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(b) At the 0.05 level of significance, test for any positive autocorrelation in the residuals of the regression model. State the null and alternative hypotheses. Ho p=0 H₂: p > 0 O Ho: p<0 O Ho:p>O O Hop=0 H₁₂: p<0 Find the value of the test statistic. (Round your answer to two decimal places.) 18.00 What are the critical values? (Round your answers to two decimal places.) du = 1.20 = 1.41 State your conclusion. Do not reject Ho. We conclude that there is no evidence of positive autocorrelation. Do not reject Ho. We conclude that there is significant positive autocorrelation. Reject Ho. We conclude that there is no evidence of positive autocorrelation. Reject Ho. We conclude that there is significant positive autocorrelation. The test is inconclusive. The following data show the daily closing prices (in dollars per share) for a stock. Date Price ($) Nov. 3 82.85 Nov. 4 82.93 Nov. 7 83.70 Nov. 8 83.16 Nov. 9 82.90 Nov. 10 83.99 Nov. 11 84.59 Nov. 14 84.42 Nov. 15 85.55 Nov. 16 86.54 Nov. 17 86.84 Nov. 18 87.74 Nov. 21 87.39 Nov. 22 88.02 Nov. 23 88.79 Nov. 25 88.76 Nov. 28 89.04 Nov. 29 89.19 Nov. 30 88.91 Dec. 1 89.31 (a) Define the independent variable Period, where Period = 1 corresponds to the data for November 3, Period = 2 corresponds to the data for November 4, Period = 3 corresponds to the data for November 7, and so on. Develop the estimated regression equation that can be used to predict the closing price (in dollars per share) given the value of Period. Use x for Period. (Round your numerical values to two decimal places.) ŷ 81.99 0.40x

(b) At the 0.05 level of significance, test for any positive autocorrelation in the residuals of the regression model. State the null and alternative hypotheses. Ho p=0 H₂: p > 0 O Ho: p<0 O Ho:p>O O Hop=0 H₁₂: p<0 Find the value of the test statistic. (Round your answer to two decimal places.) 18.00 What are the critical values? (Round your answers to two decimal places.) du = 1.20 = 1.41 State your conclusion. Do not reject Ho. We conclude that there is no evidence of positive autocorrelation. Do not reject Ho. We conclude that there is significant positive autocorrelation. Reject Ho. We conclude that there is no evidence of positive autocorrelation. Reject Ho. We conclude that there is significant positive autocorrelation. The test is inconclusive. The following data show the daily closing prices (in dollars per share) for a stock. Date Price ($) Nov. 3 82.85 Nov. 4 82.93 Nov. 7 83.70 Nov. 8 83.16 Nov. 9 82.90 Nov. 10 83.99 Nov. 11 84.59 Nov. 14 84.42 Nov. 15 85.55 Nov. 16 86.54 Nov. 17 86.84 Nov. 18 87.74 Nov. 21 87.39 Nov. 22 88.02 Nov. 23 88.79 Nov. 25 88.76 Nov. 28 89.04 Nov. 29 89.19 Nov. 30 88.91 Dec. 1 89.31 (a) Define the independent variable Period, where Period = 1 corresponds to the data for November 3, Period = 2 corresponds to the data for November 4, Period = 3 corresponds to the data for November 7, and so on. Develop the estimated regression equation that can be used to predict the closing price (in dollars per share) given the value of Period. Use x for Period. (Round your numerical values to two decimal places.) ŷ 81.99 0.40x

Intermediate Algebra
Intermediate Algebra
19th Edition
ISBN:9780998625720
Author:Lynn Marecek
Publisher:Lynn Marecek
Chapter12: Sequences, Series And Binomial Theorem
Section12.3: Geometric Sequences And Series
Problem 12.58TI: What is the total effect on the economy of a government tax rebate of $500 to each household in...
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(b) At the 0.05 level of significance, test for any positive autocorrelation in the residuals of the regression model. State the null and alternative hypotheses. Ho p=0 H₂: p > 0 O Ho: p<0 O Ho:p>O O Hop=0 H₁₂: p<0 Find the value of the test statistic. (Round your answer to two decimal places.) 18.00 What are the critical values? (Round your answers to two decimal places.) du = 1.20 = 1.41 State your conclusion. Do not reject Ho. We conclude that there is no evidence of positive autocorrelation. Do not reject Ho. We conclude that there is significant positive autocorrelation. Reject Ho. We conclude that there is no evidence of positive autocorrelation. Reject Ho. We conclude that there is significant positive autocorrelation. The test is inconclusive.
Transcribed Image Text:(b) At the 0.05 level of significance, test for any positive autocorrelation in the residuals of the regression model. State the null and alternative hypotheses. Ho p=0 H₂: p > 0 O Ho: p<0 O Ho:p>O O Hop=0 H₁₂: p<0 Find the value of the test statistic. (Round your answer to two decimal places.) 18.00 What are the critical values? (Round your answers to two decimal places.) du = 1.20 = 1.41 State your conclusion. Do not reject Ho. We conclude that there is no evidence of positive autocorrelation. Do not reject Ho. We conclude that there is significant positive autocorrelation. Reject Ho. We conclude that there is no evidence of positive autocorrelation. Reject Ho. We conclude that there is significant positive autocorrelation. The test is inconclusive.
The following data show the daily closing prices (in dollars per share) for a stock. Date Price ($) Nov. 3 82.85 Nov. 4 82.93 Nov. 7 83.70 Nov. 8 83.16 Nov. 9 82.90 Nov. 10 83.99 Nov. 11 84.59 Nov. 14 84.42 Nov. 15 85.55 Nov. 16 86.54 Nov. 17 86.84 Nov. 18 87.74 Nov. 21 87.39 Nov. 22 88.02 Nov. 23 88.79 Nov. 25 88.76 Nov. 28 89.04 Nov. 29 89.19 Nov. 30 88.91 Dec. 1 89.31 (a) Define the independent variable Period, where Period = 1 corresponds to the data for November 3, Period = 2 corresponds to the data for November 4, Period = 3 corresponds to the data for November 7, and so on. Develop the estimated regression equation that can be used to predict the closing price (in dollars per share) given the value of Period. Use x for Period. (Round your numerical values to two decimal places.) ŷ 81.99 0.40x
Transcribed Image Text:The following data show the daily closing prices (in dollars per share) for a stock. Date Price ($) Nov. 3 82.85 Nov. 4 82.93 Nov. 7 83.70 Nov. 8 83.16 Nov. 9 82.90 Nov. 10 83.99 Nov. 11 84.59 Nov. 14 84.42 Nov. 15 85.55 Nov. 16 86.54 Nov. 17 86.84 Nov. 18 87.74 Nov. 21 87.39 Nov. 22 88.02 Nov. 23 88.79 Nov. 25 88.76 Nov. 28 89.04 Nov. 29 89.19 Nov. 30 88.91 Dec. 1 89.31 (a) Define the independent variable Period, where Period = 1 corresponds to the data for November 3, Period = 2 corresponds to the data for November 4, Period = 3 corresponds to the data for November 7, and so on. Develop the estimated regression equation that can be used to predict the closing price (in dollars per share) given the value of Period. Use x for Period. (Round your numerical values to two decimal places.) ŷ 81.99 0.40x
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