Regression Line
Linear regression models aim to find a line equation that best represents the relationship between dependent (y) and independent (x) variables.
y=a + bx
- y is the dependent variable, also known as the response or explained variable.
- x is the independent variable, also known as the predictor or explanatory variable.
- a is the y-intercept, which represents the value of y when x is 0.
- b is the slope of the line, indicating the change in y for a one-unit change in x. It represents the strength and direction of the relationship between x and y.
Regression Coefficients
Regression coefficients in linear regression are the amounts by which variables in a regression equation are multiplied. Linear regression is the most commonly used form of regression analysis. Linear regression aims to determine the regression coefficients that result in the best-fitting line. These coefficients are helpful when estimating the value of an unknown variable using a known variable. This article explains regression coefficients and their formulas and provides related examples.
Table of Content
- What are Regression Coefficients?
- Regression Line
- Formula for Regression Coefficients
- Regression Coefficients Interpretation
- Steps to Calculate the Regression Coefficient
- Regression Coefficients in Different Types of Regression Models
- Solved Examples on Regression Coefficients