A statistical program is recommended. You may need to use the appropriate appendix table or technology...

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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. x₁ Yi Y₁ 6 10 -2 1 2 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ŷ = (b) Plot the standardized residuals versus ý. 1 xi Yi 2 3 0 2 4 6 8 10 12 14 16 18 20 ÿ 4 5 3 Do there appear to be any outliers in these data? Explain. The value of the standardized residual for all observations 6 4 10 8 5 8 14 17 14 (c) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) 17 Studentized Standardized Residual Deleted Residual Ⓡ. 1 -21 0 2 4 6 8 10 12 14 16 18 20 ý Ⓡ -2 0 2 4 6 8 10 12 14 16 18 201 ŷ O Ⓡ✓ xis either greater than +2 or less than -2. Therefore, there --Select-- 2 4 6 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 to.025 or less than -to.025). 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 to.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 to.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 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 8 10 12 14 16 18 20 ŷ Ⓡ Chapter 19.0 F... Get Homework... 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. x₁ Yi Y₁ 6 10 -2 1 2 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ŷ = (b) Plot the standardized residuals versus ý. 1 xi Yi 2 3 0 2 4 6 8 10 12 14 16 18 20 ÿ 4 5 3 Do there appear to be any outliers in these data? Explain. The value of the standardized residual for all observations 6 4 10 8 5 8 14 17 14 (c) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) 17 Studentized Standardized Residual Deleted Residual Ⓡ. 1 -21 0 2 4 6 8 10 12 14 16 18 20 ý Ⓡ -2 0 2 4 6 8 10 12 14 16 18 201 ŷ O Ⓡ✓ xis either greater than +2 or less than -2. Therefore, there --Select-- 2 4 6 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 to.025 or less than -to.025). 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 to.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 to.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 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 8 10 12 14 16 18 20 ŷ Ⓡ Chapter 19.0 F... Get Homework...

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