# A Compiled List of Modeling Methods & Techniques

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There are no shortage of modeling methods and techniques out there for statistical analysts to utilize when working with data. Sometimes, it can be helpful to have a resource to look at and compare the value of a modeling method based on available data and the type of information an individual is interested in pulling from a dataset.

In this regard, I have curated a list of statistical techniques that can be used and when to use them. Eventually, a follow up article will be provided that discusses which packages in python can be used to easily access and deploy these techniques as well. Enjoy!

*Regression*

1. Linear Regression:

- Summary: Linear regression models the relationship between a dependent variable and one or more independent variables using a linear equation. It’s used when you want to predict a continuous target variable based on linear relationships with one or more predictors.

-When to use: Use linear regression when you have a clear linear relationship between the target variable and predictor(s) and assumptions of linearity, independence, and constant variance are met.

2. Multiple Linear Regression: