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I received a new book titled The Obesity Code last week, written by Canadian nephrologist Jason Fung.

Over the last year, I had read a few things by Dr. Fung on his clinic’s blog, but one of the things he says about obesity in his book made me believe that the study of obesity has a lot in common with development economics. Specifically, on p. 216 of his book, Dr. Fung writes:

Picking up where I left off at the end of last week’s ‘Metrics Monday post, I wanted to continue discussing the interpretation of coefficients this week.

Specifically, I wanted to discuss the interpretation of coefficients on dummy variables in semi-logarithmic equations. What’s a semi-logarithmic equation? It’s an equation of the form

[math]\ln{y} = \alpha + \beta{D} + \gamma{x} + \epsilon[/math],*

On the subject of tests used to compare two distributions, Varun writes with two questions. His first question is as follows:

I teach part of a data analysis course at our institute. With the example of auto.dta the comes with Stata, we found that the variable miles per gallon (mpg) to be not normally distributed.
To find out whether mpg is statistically different for domestic (a sub-sample of 52 cases) and foreign (a sub-sample of 22 cases) cars (total sample size of 74), I told students there are two nonparametric tests, the Wilcoxon ranks-sum test and the two-sample Kolmogorov-Smirnov test. The Wilcoxon rank-sum test tests whether medians are significantly different while the two-sample Kolmogorov-Smirnov test tests whether distributions are different both groups. My students asked me which one they should go for between these two tests for mpg in auto.dta.