Given the model's intent which is to determine how much of wage is determined by the independent variables in the data, I would choose age. This is because age and wage have a correlation of .18 in comparison to the correlation between wage and experience that has a correlation of .09. The correlation of .18 means that the age and wage have more of a connection than wage and experience do. Wage and experience do not have as much of a linear connection as wage and age do. Since the linear correlation is stronger between wage and age, we know that we will get more of a solid expectation of the wage using age. Experience doesn't have as much of an affect or connection with wage. If wage and gender had a very high correlation of .8 or more, this would not be a multicollinearity issue. This is because wage is a dependent variable, while gender would be an independent variable. The multicollinearity issue is only between independent variables. Since they aren't both independent variables, we do not need to worry about multicollinearity. This correlation would just mean that gender does have a strong linear connection with wage and would