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Computer, Minitab, and other printouts % = −42.734 + 1.70() r2 = 67.8% 67.8% of the variation in percent body fat can be explained by the variation in waist size. 2 = .678 = .823 = .823 The correlation of .823 tells us that there is a fairly strong and positive relationship between percent body fat and waist size. Residual = − = observed – predicted % = −42.734 + 1.70() % = −42.734 + 1.70 35 = 16.766 Residual = 10 – 16.766 = - 6.766 Variable C1 Total Count 16 Variable C1 Median 0.4100 Mean 0.3281 SE Mean 0.0480 Q3 0.4650 Mean = 0.3281 Median = 0.4100 StDev 0.1920 Maximum 0.7000 Variance 0.0369 CoefVar 58.51 Minimum 0.0300 Q1 0.1300 Variable C1 Total Count 16 Variable C1 Median 0.4100 Mean 0.3281 Q3 0.4650 SE Mean 0.0480 StDev 0.1920 Variance 0.0369 Maximum 0.7000 Standard Deviation = 0.1920 IQR = Q3 – Q1 = 0.4650 – 0.1300 = 0.3350 CoefVar 58.51 Minimum 0.0300 Q1 0.1300 Variable C1 Total Count 16 Variable C1 Median 0.4100 Mean 0.3281 SE Mean 0.0480 Q3 0.4650 StDev 0.1920 Variance 0.0369 CoefVar 58.51 Minimum 0.0300 Q1 0.1300 Maximum 0.7000 Use the IQR = 0.3350. IQR X 1.5 = 0.3350 X 1.5 = 0.5025 Q3 + 0.5025 = 0.9675 Q1 – 0.5025 = - 0.3725 Compare to Minimum and Maximum values. If either are outside of this range, there are outliers. If not, there aren’t any. Because Min = 0.0300 and Max = 0.7000, there are no outliers. Y-intercept Standard error t - statistic Slope Correlation p - value