For example, if you wanted to generate a line of best fit for the association between height, weight and shoe size, allowing you to predict shoe size on the basis of a person's height and weight, then height and weight would be your independent variables ( X 1 and X 1) and shoe size your dependent variable ( Y). All you need is enter paired data into the text box, each pair of x. The line of best fit is described by the equation f (x) Ax + B, where A is the slope of the line and B is the y-axis intercept. ![]() To begin, you need to add data into the three text boxes immediately below (either one value per line or as a comma delimited list), with your independent variables in the two X Values boxes and your dependent variable in the Y Values box. This linear regression calculator uses the least squares method to find the line of best fit for a set of paired data. The first formula is specific to simple linear regressions, and the second formula can be used to calculate the R² of many types of statistical models. ![]() This calculator will determine the values of b 1, b 2 and a for a set of data comprising three variables, and estimate the value of Y for any specified values of X 1 and X 2. You can choose between two formulas to calculate the coefficient of determination (R²) of a simple linear regression. ![]() The line of best fit is described by the equation ŷ = b 1X 1 + b 2X 2 + a, where b 1 and b 2 are coefficients that define the slope of the line and a is the intercept (i.e., the value of Y when X = 0). Table of Contents Introduction Creating an initial scatter plot Creating a Linear Regression Line (Trendline) Using the Regression Equation to Calculate. Interpreting results Using the formula Y mX + b: The linear regression interpretation of the slope coefficient, m, is, 'The estimated change in Y for a 1-unit increase of X.' The interpretation of the intercept parameter, b, is, 'The estimated value of Y when X equals 0.' The first portion of results contains the best fit values of the slope and Y-intercept terms. This simple multiple linear regression calculator uses the least squares method to find the line of best fit for data comprising two independent X values and one dependent Y value, allowing you to estimate the value of a dependent variable ( Y) from two given independent (or explanatory) variables ( X 1 and X 2).
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