how to write a simple linear regression equation

For example, if you wanted to generate a line of best fit for the association between height and shoe size, allowing you to predict shoe size on the basis of a person's height, then height would be your independent variable and shoe size your dependent variable). To begin, you need to add paired data into the two text boxes immediately below (either one value per line or as a comma delimited list), with your independent variable in the X Values box and your dependent variable in the Y Values box.

This 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. The line of best fit is described by the equation ŷ = bX + a, where b is the slope of the line and a is the intercept (i.e., the value of Y when X = 0). It provides a mathematical relationship between the. The y is the value we are trying to forecast, the b is the slope of the regression line, the x is.

This simple linear regression calculator uses the least squares method to find the line of best fit for a set of paired data, allowing you to estimate the value of a dependent variable ( Y) from a given independent variable ( X). In simple linear regression, the starting point is the estimated regression equation: b0 + b1x. Below is the formula for a simple linear regression. Execute a method that returns some important key values of Linear Regression: slope, intercept, r, p, stderr stats.linregress (x, y) Create a function that uses the slope and intercept values to return a new value.

How to write a simple linear regression equation