In statistics, regression is a statistical process for evaluating the connections among variables. Regression equation calculation depends on the slope and y-intercept. Enter the X and Y values into this online linear regression calculator to calculate the simple regression equation line.

In statistics, regression is a statistical process for evaluating the connections among variables. Regression equation calculation depends on the slope and y-intercept. Enter the X and Y values into this online linear regression calculator to calculate the simple regression equation line.

Code to add this calci to your website

x and y are the variables.
m = The slope of the regression line
a = The intercept point of the regression line and the y axis.
N = Number of values or elements
X = First Data Set
Y = Second Data Set
ΣXY = Sum of the Product of First and Second Data Set
ΣX_{m} = Mean of First (X) Data Set
ΣY_{m} = Sum of Second (Y) Data Set
ΣX^{2} = Sum of Square of First (X) Data Set Values

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). Linear regression is the technique for estimating how one variable of interest (the dependent variable) is affected by changes in another variable (the independent variable). If it is one independent variable, it is called as simple linear regression. When there are more than one independent variable it is called as multiple linear regression. This statistics online **linear regression calculator** will determine the values of b and a for a set of data comprising two variables, and estimate the value of Y for any specified value of X.

To calculate the simple linear regression equation,

let consider the two variable as dependent (x) and the the independent variable (y).

X = 4, Y = 5

X = 6, Y = 8

Applying the values in the given formulas,

You will get the slope as 1.5, y-intercept as -1 and the regression equation as -1 + 1.5x.