\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{ A1 } & & & & fx & Year \\ \hline & A & & B & C & & E \\ \hline 1 & Year & & evenue & & \multirow{2}{*}{\multicolumn{2}{|c|}{ This is fictitious data. }} \\ \hline 2 & 1990 & & 143.16 & & & \\ \hline 3 & 1991 & & 156.36 & & & \\ \hline 4 & 1992 & & 151.36 & & & \\ \hline 5 & 1993 & & 158.56 & & & \\ \hline 6 & 1994 & & 149.20 & & & \\ \hline 7 & 1995 & & 171.92 & & & \\ \hline 8 & 1996 & & 159.24 & & & \\ \hline 9 & 1997 & & 180.60 & & & \\ \hline 10 & 1998 & & 159.72 & & & \\ \hline 11 & 1999 & & 194.20 & & & \\ \hline 12 & 2000 & & 169.20 & & & \\ \hline 13 & 2001 & & 230.80 & & & \\ \hline 14 & 2002 & & 258.28 & & & \\ \hline 15 & 2003 & & 228.52 & & & \\ \hline 16 & 2004 & & 274.48 & & & \\ \hline 17 & 2005 & & 284.16 & & & \\ \hline 18 & 2006 & & 262.32 & & & \\ \hline 19 & 2007 & & 313.48 & & & \\ \hline 20 & 2008 & & 270.84 & & & \\ \hline 21 & 2009 & & 338.04 & & & \\ \hline 22 & 2010 & & 399.00 & & & \\ \hline 23 & 2011 & & 395.00 & & & \\ \hline 24 & 2012 & & 358.20 & & & \\ \hline 25 & 2013 & & 456.40 & & & \\ \hline 26 & 2014 & & 359.68 & & & \\ \hline 27 & 2015 & & 415.44 & & & \\ \hline 28 & 2016 & & 508.08 & & & \\ \hline \end{tabular} Chapter 12, Problem 22P (0) homework questions roblem 16 questions left - Renews Oct. 27, 2023 e purpose of this problem is to get you used to the concept of autocorrelation in a time series. You uld do this with any time series, but here you should use the series of convenience store revenues in e file P12_10.xIsx. a. First, do it the quick way. Use the Autocorrelation procedure in StatTools to get a list of autocorrelations and a corresponding correlogram of the revenues. You can choose the number of lags. b. Now do it the more time-consuming way. Create columns of lagged versions of Revenue -3 lags will suffice. Next, look at scatterplots of Revenue versus its first few lags. If the autocorrelations are large, you should see fairly tight scatters - that's what autocorrelation is all about. Also, generate a correlation matrix to see the correlations between Revenue and its first few lags. These should be approximately the same as the autocorrelations from part a. (Autocorrelations are calculated slightly differently than regular correlations, which accounts for any slight discrepancies you might notice-but these discrepancies should be minor.) c. Create the first differences of Revenue in a new column. (You can do this manually with formulas, or you can use StatTools's Difference procedure on the Data Utilities menu.) Now repeat parts a and b with the differences instead of the original closing prices - that is, examine the autocorrelations of the differences. They should be small, and the scatterplots of the differences versus lags of the differences should be shapeless swarms. This illustrates what happens when the differences of a time series variable have insignificant autocorrelations. d. Write a short report of your findings. My Textbook Solutions Business Analytics ep-by-step solution