About assumptions and types of regression analysis

This article discusses the assumptions and types of regression analysis.

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Today we will learn about the assumptions and types of regression analysis.

Regression analysis is based on a variety of assumptions, and it is important to ensure that these assumptions are met.

Additionally, regression analysis is divided into several types depending on the relationship between dependent and independent variables.

Below we will explain the assumptions and types of regression analysis.

Assumptions of regression analysis

Linearity

The relationship between dependent and independent variables must be linear.

Independence

Each observation must be independent of the other.

Homoscedasticity

The variance of the error term must be constant. This ensures that the predicted values ​​and residuals do not show a consistent pattern across observations.

Normality

The error term must be normally distributed.

Linear Independence

There should be no multicollinearity among the independent variables.

Types of regression analysis

Linear Regression

There are simple linear regression and multiple linear regression, which analyze the linear relationship between dependent and independent variables.

Logistic Regression

Used for binomial classification problems, when the dependent variable is binomial.

Nonlinear Regression

Analyze nonlinear relationships between dependent and independent variables.

Generalized Linear Model (GLM)

It is applied when the distribution of the dependent variable is not normal, and logistic regression is an example of GLM.

Conclusion

It is important to meet the assumptions of regression analysis and choose the appropriate type of regression analysis.

This can be used to build reliable models and analyze data.

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