What is Linear Regression?
Linear Regression is a statistical method used to model the relationship between a dependent variable (also known as the response variable) and one or more independent variables (also known as predictor variables). The goal of Linear Regression is to find the best-fitting line through a set of data points, which can then be used to make predictions about the response variable for different values of the predictor variables.
Linear Regression models are represented by the equation y = mx + b, where y is the response variable, x is the predictor variable, m is the slope of the line, and b is the y-intercept. Simple Linear Regression is used to model the relationship between one predictor variable and one response variable.
Linear Regression is a widely used statistical technique and is often used in fields such as economics, engineering, and the natural sciences.
How to Calculate Regression Constant?
Regression Constant calculator uses Regression Constant = Mean of Y-(Regression Coefficient*Mean of X) to calculate the Regression Constant, Regression Constant formula is defined as the intercept of the regression line on the Y-axis. It represents the expected value of Y when X is 0. Regression Constant is denoted by b0 symbol.
How to calculate Regression Constant using this online calculator? To use this online calculator for Regression Constant, enter Mean of Y (ȳ), Regression Coefficient (b1) & Mean of X (x̅) and hit the calculate button. Here is how the Regression Constant calculation can be explained with given input values -> 50 = 200-(5*30).