statistical regression
The objective of this study is to develop an econometric model that lead me to estimate the relationship between the number of deaths due to cancer, the per capita income, the number of elderly people and the displacement of radioactive material on the Italian territory. I will use that data reported by banks are partly Istat data for 2003 and part of me generated according to a report given by the above, and also based on statistical data.
The econometric model developed is a multiple linear regression, which allowed me to estimate the effect on the dependent variable Y of a variation of three independent variables X1, X2 and X3.
Therefore, those variables associated with I: YN °
deaths due to cancer in ten thousand people [dead]
X1 Income per capita [income]
X2 N-65 ° of over ten thousand inhabitants [Seniors]
X3 Total coefficient of radioactivity resulting from
presence in the territory of Central, deposits or more reactors
of radioactivity in the index based on distance [ Coeff. Total]
OLS to estimate the coefficients B1, B2, B3, B4, errors and goodness of regression I used the package Stata / SE 10.0.
The objective of this study is to develop an econometric model that lead me to estimate the relationship between the number of deaths due to cancer, the per capita income, the number of elderly people and the displacement of radioactive material on the Italian territory. I will use that data reported by banks are partly Istat data for 2003 and part of me generated according to a report given by the above, and also based on statistical data.
The econometric model developed is a multiple linear regression, which allowed me to estimate the effect on the dependent variable Y of a variation of three independent variables X1, X2 and X3.
Therefore, those variables associated with I: YN °
deaths due to cancer in ten thousand people [dead]
X1 Income per capita [income]
X2 N-65 ° of over ten thousand inhabitants [Seniors]
X3 Total coefficient of radioactivity resulting from
presence in the territory of Central, deposits or more reactors
of radioactivity in the index based on distance [ Coeff. Total]
OLS to estimate the coefficients B1, B2, B3, B4, errors and goodness of regression I used the package Stata / SE 10.0.
Therefore, the estimated regression function is:
The regression R2 indicates the proportion of the sample variance of Y explained by the repressors Xi, varying between zero and one. Regression analysis shows that having a R2 = 0.7187, the independent variables explain much of the variability in Y. Furthermore, the three independent variables jointly participate in the determination of the dependent variable, which we can see from the regression coefficients.
For a more careful analysis we consider the t test for testing hypotheses, and thus the value of the t-statistic will allow us to reject the null hypothesis or not. Therefore, the regression analysis we note that the value of the t test is more consistent with the number of elderly in that area which, showing a value of 11.22. The income will allow us to reject the null hypothesis because the t-statistic is equal to 4:58, the same is not true for the presence of radioactive material on the Italian territory, with a value of 1.11 that we would not reject the null hypothesis to a minimum level of 1.96. It may help to decompose the effects of this regressive regression analysis combining different variables observed previously
For a more careful analysis we consider the t test for testing hypotheses, and thus the value of the t-statistic will allow us to reject the null hypothesis or not. Therefore, the regression analysis we note that the value of the t test is more consistent with the number of elderly in that area which, showing a value of 11.22. The income will allow us to reject the null hypothesis because the t-statistic is equal to 4:58, the same is not true for the presence of radioactive material on the Italian territory, with a value of 1.11 that we would not reject the null hypothesis to a minimum level of 1.96. It may help to decompose the effects of this regressive regression analysis combining different variables observed previously
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