A statistical program is recommended. Consider the following data for two variables, x and y. X ; 5 8 10 12 12 22 yi 12 14 16 15 18 20 25 18 (a) Compute the standardized residuals for these data. (Round your answers to two decimal places.) X; yi Standardized Residuals 4 12 5 14 16 15 10 18 12 20 12 25 22 18 Do the data include any outliers? Explain. O There are no standardized residuals that are less than -2 or greater than +2, so there are no possible outliers. There is one standardized residuals that is less than -2 or greater than +2, so there is one possible outlier. There are two standardized residuals that are less than -2 or greater than +2, so there are two possible outliers. There are more than two standardized residuals that are less than -2 or greater than +2, so there are more than two possible outliers. (b) Compute the leverage values for these data. (Round your answers to two decimal places.) Xi yi Leverage Values 12 5 14 16 15 10 18 12 20 12 25 22 18 Do there appear to be any influential observations in these data? Explain. Minitab identifies an observation as having high leverage if h, > -; for these data, - n Since the leverage for the observation at (x,, y)) = is greater than -, we conclude that this observation is an influential observation (c) Develop a scatter diagram for these data. 30 y 30 25 25 20 20 15 15 10 10 5 5 5 10 15 20 25 5 10 15 20 25 30- 30 25 25 20 20 15 15 10 10 5 5 10 15 X 20 25 10 15 X 20 25 Does the scatter diagram indicate any influential observations? Explain. The scatter diagram indicates that there are no influential observations because no points have a large influence on the estimated regression line. The scatter diagram indicates that there is one influential observation because one point has a large influence on the estimated regression line. The scatter diagram indicates that there are two influential observations because two points have a large influence on the estimated regression line. The scatter diagram indicates that there are more than two influential observations because more than two points have a large influence on the estimated regression line