100, 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 1 Period. (Round your numerical values to two decimal places.) ŷ = 82.89 +0.38x (b) At the 0.05 level of significance, test for any positive autocorrelation in the data. State the null and alternative hypotheses. ⒸH₁: p = 0 Ha: p > 0 Ho:p> 0 H₂: p=0 Ho: P<0 H₂: p=0 Ho: P = 0 Ha: p<0 Find the value of the test statistic. (Round your answer to two decimal places.) 0.09 X What are the critical values? (Round your answers to two decimal places.) d₁ -0.07 du = 0.25 X X State your conclusion. O Do not reject Ho. We conclude that there is significant positive autocorrelation. Reject Ho. We conclude that there is significant positive autocorrelation. O Reject Ho. We conclude that there is no evidence of positive autocorrelation.

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
Section: Chapter Questions
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(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 = 82.89 +0.38x
(b) At the 0.05 level of significance, test for any positive autocorrelation in the data.
State the null and alternative hypotheses.
ⒸH₁: p = 0
Ha: p > 0
Ho:p>0
Ha: p = 0
O Ho: P < 0
Ha: p = 0
Ho: P = 0
Ha: p < 0
Find the value of the test statistic. (Round your answer to two decimal places.)
0.09
X
X
What are the critical values? (Round your answers to two decimal places.)
dL
du
= -0.07
0.25
=
X
X
State your conclusion.
O Do not reject Ho. We conclude that there is significant positive autocorrelation.
Reject Ho. We conclude that there is significant positive autocorrelation.
O Reject Ho. We conclude that there is no evidence of positive autocorrelation.
Transcribed Image Text:(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 = 82.89 +0.38x (b) At the 0.05 level of significance, test for any positive autocorrelation in the data. State the null and alternative hypotheses. ⒸH₁: p = 0 Ha: p > 0 Ho:p>0 Ha: p = 0 O Ho: P < 0 Ha: p = 0 Ho: P = 0 Ha: p < 0 Find the value of the test statistic. (Round your answer to two decimal places.) 0.09 X X What are the critical values? (Round your answers to two decimal places.) dL du = -0.07 0.25 = X X State your conclusion. O Do not reject Ho. We conclude that there is significant positive autocorrelation. Reject Ho. We conclude that there is significant positive autocorrelation. O Reject Ho. We conclude that there is no evidence of positive autocorrelation.
Date
Nov. 3
Nov. 4
Nov. 7
Nov. 8
Nov. 9
Nov. 10
Nov. 11
Nov. 14
Nov. 15
Nov. 16
Nov. 17
Nov. 18
Nov. 21
Nov. 22
Nov. 23
Nov. 25
Nov. 28
Nov. 29
Nov. 30
Dec. 1
Price ($)
82.97
83.02
83.58
83.15
82.83
84.05
84.64
84.36
85.48
86.53
86.90
87.68
87.31
87.99
88.86
88.72
89.11
89.11
88.98
89.21
Transcribed Image Text:Date Nov. 3 Nov. 4 Nov. 7 Nov. 8 Nov. 9 Nov. 10 Nov. 11 Nov. 14 Nov. 15 Nov. 16 Nov. 17 Nov. 18 Nov. 21 Nov. 22 Nov. 23 Nov. 25 Nov. 28 Nov. 29 Nov. 30 Dec. 1 Price ($) 82.97 83.02 83.58 83.15 82.83 84.05 84.64 84.36 85.48 86.53 86.90 87.68 87.31 87.99 88.86 88.72 89.11 89.11 88.98 89.21
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