Data for two variables, x and y, follow. X, 1 2 3 4 5 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) 9- (b) Plot the standardized residuals versus. 1 7 T 2 02 4 6 3 (c) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) Studentized XViDeleted Residual 4 5 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 ✔✔ 11 T 9 12 14 1 10 12 14 16 18 20 y 15 . 18 0 2 4 6 8 10 12 14 16 18 20 y Qo 02468 10 12 14 16 10 12 14 16 18 20 9 At the 0.05 level of significance, can any of these observations be classified as an outller? Explain. (Select all that apply.) t Observation x,- 1 can be classified as an outlier since it has a large studentized deleted residual (greater than 0.025 or less than -25) Observation x; -2 can be classified as an outlier since it has a large studentized deleted residual (greater than to.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 0.025 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 0.025 or less than -0.025). None of the observations can be classified as outliers since they do not have large studentized deleted residuals (greater than 0.025 or less than -0.025). X O 02 4 6 8 10 12 14 16 18 20 9 Q