![]() ![]() It takes a value between zero and one, with zero indicating the worst fit and one indicating a perfect fit. The r 2 is the ratio of the SSR to the SST. 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). Now that we know the sum of squares, we can calculate the coefficient of determination. ![]() 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. Summary: In this article we looked at the calculated behind the simple linear regression equation. 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). The equation of a line y mx + c is also used to calculate the linear regression. In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. We see that the results are exactly the same as calculated by hand. Here, one variable is supposed to be independent, while the other is supposed to be dependent. 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). Linear regression is defined as a data technique that determines the relationship between two variables by applying a linear equation to the given data. ![]()
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