Principles of Regression Presentation

1. Discuss in your text the size of the important coefficients, not just their significance. If the x-value changes by 10%, how much does the y-value change? You do not need to do this for all x-variables, but do it for the ones whose effects you are really interested in (as opposed to control variables that are just holding everything else constant).

2. Do not write 1.23423 when rounding to 1.23 will do just as well. Fewer digits yield greater clarity.

3. Use correlation matrices to show the simple correlations between important variables.

4. Give summary statistics. Think about which are most useful. Think about presenting the mean, median, mode, minimum, maximum, standard deviations, and number of observations. Do not present all of these--think.

5. Use words for variable names, not computer codes.

6. Present the coefficients, standard errors or t-statistics (not both), R2, and number of observations. Do not present other statistics (e.g. an F-test for all coefficients equalling zero) unless you have a reason to. Maybe use stars for significance-- * for 10% level, ** for 5%, and *** for 1%.

7. If the left-hand variable (y-variable, dependent variable, endogenous variable) takes only a few values (e.g., 0 and 1) then use a special technique such as logit or tobit. If a right-hand variable (x-variable, independent variable, exogenous variable) takes only a few values, that does not create a need to use anything besides OLS.

8. If you use a technique such as logit for which the coefficient values have little meaning, do not report them in your tables. Instead, report the "marginal effects" which show how a small change in the x-value affects the y-value, evaluated at the average or median values of all the x-values. You do not need to do this for OLS or 2SLS; you do need to for logit, probit, or tobit.