![]() In this technique, independent variables are used to predict the value of a dependent variable. The only 'trick' is that the additional series need to use variables in the same data set as the regression data. 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 is a statistical technique/method used to study the relationship between two continuous quantitative variables. 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 ลท = bX + a, where b is the slope of the line and a is the intercept (i.e., the value of Y when X = 0). 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). ![]()
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