IQ_cat is a new, categorical variable that divides IQ into q…

IQ_cat is a new, categorical variable that divides IQ into quartiles. In other words, 1 indicates the respondent is in the bottom quarter of the distribution, 2 indicates the respondent is between the 25th and 50th percentile, 3 indicates the respondent is between the 50th and 75th percentile, and 4 indicates the respondent is in the top quarter of the distribution (so, those in category 4 are the “smartest”, assuming you trust IQ tests). You are interested in whether there is a relationship between IQ quartile and whether the respondent lives in a metro area as the first step in a broader analysis of the distribution of employees across different locations. So, you run the following in Stata: State the null and alternative hypotheses for this chi-square test.

You decide to substitute the continuous IQ variable for a se…

You decide to substitute the continuous IQ variable for a series of dummy variables indicating quartiles. IQ_1= 1 if the respondent is in the bottom quarter of the distribution, IQ_2=1 if the respondent is between the 25th and 50th percentile, IQ_3=1 if the respondent is between the 50th and 75th percentile, and IQ_4=1 if the respondent is in the top quarter of the distribution (so, the IQ_1 people are the “dumbest” and the IQ_4 people are the “smartest”, again, assuming you trust IQ tests). You re-run the regression and get the following output: Which of all the statistically significant coefficients (not just the IQ variables) has the largest association with wage? How do you know?