How to interpret regression output with binary variables? I have trouble understanding the regression output that I created for my beginners class of R. I use two binary variables and ask whether the fact that someone volunteers raises hers or his life satisfaction.
I am familiar with the interpretation of numeric variables as "increase in X leads to decrease in Y". But how do I interpret binary variables? How do I handle the fact that X is not statistically significant while Y is?
This might be more of a Stackoverflow question: Should/Can I change the coding so that it examines the effect of people which are volunteering (volunteeryes)?


The intercept is the log odds of life satisfaction of the group that volunteers. Unsurprisingly, this group has a mean life satisfaction that is statistically significantly different from zero (not exactly as many satisfied and unsatisfied people).
The “volunteerno” parameter is the amount by which the log-odds of life satisfaction differs among people who do not volunteer. This is not significantly different from the mean life satisfaction of people who do volunteer.
Depending on how you view the world, you may find it surprising or upsetting that people tend not to be satisfied with their lives (negative log odds means a low probability of life satisfaction).
I would conclude that there is more to the probability of life satisfaction than volunteering (job, romance, successful children, etc). However, this study does not give evidence that volunteering contributes, with the usual caveats about interpreting large p-values and causality.
