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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 8.2 7.9 9.2 9.4 9.9 7.6 Weight (kg) 154 172 219 201 242 157 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =D+Dx. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2 cm is (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OC. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.

Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 8.2 7.9 9.2 9.4 9.9 7.6 Weight (kg) 154 172 219 201 242 157 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =D+Dx. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2 cm is (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OC. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.

MATLAB: An Introduction with Applications
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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Find the regression​ equation, letting overhead width be the predictor​ (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2cm. Can the prediction be​ correct? What is wrong with predicting the weight in this​ case? Use a significance level of 0.05.

**Regression Analysis for Predicting Seal Weight from Overhead Width** To find the regression equation that predicts the weight of a seal using the overhead width as the predictor (x variable), and evaluate whether a prediction can be made when the overhead width is 2 cm, the following data is considered: | Overhead Width (cm) | Weight (kg) | |---------------------|-------------| | 8.2 | 154 | | 7.9 | 172 | | 9.2 | 219 | | 9.4 | 201 | | 9.9 | 242 | | 7.6 | 157 | **Instructions**: 1. **Regression Equation**: - The regression equation is given by \(\hat{y} = b_0 + b_1x\). - Calculate and input the required values for \(b_0\) (y-intercept) and \(b_1\) (slope). 2. **Prediction for x = 2 cm**: - Use the regression equation to predict the weight for an overhead width of 2 cm. - Round the prediction to one decimal place as needed. 3. **Validity of the Prediction**: - Assess whether the prediction can be correct using the given options. - Understand the implications of making predictions outside the data’s scope or with insufficient evidence for a linear relationship. **Options for Consideration**: - **A**: The prediction may not be correct because a weight does not make sense, and the width is outside the sample data scope. - **B**: The prediction may not be correct due to insufficient linear correlation evidence and width being outside the sample data scope. - **C**: The prediction may not be correct as a weight below zero does not make sense and there is a lack of linear correlation evidence. - **D**: The prediction might be correct with no issues in predicting weight in this case. **Additional Information**: - Click on the provided icon to view critical values of the Pearson correlation coefficient \(r\). Understanding the relationship and constraints in this model helps in evaluating the appropriateness of using regression predictions for various overhead widths, particularly those not represented in the sample data.
Transcribed Image Text:**Regression Analysis for Predicting Seal Weight from Overhead Width** To find the regression equation that predicts the weight of a seal using the overhead width as the predictor (x variable), and evaluate whether a prediction can be made when the overhead width is 2 cm, the following data is considered: | Overhead Width (cm) | Weight (kg) | |---------------------|-------------| | 8.2 | 154 | | 7.9 | 172 | | 9.2 | 219 | | 9.4 | 201 | | 9.9 | 242 | | 7.6 | 157 | **Instructions**: 1. **Regression Equation**: - The regression equation is given by \(\hat{y} = b_0 + b_1x\). - Calculate and input the required values for \(b_0\) (y-intercept) and \(b_1\) (slope). 2. **Prediction for x = 2 cm**: - Use the regression equation to predict the weight for an overhead width of 2 cm. - Round the prediction to one decimal place as needed. 3. **Validity of the Prediction**: - Assess whether the prediction can be correct using the given options. - Understand the implications of making predictions outside the data’s scope or with insufficient evidence for a linear relationship. **Options for Consideration**: - **A**: The prediction may not be correct because a weight does not make sense, and the width is outside the sample data scope. - **B**: The prediction may not be correct due to insufficient linear correlation evidence and width being outside the sample data scope. - **C**: The prediction may not be correct as a weight below zero does not make sense and there is a lack of linear correlation evidence. - **D**: The prediction might be correct with no issues in predicting weight in this case. **Additional Information**: - Click on the provided icon to view critical values of the Pearson correlation coefficient \(r\). Understanding the relationship and constraints in this model helps in evaluating the appropriateness of using regression predictions for various overhead widths, particularly those not represented in the sample data.
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