## How many types of regressions are there?

On average, analytics professionals know only 2-3 types of regression which are commonly used in real world. They are linear and logistic regression. But the fact is there are more than 10 types of regression algorithms designed for various types of analysis. Each type has its own significance.

**What is the difference between OLS and linear regression?**

Yes, although ‘linear regression’ refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data. Linear regression refers to any approach to model a LINEAR relationship between one or more variables.

### What do regressions mean?

Definition of regression 1 : the act or an instance of regressing. 2 : a trend or shift toward a lower or less perfect state: such as. a : progressive decline of a manifestation of disease. b(1) : gradual loss of differentiation and function by a body part especially as a physiological change accompanying aging.

**What are the most commonly pronounced assumptions for linear regression?**

What are the key assumptions of linear regression?

- Validity. Most importantly, the data you are analyzing should map to the research question you are trying to answer.
- Additivity and linearity.
- Independence of errors. . . .
- Equal variance of errors. . . .
- Normality of errors. . . .

## What is TSS ESS and RSS?

TSS = ESS + RSS, where TSS is Total Sum of Squares, ESS is Explained Sum of Squares and RSS is Residual Sum of Suqares. The aim of Regression Analysis is explain the variation of dependent variable Y.

**Is regression supervised or unsupervised?**

Regression analysis is a subfield of supervised machine learning. It aims to model the relationship between a certain number of features and a continuous target variable.