# How to write a regression equation

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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. 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. 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. 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. This calculator can estimate the value of a dependent variable Y for any specified value of an independent variable X. Simply add the X values for which you wish to generate an estimate into the Estimate box below either one value per line or as a comma delimited list.

## Linear Regression With R

Imagine you have some points, and want to have a line that best fits them like this:. We can place the line "by eye": try to have the line as close as possible to all points, and a similar number of points above and below the line. But for better accuracy let's see how to calculate the line using Least Squares Regression. Our aim is to calculate the values m slope and b y-intercept in the equation of a line :. Sam hears the weather forecast which says "we expect 8 hours of sun tomorrow", so he uses the above equation to estimate that he will sell. It works by making the total of the square of the errors as small as possible that is why it is called "least squares" :. The straight line minimizes the sum of squared errors.

### Regression

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Published on February 25, by Rebecca Bevans. Revised on December 14, Linear regression is a regression model that uses a straight line to describe the relationship between variables. It finds the line of best fit through your data by searching for the value of the regression coefficient s that minimizes the total error of the model.
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