y-axis.
After doing data analysis of this model, we conclude that the regression equation for this is:
Here,
is an intercept which is 3.894
is a slope of this equation which is 0.029
– Estimated value
If population is increase by 1, there is increase in GDP by 3.60
There is strong positive linear relationship between GDP and POPULATION as the slope is positive.
Now if we talk about correlation between these two variable which is R.
= +√(.819)
= +.905
In this + sign shows that correlation is positive and is .905
Now is .81 which shows that 81 % variance in GDP is explained by population.
Now if we talk about this model whether it is good or bad, we have to check two condition.
should be high
In this is high.
Hypothesis test:
: β = 0 (no linear relationship between X and Y)
: β ≠ 0 (linear relationship between X and Y)
This is conclude by t statistics
Now, = 9.031
– Standard error
value is .000 and we assume α is .05 which is greater than p-value.
Hence we reject .
So we conclude that it is a good regression model.
7) Conclusion
The effect of factors like employment ratio, foreign direct investment, lending interest rate and population on GDP of India are considered as important variables which we have tried to explain with the help of regression analysis and hypothesis testing. By considering the data of past 20 years we have also calculated its mean, median, mode, Variance, standard deviation (appendix 2). We have one dependent variable that is GDP and four independent variables which are FDI, employment ratio, population, and interest rate.
According to multiple regressions, the equation for the model is:
Where x1, x2, x3, x4 are the independent variable, estimated value E(y) is expected by these variable. In our report, we have taken separate simple regression models
Regression analysis cannot interpret as a procedure for establishing a cause and effect relationship between variables. It can only show that how much these variables are related or associated with each other. Regression equation tells us about mean value of y for given value of x. According to
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