Data
====

1. earnings1.data.R
  - N       : number of observations
  - earn_pos: is earnings positive? 1: Yes, 0: No
  - height  : height in inches
  - male    : is male? 1: Yes, 0: No

2. earnings2.data.R
  - N       : number of observations
  - earnings: earnings in dollars
  - height  : height in inches
  - sex     : 1: male, 2: female

3. wells.data.R
  - N      : number of observations
  - arsenic: level of arsenic of respondent's well
  - assoc  : any household members active in community organizations? 1: Yes, 0: No
  - dist   : distance (in meters) to closest known safe well
  - educ   : education level of head of household
  - switc  : household switched to new well? 1: Yes, 0: No

Models
======

1. One predictor
  wells_logit.stan : glm(switc ~ dist100, family=binomial(link="logit"))
  wells_probit.stan: glm(switc ~ dist100, family=binomial(link="probit"))

2. Multiple predictors with no interaction
  earnings1.stan: glm(earn_pos ~ height + male, family=binomial(link="logit"))

3. Log transformations
  earnings2.stan: lm(log(earnings) ~ height + male)