Question

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

Date	Price ($)
Nov. 3	83.71
Nov. 4	83.87
Nov. 7	83.40
Nov. 8	83.86
Nov. 9	83.24
Nov. 10	82.90
Nov. 11	84.66
Nov. 14	84.35
Nov. 15	85.74
Nov. 16	86.62
Nov. 17	86.74
Nov. 18	87.93
Nov. 21	87.98
Nov. 22	87.60
Nov. 23	88.22
Nov. 25	88.39
Nov. 28	88.94
Nov. 29	89.72
Nov. 30	89.77
Dec. 1	89.22

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)?

With critical values for the Durbin-Watson test for autocorrelation  dL = 1.2 and du= 1.41, what is your conclusion?

- Select your answer -Cannot conclude a significant autocorrelation.There is a significant negative autocorrelation.There is a significant positive autocorrelation.Item 4

Answer 1

using minitab>Stat>Regression

we have

Regression Analysis: Price ($) versus Date

Analysis of Variance

Source DF Adj SS Adj MS F-Value P-Value
Regression 1 100.011 100.011 191.28 0.000
Error 18 9.411 0.523
Total 19 109.422

Model Summary

S R-sq R-sq(adj) R-sq(pred)
0.723077 91.40% 90.92% 89.25%

Coefficients

Term Coef SE Coef T-Value P-Value VIF
Constant 82.271 0.336 244.93 0.000
Date 0.3878 0.0280 13.83 0.000 1.00

Regression Equation

Price ($) = 82.271 + 0.3878 Date

Durbin-Watson Statistic

Durbin-Watson Statistic = 0.918993

a)t he estimated regression equation that can be used to predict the closing price given the value of Period (to 3 decimals).

Price=82.271+0.388*Period

b. the value of the Durbin-Watson statistic =0.919

With critical values for the Durbin-Watson test for autocorrelation  dL = 1.2 and du= 1.41, what is your conclusion?

-we Can conclude a significant autocorrelation.

There is a significant positive autocorrelation.