Question

The following data show the daily closing prices (in dollars per share) for a stock.

Date	Price ($)
Nov. 3	82.74
Nov. 4	82.18
Nov. 7	83.71
Nov. 8	83.88
Nov. 9	83.50
Nov. 10	82.48
Nov. 11	84.26
Nov. 14	84.76
Nov. 15	85.09
Nov. 16	86.56
Nov. 17	86.27
Nov. 18	87.47
Nov. 21	87.60
Nov. 22	87.57
Nov. 23	88.68
Nov. 25	88.77
Nov. 28	89.74
Nov. 29	89.89
Nov. 30	89.09
Dec. 1	88.34

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 , and so on. Develop the estimated regression equation that can be used to predict the closing price given the value of Period (to 3 decimals).

Price = (___) + (___) period

b. At the .05 level of significance, test for any positive autocorrelation in the data.

What is the value of the Durbin-Watson statistic (to 3 decimals)?

Answer 1

(a) Price = 81.781 + 0.414*Period

(b) The Durbin-Watson statistic = 1.172

	r²	0.911
	r	0.954
	Std. Error	0.789
	n	20
	k	1
	Dep. Var.	Price ($)
ANOVA table
Source	SS	df	MS	F	p-value
Regression	114.0280	1	114.0280	183.22	7.09E-11
Residual	11.2024	18	0.6224
Total	125.2304	19
Regression output				confidence interval
variables	coefficients	std. error	t (df=18)	p-value	95% lower	95% upper
Intercept	81.781
Period	0.414	0.0306	13.536	7.09E-11	0.3498	0.4784

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