Question: First run a moving average model using the revenue data. a. State the moving average period you picked and briefly discuss whether you think the

First run a moving average model using the revenue data.

a. State the moving average period you picked and briefly discuss whether you think the model can do a good job forecasting and why.

Simple exponential smoothing

a. State the reason for your choice of the smoothing model and run the model. Make sure to store the residuals.

d. Show the resulting graph from the smoothing method. State the model you picked and the parameter values such as , , , if any.

c. Examine the residuals using time series plot and ACF graph. Do you observe any significant autocorrelation?

d. Briefly discuss whether you think the model can do a good job forecasting and why.

*Date / Revenue Chart*

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

1991-06-28 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

First run a moving average model using the revenue data. a. State

the moving average period you picked and briefly discuss whether you think

the model can do a good job forecasting and why. Simple exponential

smoothing a. State the reason for your choice of the smoothing model

Moving Average Plot for Revenue 35000 Variable Actual Fits 30000 -- Forecasts - 95.0% PI 25000 Moving Average Length 4 20000 Revenue Accuracy Measures MAPE G MAD 955 15000 MSD 2360987 10000 5000 0 1 11 22 33 44 55 66 77 88 99 110 Index Smoothing Plot for Revenue Single Exponential Method 35000 Variable Actual Fits 30000 Smoothing Constant 0.738697 a 25000 Accuracy Measures MAPE 4 20000 Revenue 701 MAD MSD 1749221 15000 10000 5000 0 1 11 22 33 44 55 66 77 88 99 110 Index Autocorrelation Function for Revenue (with 5% significance limits for the autocorrelations) 1.0 0.8 0.6 0.4 0.2 Autocorrelation 0.0 -0.2 -0.4- -0.6 -0.8 -1.0 2 4 6 09 8 10 12 14 16 18 20 22 24 26 28 Lag Time Series Plot of Revenue 35000 30000 osobe 25000 20000 Revenue 15000 10000 5000 0 1990-03-30 1995-06-30 1992-09-30 1998-03-31 2000-12-31 2003-09-30 2006-06-30 2009-03-31 2011-12-31 2014-09-30 2017-06-30 Date Moving Average Plot for Revenue 35000 Variable Actual Fits 30000 -- Forecasts - 95.0% PI 25000 Moving Average Length 4 20000 Revenue Accuracy Measures MAPE G MAD 955 15000 MSD 2360987 10000 5000 0 1 11 22 33 44 55 66 77 88 99 110 Index Smoothing Plot for Revenue Single Exponential Method 35000 Variable Actual Fits 30000 Smoothing Constant 0.738697 a 25000 Accuracy Measures MAPE 4 20000 Revenue 701 MAD MSD 1749221 15000 10000 5000 0 1 11 22 33 44 55 66 77 88 99 110 Index Autocorrelation Function for Revenue (with 5% significance limits for the autocorrelations) 1.0 0.8 0.6 0.4 0.2 Autocorrelation 0.0 -0.2 -0.4- -0.6 -0.8 -1.0 2 4 6 09 8 10 12 14 16 18 20 22 24 26 28 Lag Time Series Plot of Revenue 35000 30000 osobe 25000 20000 Revenue 15000 10000 5000 0 1990-03-30 1995-06-30 1992-09-30 1998-03-31 2000-12-31 2003-09-30 2006-06-30 2009-03-31 2011-12-31 2014-09-30 2017-06-30 Date

Step by Step Solution

There are 3 Steps involved in it

1 Expert Approved Answer

Step: 1 Unlock blur-text-image

Question Has Been Solved by an Expert!

Get step-by-step solutions from verified subject matter experts

Step: 2 Unlock

Step: 3 Unlock

Students Have Also Explored These Related Accounting Questions!

First run a moving average model using the revenue data. a.State the moving average period you picked and briefly discuss whether you think the model can do a good job forecasting and why. Simple...

You will use the major forecasting techniques learned in this course to build the forecasting models for your revenue data. Please answer the questions below based on the picture I attached and the...

topic needed abstsrct , purpose of report, problem, methodology, analysis, result, conclusion everthing is clear question in managment report which i have to cover The Case Description Case studies...

SYSOIL Inc. is a producer of high-quality palm kernel oil. The company buys raw palm kennel seeds directly from large agricultural companies and refines the seeds into palm kennel oil that it sells...

Business Decision Analysis Description Case studies are used to enable you to apply new concepts, use the tools you have mastered, and improve your technical skills you have attained. Through the...

QUESTION 1 Although recent events in Hong Kong have created uncertainty, often such things can also provide opportunities for investors. As a first step, an investor considering the Hong Kong share...

i need answers for this homework. if possible with all the steps included Leadstar College of Management and Leadership Faculty of Business and Leadership FINANCIAL MANAGEMENT Assignment, Version-2...

please find the attachments and provide your solutions as appropriate soon. Leadstar College of Management and Leadership Faculty of Business and Leadership FINANCIAL MANAGEMENT Assignment, Version-2...

ECONOMICS QUESTION: please help me apply Marginal cost & revenue to this problem. specifically: Is Grace using marginal cost and marginal revenue correctly in her analysis? Explain the errors in her...

Term Project Operations Management Description You will be required to submit a written report to management and to include the spreadsheet models you used to generate price forecasts, optimize the...

Kelvins Commemoratives makes and sells two types of decorative plates. One plate displays a hand-painted image of Princess Diana; the other plate displays a machine-pressed image of Marilyn Monroe....

Repeat (a) and (b) of Problem 11.16, assuming that only 50 decision makers involved in Big Data purchases for each company size were surveyed. Discuss the implications of sample size on the x2 test...

Bashira is a cash - basis calendar - year taxpayer. During the last week of December she recelved a letter containing a $ 5 , 0 0 0 check for services rendered. Which of the following is a true...

5. Develop a scenario comparing two PH programs and involving the use of a CBA.

1. We include here a pictorial representation of this chapter in the form of a mind map ( Figure 1.1 ). Use the key words we have included to refresh your memory of what we have covered in this...

1. In what ways is information technology being used by HR professionals?

2. In what ways is information technology being used by line managers?