Linear Least Squares Regression Line Equation Calculator
Calculates the Slope(m), Y - intercept and Least Square Regression Line Equation for given values.
The least squares linear regression is a method for predicting the value of a dependent variable Y, based on independent value of variable X.
If X is an independent variable and Y is a dependent variable, then the population regression line is:
Y = Β0 + Β1X
The regression line passes through the mean of the X values (x) and through the mean of the Y values (y) and the regression constant (b0) is equal to the y intercept of the regression line.
The slope of the regression line is the regression coefficient (b1) is the average change in the dependent variable (Y) for a one unit change in the independent variable (X) where b0 is the constant in the regression equation, b1 is the regression coefficient The least squares regression line for straight line, the line minimizes the sum of squared differences between observed values ( y values) and predicted values.
Difference between linear regression and finding a least squares square regression
The Linear regression is usually solved by minimizing the least squares error of the model to the data, therefore large errors are penalized quadratically. Least square regression is accurate in predicting continuous values from dependent variables.