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Which algorithm can be used for both continuous and binary dependent variables?
Regression models aim to project value based on independent features. The main difference that makes both different from each other is when the dependent variables are binary logistic regression is considered and when dependent variables are continuous then linear regression is used.
What if you have multiple dependent variables?
When multiple dependent variables are different measures of the same construct—especially if they are measured on the same scale—researchers have the option of combining them into a single measure of that construct. If they have poor internal consistency, then they should be treated as separate dependent variables.
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 the best type of regression to use?
There is only one independent and dependent variable. The type of regression line: a best fit straight line. Simple linear regression allows a data scientist or data analyst to make predictions about only one variable by training the model and predicting another variable.
How to choose an appropriate statistical test for two dependent variables?
This table is designed to help you choose an appropriate statistical test for data with two or more dependent variables. Hover your mouse over the test name (in the Test column) to see its description. The Methodology column contains links to resources with more information about the test.
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.