What type of regression analysis deals with an independent and a dependent variable in a linear relationship?

Linear regression is a statistical method used to model the relationship between two variables by fitting a linear equation to observed data. In this case, one variable is considered the independent variable (predictor) and the other is the dependent variable (response). The primary objective is to find the best-fitting straight line through the data points that can be used to predict the dependent variable based on the independent variable.

In the context of linear regression, the relationship is expressed as a linear equation, typically in the form of (y = mx + b), where (y) is the dependent variable, (m) is the slope of the line, (x) is the independent variable, and (b) is the y-intercept. This indicates a direct proportionality: as the independent variable changes, the dependent variable changes linearly.

In contrast, polynomial regression involves a nonlinear relationship by including polynomial terms, meaning that it fits a polynomial equation rather than a straight line. Logistic regression is used for binary outcome variables, focusing on estimating the probability that an event occurs, making it unsuitable for scenarios that require modeling linear relationships. Multiple regression extends linear regression by using multiple independent variables to predict the dependent variable, but at its core, it still relies on