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Table of Contents
What is Regression Analysis?
Terminologies related to regression analysis
Types of Regression
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Quantile Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Principal Components Regression (PCR)
- Partial Least Squares (PLS) Regression
- Support Vector Regression
- Ordinal Regression
- Poisson Regression
- Negative Binomial Regression
- Quasi Poisson Regression
- Cox Regression
- Tobit Regression
How to choose the correct regression model?
Summary
This is an in-depth tutorial on different types of regression analysis in statistics, mainly focusing on their applications in data science. It covers a variety of regression techniques, their assumptions, applications, and how they differ from each other.
Here's a summary:
Regression Analysis Overview: The post starts by defining regression analysis, which is used to model the relationship between a dependent variable and one or more independent variables. It's a common method for predictive modeling.
Types of Regression:
Linear Regression: Models the linear relationship between dependent and independent variables.
Polynomial Regression: Fits a non-linear relationship using polynomial functions of the independent variable.
Logistic Regression: Used when the dependent variable is binary.
Quantile Regression: Focuses on estimating quantiles (percentiles) of the dependent variable.
Ridge and Lasso Regression: These involve regularization to prevent overfitting, with Lasso also performing feature selection.
Elastic Net Regression: Combines L1 and L2 regularization, used when dealing with highly correlated variables.
Principal Components Regression (PCR) and Partial Least Squares (PLS) Regression: Both used for high-dimensional data, with PLS considering the response variable.
Support Vector Regression (SVR): Applies the principles of Support Vector Machines to regression.
Ordinal Regression: For ordinal dependent variables.
Poisson Regression: For count data.
Negative Binomial Regression: Similar to Poisson but for overdispersed count data.
Cox Regression: For time-to-event data analysis.
Tobit Regression: For censored dependent variables.
How to Choose the Correct Regression Model: The post discusses considerations for choosing the appropriate regression model based on the nature of the dependent variable and the specific characteristics of the data.
Practical Applications and Examples: Each type of regression is accompanied by practical examples and scenarios where they would be the most appropriate choice.
The post is aimed at readers with an interest in data science, providing them with a comprehensive understanding of various regression models and their applications.