Tuesday, September 22, 2009
Part 3: Top Richest people in the Forbes
III. Econometric Model:
This study made used of multiple Regression to establish whether Billionaires, who are citizens of the United States and have more life experiences, have more wealth than those who are younger and from other countries.
--------Y = β0 + β1X1 + β2X2 + β3X3 + є
where: β0 = the Y-intercept of the regression surface
--------βi = i = 1, 2,3 is the slope of the regression surface
--------X = the independent variable (Age, Citizenship, and Residence)
--------Y = the dependent variable (Wealth)
--------є = the random error
IV. Empirical results
This section presents the comparison and correlation of the variables. This study aims to determine if Billionaires, who are citizens of the United States and have more life experiences, have more wealth than those who are younger and from other countries.
Using 0.05 level of significance, F-statistics, and an Analysis of variance (ANOVA) test, it was established that the df num value is k-1, or 4 -1, or 3 and the df den value is T-k, or 2 4 - 3, or 21. So, with α = 0.05, the critical value of F in this analysis of variance test was F0.05 (3, 21) = 8.66. Since computed F (FC) is less than Tabulated F (FT), Ho is accepted. This is because the result of the “Analysis of Variance” (ANOVA) shows that the computed F (0.121) is less than the tabular values of F-statistics (8.66) at 0.05 degree of freedom (3, 21). This means that Billionaires, who are citizens of the United States and have more life experiences, have more wealth than those who are younger and from other countries.
The resultant R Square 0.017 is very far to 1 which means that the correlation is very far from the normal curve distribution, so, it is interpreted as very small positive correlation. Thus, in percentile (%), 1.7 is an indicator of significant relationship between Billionaires Wealth (Y) to the Age (X1), Citizenship (X2), and the Residence (X3). Finally, the R square of 0.017 is very close to the adjusted R square - 0.123. This means that the data for regression model are not fit.
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