If you have a continuous dependent variable, linear regression is probably the first type you should consider. Linear models are the most common and most straightforward to use. This analysis estimates parameters by minimizing the sum of the squared errors (SSE).
Related posts: Nominal, Ordinal, Interval, and Ratio Scales, Guide to Data Types and How to Graph Them, and Independent and Dependent Variables Explained
This process should help narrow the choices! I’ll cover regression models that are appropriate for dependent variables that measure continuous, categorical, and count data. If you’re not sure which procedure to use, determine which type of dependent variable you have, and then focus on that section in this post. I organize the types of regression by the different kinds of dependent variable. I’ll provide an overview along with information to help you choose. In this post, I cover the more common types of regression analyses and how to decide which one is right for your data. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. There are numerous types of regression models that you can use. Regression analysis mathematically describes the relationship between a set of independent variables and a dependent variable.