This means that when x 0 then the predicted value of y is 6.5. 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). Example: Interpreting the Equation for a Line Section. 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. Also, if X and Y are perfectly positively correlated, i.e., if Y is an exact positive linear function of X, then Yt Xt for all t, and the formula for rXY. Using MINITAB, the scatterplot and the least square line are: Temp. 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. From the printout, the least squares prediction equation is 295.25 16.364 y x. Instead of including multiple independent variables, we start considering the simple linear regression, which includes only one independent variable. 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 equation is called the regression equation. The other variable, y, is known as the response variable. One variable, x, is known as the predictor variable. 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). Simple linear regression is a statistical method you can use to understand the relationship between two variables, x and y.
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