Stata – be the master Stata

“After I have run my standard commands, what can I do to make my model better (and understand better what is going on)?”

Using dummies with interval variables can help improve fit -Create two extra dummies: one for here and one for here -Or (typically when you have a lot of data points): create dummies per group

Variables need not be normally distributed … but it is often nice if they are (and gladder price will give you a graphical representation as well)

interact.ado A command to generate interaction effects Centralizes automatically for interval variables (and that’s important) interact var1 var2, gen(var1_X_var2) Installation: + Download diagfiles.zip online + Put files in some folder + Add that folder to adopath (adopath + “/folderpath”) (+ Add this adopath statement to “profile.do”)

Interpreting interactions: when you have interactions, “there are no main effects any more”

Potential transformations - fracpoly … and there are several options, for instance to decide on the space of searched transformations

fracplot shows the estimated shape

Finding outliers - diag2.ado (but only possible after regress, and you have to keep thinking yourself!)

The better way to find outliers in logit: ldfbeta (“findit ldfbeta”)

Note: Actually not completely Correct. Better (but more tedious), is to standardize the X-variables first.

Other possibilities … Try to find a subset of your data for which your model works better / differently (typically easier when you know something about the topic substantially) Consider sequences of models, instead of focusing on “the best model”: 

Sequences of models (easiest when you do not have that many variables)

Handy bits of coding global VARS var1 var2 var3 … reg y $VARS forvalues i = 1/10 { gen var`i’ = (varindata == `i’) }

Granddad talking: More buttons get rid of determination …

squeeze, but be honest

To Do Back to your logistic regression assignment. Compare what others have done with the dataset that you had. Improve, squeeze, and deliver one assignment (make that a do-file) per data set