![]() ![]() The equation of linear regression is similar to the slope formula what we have learned before in earlier classes such as linear equations in two variables. 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). Linear regression shows the linear relationship between two variables. Use your model to make prediction given an x-value. 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. Choose from a variety of regression models including Linear, Quadratic, Logarithmic, Exponential and more Navigate to the regression model to view the regression equation and the predicted value. 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. Instant Results: Get the equation of the regression line and the correlation coefficient at the click of a button. ![]() 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). The Regression Calculator offers: Easy Input: Input your data points as comma-separated values for X and Y variables. ![]() It’s helpful to know the estimated intercept in order to plug it into the regression equation and predict values of the dependent variable: heart disease 15 + (-0.2biking) + (0.178smoking) ± e. this is the y-intercept of the regression equation. 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). Multiple linear regression is used to estimate the. ![]()
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