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# SE-5004L and Econometrics, Semester Two, 2013/4 - Statistics Project Example

Summary
The data is a time series data spanning from 2001 to 2012. The dependent variable represents the per capita on cheese while the independent variable is the unemployment rate from UK.
Per capita data for food has…

## Extract of sample"SE-5004L and Econometrics, Semester Two, 2013/4"

Download file to see previous pages we observe that the coefficient for the unemployment rate is 6.2636, this implies that for any unit change (increase) in employment rate, the per capita on cheese increases per a factor of 6.2636. on the other hand, the constant term is 29.6256, implying that at zero unemployment rate we expect the per capita on cheese to be \$29.6256
From the regression table above, the value of R-Squared (R2) is 0.8894 implying that 88.94% variation in the dependent variable (per capita on cheese) is explained by the explanatory variable (unemployment rate) in the model. The remaining 11.06% could probably be explained by the residual (omitted variables) in the model.
The estimated value of b0 (constant term) is positive. The sign of the constant term does not matter (has impact/effect) on the reliability of b1. The value or sign of the constant term does not in any way affect the reliability of b1.
This implies that at zero unemployment rate, the per capita on cheese is assumed to be \$29.6255. the coefficient on unemployment rate is 6.2636, this shows that as the unemployment rate increases so does the per capita on cheese increase by a factor of 6.2636.
Just like in (i) above, we performed the Wald test for the null hypothesis that this coefficient b1=0, the p-value=0.5066>0.05 (significance level), we thus reject the null hypothesis and conclude that
In equation1 we observe that R-Squared is 0.8894 while in equation 2 we find that the value of R-Squared is 0.895. In the first equation it implies that 88.94% of variation in the dependent variable (per capita on cheese) is explained by the explanatory variable (unemployment rate) in the model on contrary, in the second equation, 89.5% of variation in the dependent variable (per capita on cheese) is explained by the explanatory variable (unemployment rate) in the model. This represents a slight improvement in the model, thus the second equation is more improved (much appropriate and better) than equation 1.
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