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A statistical program is recommended. You may need to use the appropriate appendix table or technology to answer this question. Data for two variables, x and y, follow. x; 22 24 26 28 40 Y; 17 26 36 40 75 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ŷ=-48.28 +3.11x (b) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) Studentized Deleted Residual 22 17 -3.14 24 26 26 36 28 40 40 75 X At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation x, = 22 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x, = 24 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x₁ = 26 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x, = 28 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x₁ = 40 can be classified as an outlier since it has a large studentized deleted residual (greater than to.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 to.025 or less than -0.025).

A statistical program is recommended. You may need to use the appropriate appendix table or technology to answer this question. Data for two variables, x and y, follow. x; 22 24 26 28 40 Y; 17 26 36 40 75 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ŷ=-48.28 +3.11x (b) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) Studentized Deleted Residual 22 17 -3.14 24 26 26 36 28 40 40 75 X At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation x, = 22 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x, = 24 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x₁ = 26 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x, = 28 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x₁ = 40 can be classified as an outlier since it has a large studentized deleted residual (greater than to.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 to.025 or less than -0.025).

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.CR: Chapter 1 Review
Problem 86CR
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(c) Compute the leverage values for these data. (Round your answers to two decimal places.) Leverage Value 22 24 26 36 17 28 40 26 40 75 Do there appear to be any influential observations in these data? Explain. Because the leverage value for ---Select--- is greater than (d) Compute Cook's distance measure for these data. (Round your answers to two decimal places.) X; Y; 24 22 17 26 28 26 36 40 40 75 we conclude that there -Select--- Cook's Distance Are any observations influential? Explain. (Select all that apply.) Observation x; = = 22 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation ¹X;: = 24 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation x; = 26 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation x = 28 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation x; = 40 is an influential observation since it has a large Cook's distance measure (greater than 1). None of the observations are influential since they do not have large Cook's distance measures (greater than 1).
Transcribed Image Text:(c) Compute the leverage values for these data. (Round your answers to two decimal places.) Leverage Value 22 24 26 36 17 28 40 26 40 75 Do there appear to be any influential observations in these data? Explain. Because the leverage value for ---Select--- is greater than (d) Compute Cook's distance measure for these data. (Round your answers to two decimal places.) X; Y; 24 22 17 26 28 26 36 40 40 75 we conclude that there -Select--- Cook's Distance Are any observations influential? Explain. (Select all that apply.) Observation x; = = 22 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation ¹X;: = 24 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation x; = 26 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation x = 28 is an influential observation since it has a large Cook's distance measure (greater than 1). Observation x; = 40 is an influential observation since it has a large Cook's distance measure (greater than 1). None of the observations are influential since they do not have large Cook's distance measures (greater than 1).
A statistical program is recommended. You may need to use the appropriate appendix table or technology to answer this question. Data for two variables, x and y, follow. X; 22 24 26 28 40 Y; 17 26 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ý = |–48.28 +3.11x (b) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) X; Yi 22 17 24 26 26 36 28 40 36 40 75 40 75 Studentized Deleted Residual -3.14 x At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation x; = 22 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x; = 24 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x; = 26 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; = 28 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x; = 40 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 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 to.025 or less than -to.025).
Transcribed Image Text:A statistical program is recommended. You may need to use the appropriate appendix table or technology to answer this question. Data for two variables, x and y, follow. X; 22 24 26 28 40 Y; 17 26 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ý = |–48.28 +3.11x (b) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) X; Yi 22 17 24 26 26 36 28 40 36 40 75 40 75 Studentized Deleted Residual -3.14 x At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation x; = 22 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x; = 24 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x; = 26 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; = 28 can be classified as an outlier since it has a large studentized deleted residual (greater than to.025 or less than -to.025). Observation x; = 40 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 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 to.025 or less than -to.025).
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