Data for two variables, x and y, follow. X, 1 2 3 4 y 7 11 9 15 18 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ý= (b) Plot the standardized residuals versus ý. standardized Residual O 0 246 8 10 12 14 16 18 20 ý 39 4 15 standardized Residual (c) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) Studentized x, y, Deleted Residual 17 2 11 L 0 246 8 10 12 14 16 18 20 ý Do there appear to be any outliers in these data? Explain. The value of the standardized residual for no observations ✔✔✔ is either greater than -2 or less than -2. Therefore, there are no outliers [ Standardized Residual 0246 8 10 12 14 16 18 20 ý 5 18 At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation x = 1 can be classified as an outlier since it has a large studentized deleted residual (greater than 25 or less than -25). Observation x = 2 can be classified as an outlier since it has a large studentized deleted residual (greater than 0.025 or less than -0.025). Observation x = 3 can be classified as an outlier since it has a large studentized deleted residual (greater than 25 or less than -0.025). Observation x, = 4 can be classified as an outlier since it has a large studentized deleted residual (greater than 0.025 or less than -0.025)- Observation x = 5 can be classified as an outlier since it has a large studentized deleted residual (greater than t or less than-track Standardized Residual O 0246 6 8 10 12 14 16 18 20 ÿ @

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