A statistical program is recommended. Consider the following data for two variables, x and y. 135 110 130 145 175 160 120 145 100 120 115 135 130 110 (a) Compute the standardized residuals for these data. (Round your answers to two decimal places.) Standardized Residuals 135 145 110 100 130 120 145 115 175 135 160 130 120 110 Do the data include any outliers? Explain. (Round your answers to two decimal places.) The standardized residual with the largest absolute value is | , corresponding to y, = . Since this residual is |greater than +2 v it could be an outlier. (b) Plot the standardized residuals against y. 2.5 2.5 2.5 2.5 1.5 1.5 1.5 1.5 0.5 0.5 0.5 0.5 Standardized Residuals Standardized Residuals Standardized Residuals -0.5 -0.5- -0.5 - -0.5 Standardized -1.5 -1.5 -1.5 -1.5 -2.5+ -2.5 -2.5 105 110 115 120 125 130 135 140 -2.5+ 105 110 115 120 125 130 135 140 105 110 115 120 125 130 135 140 105 110 115 120 125 130 135 140 O Does this plot reveal any outliers? O The plot shows no possible outliers. O The plot shows one possible outlier. O The plot shows two possible outliers. The plot shows more than two possible outliers.(c) Develop a scatter diagram for these data. J 150T 1507 150 T 150 140 140 140 140 130 130 130 130 120- 120- 120- 120- 110 110 110 110 100 100 100 100 X 110 120 130 140 150 160 170 180 110 120 130 140 150 160 170 180 X 110 120 130 140 150 160 170 180 110 120 130 140 150 160 170 180 O Does the scatter diagram indicate any outliers in the data? The diagram indicates that there are no possible outliers. O The diagram indicates that there is one possible outlier. O The diagram indicates that there are two possible outliers. O The diagram indicates that there are more than two possible outliers. In general, what implications does this finding have for simple linear regression? For simple linear regression, we must calculate standardized residuals, plot a standardized residual plot, and construct a scatter diagram to identify an outlier. O For simple linear regression, it is impossible to determine whether there is an outlier using standardized residuals, a standardized residual plot, or a scatter diagram. O For simple linear regression, we can determine an outlier by looking at the scatter diagram.A statistical program is recommended. Charity Navigator is America's leading independent charity evaluator. The following data show the total expenses ($), the percentage of the total budget spent on administrative expenses, the percentage spent on fundraising, and the percentage spent on program expenses for 10 supersized charities. Administrative expenses include overhead, administrative staff and associated costs, and organizational meetings. Fundraising expenses are what a charity spends to raise money, and program expenses are what the charity spends on the programs and services it exists to deliver. The sum of the three percentages does not add to 100% because of rounding. Total Administrative Fundraising Program Charity Expenses Expenses Expenses Expense ($) (%) (%) (%) A 3,354,177,445 3.9 3.8 92.1 B 1,205,887,020 4.0 7.5 88.2 C 1,080,995,083 23.5 2.6 73.7 D 1,050,829,851 0.7 2.4 96.8 E 1,003,781,897 6.1 22.2 71.6 F 929,158,968 8.6 1.5 89.6 877,321,613 13.1 1.6 85.2 H 854,604,824 0.4 0.7 98.8 T 829,662,076 9.6 16.9 73.4 736,176,619 13.7 3.0 83.1 (a) Develop a scatter diagram with fundraising expenses (%) on the horizontal axis and program expenses (%) on the vertical axis. 120 7 120 120- 100- 100 100 80 80 80 60 Program Expenses (%) Program Expenses (%% 60 60 Program Expenses ( 40 40 40 20 5 10 15 20 25 5 10 15 20 25 5 10 15 20 25 Fundraising Expenses (%) Fundraising Expenses (%) DO Fundraising Expenses (%) 120 T 100 . 80 Program Expenses (%%) 60 40 20 0 5 10 15 20 25 O Fundraising Expenses (%)Looking at the data, do there appear to be any outliers and/or influential observations? O There appear to be no possible outliers or influential observations. O There appears to be one possible outlier or influential observation. There appear to be two or more possible outliers and/or influential observations. (b) Develop an estimated regression equation that could be used to predict program expenses (%) given fundraising expenses (%). (Round your numerical values to two decimal places.) (c) Does the value for the slope of the estimated regression equation make sense in the context of this problem situation? For every one percentage point increase in the amount spent on fundraising, the percentage spent on program expenses will increase by the value of the slope found in part (b). The positive slope and value seem to make sense in the context of this problem situation. For every one percentage point increase in the amount spent on fundraising, the percentage spent on program expenses will decrease by the value of the slope found in part (b). The negative slope and value seem to make sense in the context of this problem situation. For every one percentage point increase in the amount spent on fundraising, the percentage spent on program expenses will increase by the value of the slope found in part (b). The negative slope and value do not seem to make sense in the context of this problem situation. For every one percentage point decrease in the amount spent on fundraising, the percentage spent on program expenses will decrease by the value of the slope found in part (b). The negative slope and value seem to make sense in the context of this problem situation. For every one percentage point decrease in the amount spent on fundraising, the percentage spent on program expenses will decrease by the value of the slope found in part (b). The positive slope and value do not seem to make sense in the context of this problem situation. (d) Use residual analysis to determine whether any outliers and/or influential observations are present. (Select all that apply.) O Charity C is a possible outlier because it has a large standardized residual (less than -2 or greater than +2). O Charity C is a possible influential observation because it has high leverage (greater than 6). O Charity E is a possible outlier because it has a large standardized residual (less than -2 or greater than +2). O Charity E is a possible influential observation because it has high leverage (greater than 6). O Charity H is a possible outlier because it has a large standardized residual (less than -2 or greater than +2). O Charity H is a possible influential observation because it has high leverage (greater than 6). There are no possible outliers present. There are no possible influential observations present. Briefly summarize your findings and conclusions