Regression is a very general term - notice the similarities between neural nets and regression, for instance. Another example is structural equation modeling (SEM). About 40 years ago UCLA professor Peter Bentler and his doctoral student David Weeks showed how SEM could be represented as regression, which reduced the number of required matrices from eight in the traditional LISREL notation to three. Results will be identical. Factor analysis and latent class clustering can also be conceptualized as regression.
I did not have space to mention clustered (multilevel) data. In some cases ignoring hierarchical structure in data can be very consequential and cause us to misinterpret our data. Sometimes different models are required for different levels of the data, as well.
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Post #2002
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AI, Python, Cognitive Neuroscience When regression models perform poorly, there are typically several reasons. Here are some: Important variables have been omitted from the model. These may include latent (unobservable) variables, such as socio-economic status. Errors in the data and missing…