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Unformatted text preview: 6.2142857*4 = 10.7142858 Thus, the regression line for the original 7 pairs is ˆ y = 6.2142857*x  10.7142858 x y 7 6 5 4 3 2 1 60 50 40 30 20 10 Scatterplot of y vs x Now drop the outlier we found earlier and find the correlation. So, we are only working with 6 pairs of numbers. X = x n ∑ = 21/6 = 3.5 Y = y n ∑ = 44/6 = 7.333333333 b = SSxy/SSxx = 31/17.5 = 1.771428571 a = Y b* X = 7.33333333– 1.771428571*3.5 = 1.13333333 Thus, the regression line for the 6 pairs after the outlier is dropped. ˆ y = 1.771428571*x + 1.133333333 x_1 y_1 6 5 4 3 2 1 12 10 8 6 4 2 Scatterplot of y_1 vs x_1 Now look at the two regression lines superimposed on the same graph XData YData 7 6 5 4 3 2 1 60 50 40 30 20 10 Variable y * x y_1 * x_1 Scatterplot of y vs x, y_1 vs x_1 The outlier pulls the regression line towards it....
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 Fall '07
 MELutz
 Statistics, Harshad number, regression line, pl ot, outlier effect

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