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Data for two variables, and y, follow. 3₁ 12 21 a. Develop the estimated regression equation for these data (to 2 decimals). Enter negative value as negative number. ŷ y = * b. Compute the standardized residuals for these data (to 2 decimals). Do not round your intermediate calculations. Enter negative value as negative number. 2₂ No 22 24 26 28 40 Yi 12 21 31 35 70 x Standardized Residual Can any of these observations be classified as an outlier? Explain. S * Xi 22 24 26 28 40 35 70 31 35

Data for two variables, and y, follow. 3₁ 12 21 a. Develop the estimated regression equation for these data (to 2 decimals). Enter negative value as negative number. ŷ y = * b. Compute the standardized residuals for these data (to 2 decimals). Do not round your intermediate calculations. Enter negative value as negative number. 2₂ No 22 24 26 28 40 Yi 12 21 31 35 70 x Standardized Residual Can any of these observations be classified as an outlier? Explain. S * Xi 22 24 26 28 40 35 70 31 35

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 6PPS
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Data for two variables, and y, follow. xi No a. Develop the estimated regression equation for these data (to 2 decimals). Enter negative value as negative number. ŷ b. Compute the standardized residuals for these data (to 2 decimals). Do not round your intermediate calculations. Enter negative value as negative number. 22 24 26 28 40 Yi 12 21 31 35 70 x Standardized Residual Can any of these observations be classified as an outlier? Explain. Xi 22 24 26 28 40 Because none c. Develop a standardized residual plot for . Yi 12 21 31 35 70 of the standardized residuals are less than -2 or greater than 2, none of the observations can be classified as an outlier.
Transcribed Image Text:Data for two variables, and y, follow. xi No a. Develop the estimated regression equation for these data (to 2 decimals). Enter negative value as negative number. ŷ b. Compute the standardized residuals for these data (to 2 decimals). Do not round your intermediate calculations. Enter negative value as negative number. 22 24 26 28 40 Yi 12 21 31 35 70 x Standardized Residual Can any of these observations be classified as an outlier? Explain. Xi 22 24 26 28 40 Because none c. Develop a standardized residual plot for . Yi 12 21 31 35 70 of the standardized residuals are less than -2 or greater than 2, none of the observations can be classified as an outlier.
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