0. I measure Show HN projects, some of which are startups. The dead&#x2F;alive status as success is a proper measure for survival. For success, see the section on commercial success.<p>1. OLS works fine in classification problems. And it has advantages. For example, see <a href="http:&#x2F;&#x2F;chrisblattman.com&#x2F;2015&#x2F;07&#x2F;22&#x2F;statistician-neal-beck-just-justified-my-longstanding-hatred-and-loathing-of-logit&#x2F;" rel="nofollow">http:&#x2F;&#x2F;chrisblattman.com&#x2F;2015&#x2F;07&#x2F;22&#x2F;statistician-neal-beck-j...</a><p>And I purposefully included results from three models, not one. They all say the same thing.<p>2. Multicollinearity refers to perfect multicollinearity. Having correlated independent variables (as in score and comments) are okay. See <a href="http:&#x2F;&#x2F;stats.stackexchange.com&#x2F;questions&#x2F;86269&#x2F;what-is-the-effect-of-having-correlated-predictors-in-a-multiple-regression-mode" rel="nofollow">http:&#x2F;&#x2F;stats.stackexchange.com&#x2F;questions&#x2F;86269&#x2F;what-is-the-e...</a><p>3. I mention R² as one measure of predictive power.<p>4. The regressions without GitHub-hosted projects return same results.