We can use either the population or sample formulas for covariance (as long as we stick to one or the other). Where the coefficients b m are the solutions to the following k equations in k unknowns.Ĭlick here for a proof of Property 1 (using calculus). Property 1: The regression line has the form Where ŷ i is the y-value on the best-fit line corresponding to x, …, x ik.ĭefinition 1: The best-fit line is called the ( multiple) regression line As in the simple regression case, this means finding the values of the b j coefficients for which the sum of the squares, expressed as follows, is minimum: Given a set of n points ( x 11, …, x 1 k, y 1), …, ( x n 1, …, x nk, y n), our objective is to find a line of the above form which best fits the points. We will now extend the method of least squares to equations with multiple independent variables of the formĪs in Method of Least Squares, we express this line in the form
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