I currently have a data set where my dependent variable is a proportion (ex: the percent of a success). I currently have it set up where my y-values are between 0 and 1 (ex: 54.6% is 0.546). I decided to use logistic regression because my y-variable is between 0 and 1. However, since my y-variable is not binary, I assume I cannot use the family "binomial" in
It is also worthy to note that I am doing a class project so dispersion value is important as well as AIC. When I use the binomial family, R automatically makes the dispersion value as 1, which does not help me figure out if my data is overdispersed.
- Is logistic regression appropriate to use in this scenario?
- If the binomial family (
link = logit) is appropriate as well for logistic reg, how should I scale/change the weight so that I can get an accurate dispersion value, as opposed to the assumed "1"?
I am open to other methods for going about this. Thanks!
UPDATE: I was able to do some more research, and one of my independent variable "total population" is the total for the percentage proportion (had to calculate it myself to make sure it aligns). My y-variable is still the percentage/proportion of success that the event happened within the population.