01:960:401 Lecture Notes - Lecture 3: Explained Variation, Total Variation
7 Oct 2015
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*the coefficient of correlation is (+) when slope is (+) and (-) when slope is (-) *r = 0 when (a) there is no correlation and (b) when there is a non-linear correlation shared variability of x y separate variability of x y = covariance of x y. Sum of products of deviations: the measure of shared. *sxy = x x x x y y y y y y x x . [is positive if x and y are both high or if x and y are both low; is negative if x is low and y is high or if x is high and y is low] 1 n 1 zx z y x x sx y y s y. ___% of the sample variation in y can be explained by using ___(x) to predict ___(y) in the linear model. Error = y yhat (actual predicted; ap) *least squares minimizes the sum of the squared differences (sse)
