Look for patterns in your estimates and write in terms of the ideas that drew you to the study.

1. Do not think the point of your paper is to interpret in detail every single regression statistic. • Don’t give each of the independent variables equal emphasis in your discussion. • Spend more time with the variables that are central to the story of your paper.

2. Include all the variables in your regression models that you think have an effect on your dependent variable. Don’t drop variables just because they are statistically insignificant. • Do not ignore the empirical results and simply discuss what makes sense. If a result is counter-intuitive, acknowledge it in your discussion.

3. Do not confuse X with Y in discussing your empirical results. Your purpose in interpreting results is to explain the variation in Y. The reason for the variation in Y is the variation in X. • If you talk about sign and/or magnitude of a relationship, acknowledge whether the relationship is statistically significant. It is ok to discuss insignificant relationships.

4. Look for patterns in your estimates and write in terms of the ideas that drew you to the study. • Do not feel obligated to, in every case, mechanically present a scenario expressing magnitudes: “a 10% increase in income will cause…..” If there is a reason to concentrate on the magnitude of a specific variable, then concentrate on the magnitude. Otherwise, focus on sign and significance.

5. Be careful in interpreting the magnitude of percentage variables in linear models. • Pay attention to the variables you are constructing. Look at your data! If, for example a variable representing population size turns out to have some negative values, you know something must be wrong.

6. Don’t misinterpret magnitudes. • For example, suppose you have the regression model and b1=.05, b2=2.5. This doesn’t necessarily mean that X2 has a larger impact on the dependent variable than X1 has. You have to look at the units of X1 and X2. • Suppose X1 is untransformed population size and X2 is income in $1000’s. What would have a larger effect: an additional 1000 people or a $1000 increase in income? • You also have to look at how much X1 and X2 actually vary.

7. Do not analyze above your head. For example, if you are shaky on using p-values to perform hypothesis tests, don’t use them; use the t-statistic! • Don’t interpret a negative coefficient as meaning the relationship between the respective X and Y is weak. • Don’t be wordy. Get to your point. Don’t feel obligated to pad.