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This exercise requires the use of a statistical software package. An article included the accompanying data on 17 fish caught in 2 consecutive years. Fish Year Number Weight Length Age (g) (mm) (years) Year 1 1 777 410 9 2 581 368 11 3 540 357 15 4 649 373 12 5 537 361 9 6 892 385 9 7 674 380 10 8 782 400 12 Year 2 9 572 407 12 10 628 410 13 11 726 421 12 12 866 446 19 13 1,041 478 19 14 803 441 18 15 833 454 12 16 765 440 12 17 726 427 12 (a) Fit a multiple regression model to describe the relationship between weight and the predictors length and age. (Use x₁ for length and x2 for age. Round your numerical values to two decimal places.) (b) Carry out the model utility test to determine whether at least one of the predictors length and age are useful for predicting weight. Use a significance level of 0.05. Calculate the test statistic. (Round your answer to two decimal places.) F = Use technology to calculate the P-value. (Round your answer to four decimal places.) P-value = What can you conclude? Fail to reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Fail to reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight.

This exercise requires the use of a statistical software package. An article included the accompanying data on 17 fish caught in 2 consecutive years. Fish Year Number Weight Length Age (g) (mm) (years) Year 1 1 777 410 9 2 581 368 11 3 540 357 15 4 649 373 12 5 537 361 9 6 892 385 9 7 674 380 10 8 782 400 12 Year 2 9 572 407 12 10 628 410 13 11 726 421 12 12 866 446 19 13 1,041 478 19 14 803 441 18 15 833 454 12 16 765 440 12 17 726 427 12 (a) Fit a multiple regression model to describe the relationship between weight and the predictors length and age. (Use x₁ for length and x2 for age. Round your numerical values to two decimal places.) (b) Carry out the model utility test to determine whether at least one of the predictors length and age are useful for predicting weight. Use a significance level of 0.05. Calculate the test statistic. (Round your answer to two decimal places.) F = Use technology to calculate the P-value. (Round your answer to four decimal places.) P-value = What can you conclude? Fail to reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Fail to reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight.

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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This exercise requires the use of a statistical software package. An article included the accompanying data on 17 fish caught in 2 consecutive years. Fish Year Number Weight Length Age (g) (mm) (years) Year 1 1 777 410 9 2 581 368 11 3 540 357 15 4 649 373 12 5 537 361 9 6 892 385 9 7 674 380 10 8 782 400 12 Year 2 9 572 407 12 10 628 410 13 11 726 421 12 12 866 446 19 13 1,041 478 19 14 803 441 18 15 833 454 12 16 765 440 12 17 726 427 12 (a) Fit a multiple regression model to describe the relationship between weight and the predictors length and age. (Use x₁ for length and x2 for age. Round your numerical values to two decimal places.) (b) Carry out the model utility test to determine whether at least one of the predictors length and age are useful for predicting weight. Use a significance level of 0.05. Calculate the test statistic. (Round your answer to two decimal places.) F = Use technology to calculate the P-value. (Round your answer to four decimal places.) P-value = What can you conclude? Fail to reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Fail to reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight.
Transcribed Image Text:This exercise requires the use of a statistical software package. An article included the accompanying data on 17 fish caught in 2 consecutive years. Fish Year Number Weight Length Age (g) (mm) (years) Year 1 1 777 410 9 2 581 368 11 3 540 357 15 4 649 373 12 5 537 361 9 6 892 385 9 7 674 380 10 8 782 400 12 Year 2 9 572 407 12 10 628 410 13 11 726 421 12 12 866 446 19 13 1,041 478 19 14 803 441 18 15 833 454 12 16 765 440 12 17 726 427 12 (a) Fit a multiple regression model to describe the relationship between weight and the predictors length and age. (Use x₁ for length and x2 for age. Round your numerical values to two decimal places.) (b) Carry out the model utility test to determine whether at least one of the predictors length and age are useful for predicting weight. Use a significance level of 0.05. Calculate the test statistic. (Round your answer to two decimal places.) F = Use technology to calculate the P-value. (Round your answer to four decimal places.) P-value = What can you conclude? Fail to reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Reject Ho. We do not have convincing evidence that at least one of the predictors length and age are useful for predicting weight. Fail to reject Ho. We have convincing evidence that at least one of the predictors length and age are useful for predicting weight.
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