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 0.05 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 dy = 1.2 and du = 1.41, what is your conclusion? There is a significant positive autocorrelation.The following data show the daily closing prices (in dollars per share) for a stock. Date Price ($) Nov. 3 83.89 Nov. 4 82.17 Nov. 7 82.73 Nov. 8 82.31 Nov. 9 82.09 Nov. 10 83.33 Nov. 11 84.52 Nov. 14 84.62 Nov. 15 85.05 Nov. 16 86.76 Nov. 17 86.69 Nov. 18 87.71 Nov. 21 87.95 Nov. 22 87.15 Nov. 23 88.62 Nov. 25 88.05 Nov. 28 89.23 Nov. 29 88.73 Nov. 30 88.82 Dec. 1 89.84 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)