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. xi 1 2 3 4 5 yi 7 11 9 15 18 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ŷ = (b) Plot the standardized residuals versus ŷ. A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, 0.2), (9.4, 0.9), (12.0, −1.6), (14.6, 0.2), (17.2, 0.6). A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, −1.6), (9.4, 0.2), (12.0, 0.2), (14.6, 0.6), (17.2, 0.9). A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, 0.3), (9.4, −0.8), (12.0, 0.5), (14.6, −0.8), (17.2, −0.0). A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, 0.9), (9.4, 0.6), (12.0, 0.2), (14.6, 0.2), (17.2, −1.6). Do there appear to be any outliers in these data? Explain. The value of the standardized residual for is either greater than +2 or less than −2. Therefore, there . (c) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) xi yi Studentized Deleted Residual 1 7 2 11 3 9 4 15 5 18 At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation xi = 1 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 2 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 3 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 4 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 5 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).None of the observations can be classified as outliers since they do not have large studentized deleted residuals (greater than t0.025 or less than −t0.025).

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. xi 1 2 3 4 5 yi 7 11 9 15 18 (a) Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.) ŷ = (b) Plot the standardized residuals versus ŷ. A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, 0.2), (9.4, 0.9), (12.0, −1.6), (14.6, 0.2), (17.2, 0.6). A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, −1.6), (9.4, 0.2), (12.0, 0.2), (14.6, 0.6), (17.2, 0.9). A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, 0.3), (9.4, −0.8), (12.0, 0.5), (14.6, −0.8), (17.2, −0.0). A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations: (6.8, 0.9), (9.4, 0.6), (12.0, 0.2), (14.6, 0.2), (17.2, −1.6). Do there appear to be any outliers in these data? Explain. The value of the standardized residual for is either greater than +2 or less than −2. Therefore, there . (c) Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.) xi yi Studentized Deleted Residual 1 7 2 11 3 9 4 15 5 18 At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.) Observation xi = 1 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 2 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 3 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 4 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).Observation xi = 5 can be classified as an outlier since it has a large studentized deleted residual (greater than t0.025 or less than −t0.025).None of the observations can be classified as outliers since they do not have large studentized deleted residuals (greater than t0.025 or less than −t0.025).

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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_i	1	2	3	4	5
y_i	7	11	9	15	18

(a)

Develop the estimated regression equation for these data. (Round your numerical values to two decimal places.)

ŷ =

(b)

Plot the standardized residuals versus ŷ.

A standardized residual plot is labeled y hat on the horizontal axis and ranges from 0 to 20. The vertical axis is labeled Standardized Residual and ranges from −2 to 2. There is a horizontal dashed line that goes through 0 on the vertical axis. 5 points appear on the plot at the following approximate locations:

(6.8, 0.2),
(9.4, 0.9),
(12.0, −1.6),
(14.6, 0.2),
(17.2, 0.6).

(6.8, −1.6),
(9.4, 0.2),
(12.0, 0.2),
(14.6, 0.6),
(17.2, 0.9).

(6.8, 0.3),
(9.4, −0.8),
(12.0, 0.5),
(14.6, −0.8),
(17.2, −0.0).

(6.8, 0.9),
(9.4, 0.6),
(12.0, 0.2),
(14.6, 0.2),
(17.2, −1.6).

Do there appear to be any outliers in these data? Explain.

The value of the standardized residual for is either greater than +2 or less than −2. Therefore, there .

(c)

Compute the studentized deleted residuals for these data. (Round your answers to two decimal places.)

x_i	y_i	Studentized Deleted Residual
1	7
2	11
3	9
4	15
5	18

At the 0.05 level of significance, can any of these observations be classified as an outlier? Explain. (Select all that apply.)

Observation x_i = 1 can be classified as an outlier since it has a large studentized deleted residual (greater than t_0.025 or less than −t_0.025).Observation x_i = 2 can be classified as an outlier since it has a large studentized deleted residual (greater than t_0.025 or less than −t_0.025).Observation x_i = 3 can be classified as an outlier since it has a large studentized deleted residual (greater than t_0.025 or less than −t_0.025).Observation x_i = 4 can be classified as an outlier since it has a large studentized deleted residual (greater than t_0.025 or less than −t_0.025).Observation x_i = 5 can be classified as an outlier since it has a large studentized deleted residual (greater than t_0.025 or less than −t_0.025).None of the observations can be classified as outliers since they do not have large studentized deleted residuals (greater than t_0.025 or less than −t_0.025).

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