You will make a Revenue analysis for Company "Verizon".

Please answer the questions below with detailed explanations. Also, please add a detailed explanation to the 3 pictures I attached.

1.Introduction -Briefly describe the business in general, such the year the company was established, the products/services the company provides, and the location of the headquarter, etc. Then discuss what you are going to do ,and why. For example, the objective is to develop the best two-year quarterly forecast for ____ company revenues. This is your description of the purpose and use of the forecast and plan. You may add a statement of the value of the objective to the company and its leadership.

2.Data Description--Describe your data; show time series plots and ACF from Minitab; identify the T, C and S components in the time series plot if any. Does the ACF graph confirm what you observe in the time series plot? Explain what observation in ACF lead you to confirm T, C or S

* Date/ Revenue

1990-03-30 3019.0

1990-06-29 3076.0

1990-09-28 3099.0

1990-12-31 3105.0

1991-03-29 2996.0

991-06-28 3081.0

1991-09-30 3104.0

1991-12-31 3099.0

1992-03-31 3019.0

1992-06-30 3149.0

1992-09-30 3167.0

1992-12-31 3254.0

1993-03-31 3163.0

1993-06-30 3220.0

1993-09-30 3290.0

1993-12-31 3317.0

1994-03-31 3373.0

1994-06-30 3394.0

1994-09-30 3415.0

1994-12-30 3609.0

1995-03-31 3450.0

1995-06-30 3564.0

1995-09-29 3261.0

1995-12-29 3265.0

1996-03-31 3208.0

1996-06-30 3224.0

1996-09-30 7377.0

1996-12-31 7405.0

1997-03-31 7416.0

1997-06-30 7708.0

1997-09-30 7374.0

1997-12-31 7696.0

1998-03-31 7651.0

1998-06-30 7928.0

1998-09-30 7910.0

1998-12-31 8077.0

1999-03-31 7967.0

1999-06-30 14510.0

1999-09-30 14660.0

1999-12-31 15260.0

2000-03-31 14530.0

2000-06-30 16770.0

2000-09-30 16530.0

2000-12-31 16870.0

2001-03-31 16270.0

2001-06-30 16910.0

2001-09-30 17000.0

2001-12-31 17010.0

2002-03-31 16430.0

2002-06-30 16750.0

2002-09-30 17110.0

2002-12-31 16910.0

2003-03-31 16490.0

2003-06-30 16830.0

2003-09-30 17060.0

2003-12-31 17200.0

2004-03-31 17060.0

2004-06-30 17760.0

2004-09-30 18210.0

2004-12-31 12730.0

2005-03-31 18180.0

2005-06-30 18050.0

2005-09-30 18490.0

2005-12-31 15300.0

2006-03-31 21230.0

2006-06-30 21890.0

2006-09-30 22460.0

2006-12-31 22610.0

2007-03-31 22580.0

2007-06-30 23270.0

2007-09-30 23770.0

2007-12-31 23840.0

2008-03-31 23830.0

2008-06-30 24120.0

2008-09-30 24750.0

2008-12-31 24640.0

2009-03-31 26590.0

2009-06-30 26860.0

2009-09-30 27260.0

2009-12-31 27090.0

2010-03-31 26910.0

2010-06-30 26770.0

2010-09-30 26480.0

2010-12-31 26400.0

2011-03-31 26990.0

2011-06-30 27540.0

2011-09-30 27910.0

2011-12-31 28440.0

2012-03-31 28240.0

2012-06-30 28550.0

2012-09-30 29010.0

2012-12-31 30040.0

2013-03-31 29420.0

2013-06-30 29790.0

2013-09-30 30280.0

2013-12-31 31060.0

2014-03-31 30820.0

2014-06-30 31480.0

2014-09-30 31590.0

2014-12-31 33190.0

2015-03-31 31980.0

2015-06-30 32220.0

2015-09-30 33160.0

2015-12-31 34250.0

2016-03-31 32170.0

2016-06-30 30530.0

2016-09-30 30940.0

2016-12-31 32340.0

2017-03-31 29810.0

2017-06-30 30550.0

2017-09-30 31720.0

Autocorrelation Function for Revenue (with 5% significance limits for the autocorrelations) 1.0 D.8 0.6 0.4 0.2 Autocorrelation 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 2 6 8 10 12 14 16 18 20 22 24 26 28 LagSummary Report for Revenue Anderson-Darling Normality Test A-Squared 3.64 P-Value