Can you run a regression with non normal data?
It seems like it’s working totally fine even with non-normal errors. In fact, linear regression analysis works well, even with non-normal errors. When errors are not normally distributed, estimations are not normally distributed and we can no longer use p-values to decide if the coefficient is different from zero.
What does linear regression with only categorical dependent variables look like?
A linear regression with only categorical dependent variables is called ANOVA. This website explains pretty clearly what the binary variables look like. Essentially, you will have a new variable for each level of your factor (e.g. White Flour). The new variable will take the value 1 if that recipe used white flour and 0 if it did not.
Can a binary dependent variable be used in regression?
Yes you can! In your case, you’re talking about a binary dependent variable because it has only two levels (presumably), admitted and not admitted. In that case, you’d use binary logistic regression and it’s fine to use a binary (or categorical) independent variable.
Which is better nonlinear regression or linear regression?
PLS uses the correlation structure to identify smaller effects and model multivariate patterns in the dependent variables. Nonlinear regression also requires a continuous dependent variable, but it provides a greater flexibility to fit curves than linear regression. Like OLS, nonlinear regression estimates the parameters by minimizing the SSE.
What’s the difference between PLS and linear regression?
Unlike OLS, you can include multiple continuous dependent variables. PLS uses the correlation structure to identify smaller effects and model multivariate patterns in the dependent variables. Nonlinear regression also requires a continuous dependent variable, but it provides a greater flexibility to fit curves than linear regression.