Create an Excel file containing the prices for Google stock for 07/01/2019 - 11/25/2019 (inclusively). Use Yahoo Finance or similar websites to obtain historical data. Ignore dividends. a) Assuming a linear relationship between the closing price and the business day (07/01/2019 - business day 1, 07/02/2019 - business day 2, etc), use the least-squares method to compute the regression coefficients. You can use Data Analysis Toolpack for this. State the obtained model with the found estimated parameters. b) State the extrapolated price for the Google stock for 11/26/2019 (since the date is out of the range, this predicted value for the stock for 11/26/2019 will be an extrapolation). 0) Perform the residual analysis. Based on the results, evaluate whether the assumptions of the linear regression are seriously violated. d) Find the Durbin-Watson statistic. Do this with Excel but don't use Data Analysis or any additional Excel Toolpacks for this. At level of significance or = 0.01, is there evidence of positive autocorrelation among the errors? Be careful with the formulation of your hypothesis here. e) Based on your observations in (c)-(d), is there a reason to question the validity of the linear model? Explain your solutions in complete sentences