Types of Regression Analysis Techniques

- Overview

Regression refers to a statistical technique that uses several explanatory variables to predict the outcome of a response variable. Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).

Regression analysis is a statistical procedure used to estimate the relationship between a dependent (or criterion variable) and one or more independent (or predictor) variables. 

Criterion Variable. In regression analysis (such as linear regression) the criterion variable is the variable being predicted. In general, the criterion variable is the dependent variable.

Regression analysis is usually used when we are dealing with a data set in which the target variable is in the form of continuous data. Regression analysis explains changes in a criterion with respect to changes in selected predictor variables. 

Standard conditional expectations are based on predictors, which give the mean of the dependent variable when the independent variable changes. 

The three main uses of regression analysis are to determine the strength of predictor variables, predictive effectiveness, and trend prediction.

- Types of Regression Analysis Techniques

The following are some types of regression analysis techniques, including: 

• Linear regression: The simplest form of regression, which helps estimate the relationship between dependent and independent variables.
• Multiple linear regression: A regression model that estimates the relationship between a quantitative dependent variable and two or more independent variables using a straight line.
• Logistic regression: Widely used to analyze categorical data, particularly for binary response data in business data modeling.
• Polynomial regression: A kind of linear regression in which the relationship shared between the dependent and independent variables Y and X is modeled as the nth degree of the polynomial.
• Lasso regression: A regularization technique that applies a penalty to prevent overfitting and enhance the accuracy of statistical models.
• Nonlinear regression: A statistical technique that helps describe nonlinear relationships in experimental data.

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