![]() 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). Thus, the residual for this data point is 62 63.7985 -1.7985. 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. To find out the predicted height for this individual, we can plug their weight into the line of best fit equation: height 32.783 + 0.2001 (weight) Thus, the predicted height of this individual is: height 32.783 + 0.2001 (155) height 63.7985 inches. 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. In simple linear regression, the starting point is the estimated regression equation: b 0 + b 1 x. ![]() Approximately 44 percent of the variation (0.4397 is approximately 0.44) in the final exam grades can be explained by the variation in the grades on the third exam, using the best-fit regression line. 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). Interpret r2 in the context of this example. 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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