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 your story.

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. Don’t attempt to theorize or justify blatantly counterintuitive results.

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.

4. Do not automatically disregard insignificant coefficients and interpret the independent variables as having “no effect” on your dependent variable. – We are simply less confident that the independent variable has an effect on Y within the population if it is insignificant – We still do not know whether the X variable has an effect on Y or not within the population

5. Recognize who your audience is (the audience for your paper is your professor). Given your audience, it makes no sense to explain within your narrative the meaning of the statistics you want to discuss. – You don’t have to explain what R-squared stands for – your reader already knows this – In your narrative, do not detail how you perform hypothesis tests. Just note whether the coefficient discussed is statistically significant or not and mention which level of significance you are using

6. Be careful about how you interpret your X and Y variables For example, consider the two independent variables: A.Proportion of the county’s adult residents who have at least a Bachelor’s degree B.Proportion of the county’s adult residents who have a high school degree or less

7. The bachelor variable is positively related to education level. The high school variable is inversely related to education level. Therefore, for example, if the researcher hypothesizes that education level has a positive effect on local economic growth. – We should expect a positive coefficient for the bachelor variable in a regression determining economic growth – We should expect a negative coefficient for the high school variable

8. 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.

9. 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.

10. Don’t misinterpret magnitudes. For example, suppose you have the regression model and b 1 =.05, b 2 =2.5. This doesn’t necessarily mean that X 2 has a larger impact on the dependent variable than X 1 has. You have to look at the units of X 1 and X 2. Suppose X 1 is untransformed population size and X 2 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 X 1 and X 2 actually vary.

11. 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.