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。
se in GDP by 2.958.
There is negative linear relationship between GDP and employment ratio.
Now if we talk about correlation between these two variable which is R.
= +√(.720)
= +.849
In this + sign shows that correlation is positive and is .849
Now is .72 which shows that 72 % variance in GDP is explained by employment ratio.
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, = -6.80
– 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.
6.2) FDI Regression (Appendix 9.3.2)
In this regression model,
FDI 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 FDI is increase by 1, there is increase in GDP by .
There is positive linear relationship between GDP and FDI as the slope is positive.
Now if we talk about correlation between these two variable which is R.
= +√(.782)
= +.884
In this + sign shows that correlation is positive and is .884
Now is .78 which shows that 78 % variance in GDP is explained by FDI.
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, = 8.025
– 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.
6.3) Lending interest rate (Appendix 9. 3.3)
In this regression model,
Lending interest rate 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 2.088
is a slope of this equation which is -1.066
– Estimated value
If lending interest rate increases by 1, there is decrease in GDP by 1.066.
There is negative linear relationship between GDP and lending interest rate as the slope is negative.
Now if we talk about correlation between these two variable which is R.
= +√(.466)
= +.683
In this + sign shows that correlation i
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