I find it interesting that you lack familiarity with log-odds? What field are you in? Statisticians will usually be familar with them, as the logit is the canonical link function for the binomial function when using general linear modeling. Cut of (some) jargon, if I have a data set with binomial outcomes, and I wish to model my data as having normal errors, and the predictors as having linear effect on the outcome, I’d convert my data by using log odds. So, for instance, if I was looking at age as a predictor for diabetes (which is a yes no outcome)
I find it interesting that you lack familiarity with log-odds? What field are you in? Statisticians will usually be familar with them, as the logit is the canonical link function for the binomial function when using general linear modeling. Cut of (some) jargon, if I have a data set with binomial outcomes, and I wish to model my data as having normal errors, and the predictors as having linear effect on the outcome, I’d convert my data by using log odds. So, for instance, if I was looking at age as a predictor for diabetes (which is a yes no outcome)
I have a very strong competition math background from high school, but my primary field is chemistry.