Autocorrelation is not expected to be an issue with the model, given that the it is not using time series data, and it can therefore be assumed that there is no autocorrelation present in the model.
To make sure that there is no perfect collinearity present in the model, it is recommended to run a collinearity test of the model (see: Exhibit 1.1). If a variable generate a VIF (Variance Inflation Factors) value above 10.0, the model might have a collinearity problem and attempts to correct the model should be made. However, the test did not generate any VIF values above 10.0 for any of the variables, thus not suggesting any problems and also implying that the model shows no perfect collinearity i.e. not violating the assumption. Moreover, the …show more content…
However, the “abvavg” variable does indicate a positive relationship with the hourly wage – suggesting that more physically attractive individuals do experience a monetary benefit because of their appearance, but the variable is deemed statistically insignificant and the results hold no weight in the model. Instead, “belavg” displays a negative relationship with the hourly wage, which indicates that it is less physically attractive individuals that are experiencing a monetary disadvantage as a result of their appearance. Two other variables of statistical significance are “exper”, indicating a positive relationship between the amount of experience an individual has and his or her hourly wage, and “educ”, which too indicates a positive relationship, but between the amount of education an individual has and his or her hourly wage, stating that for each additional year of schooling or workforce experience, and individual’s hourly wage will increase. Also explicitly evident in the model is the strong negative relationship the “female” variable has with hourly wages, which states that women generally make about $2.17 less per hour than men. Furthermore, neither the “service” variable’s nor the nor the “black” variable’s negative relationship are deemed statistically significant on their own, and it remains to be seen if they hold any value when combined with another variable in an interaction variable in later estimations. Meanwhile, the model’s adjusted R-squared is measured to be roughly 0.2095, which means that circa 20.95% of all variations in the hourly wage are accounted for by this
To make sure that there is no perfect collinearity present in the model, it is recommended to run a collinearity test of the model (see: Exhibit 1.1). If a variable generate a VIF (Variance Inflation Factors) value above 10.0, the model might have a collinearity problem and attempts to correct the model should be made. However, the test did not generate any VIF values above 10.0 for any of the variables, thus not suggesting any problems and also implying that the model shows no perfect collinearity i.e. not violating the assumption. Moreover, the …show more content…
However, the “abvavg” variable does indicate a positive relationship with the hourly wage – suggesting that more physically attractive individuals do experience a monetary benefit because of their appearance, but the variable is deemed statistically insignificant and the results hold no weight in the model. Instead, “belavg” displays a negative relationship with the hourly wage, which indicates that it is less physically attractive individuals that are experiencing a monetary disadvantage as a result of their appearance. Two other variables of statistical significance are “exper”, indicating a positive relationship between the amount of experience an individual has and his or her hourly wage, and “educ”, which too indicates a positive relationship, but between the amount of education an individual has and his or her hourly wage, stating that for each additional year of schooling or workforce experience, and individual’s hourly wage will increase. Also explicitly evident in the model is the strong negative relationship the “female” variable has with hourly wages, which states that women generally make about $2.17 less per hour than men. Furthermore, neither the “service” variable’s nor the nor the “black” variable’s negative relationship are deemed statistically significant on their own, and it remains to be seen if they hold any value when combined with another variable in an interaction variable in later estimations. Meanwhile, the model’s adjusted R-squared is measured to be roughly 0.2095, which means that circa 20.95% of all variations in the hourly wage are accounted for by this