predictive analytics techniques and graphs
The purpose of this assignment is to use predictive analytics
techniques and the graphs and charts associated with these techniques
to forecast outcomes and make business decisions.Using specified data files, chapter example files, and templates
from the “Topic 1 Student Data, Template, and Example
Files” topic material, complete Chapter 12 Problems 28, 30, 34,
46, 60, 62 (b through e), and 63 from the textbook. Use the Palisade
DecisionTools Excel software to complete these problems where
requested and applicable. Problem 60 should be completed using only Excel.To receive full credit on the assignment, complete the following.Ensure that the Palisade software output is included with your
submission.Ensure that Excel files include the associated
cell functions and/or formulas if functions and/or formulas are
used.Include a written response to all narrative questions
presented in the problem by placing it in the associated Excel
file.Place each problem in its own Excel file. Ensure that
your first and last name are in your Excel file names.APA style is not required, but solid academic writing is expected.This assignment uses a rubric. Please review the rubric prior to
beginning the assignment to become familiar with the expectations for
successful completion. 28.The
file P12_10.xlsx contains annual revenues for a convenience store. If
you want to forecast revenue for the next few years with the moving
averages method, what span should you use? Will any span work well?.
30.The
file P02_28.xlsx contains total monthly U.S. retail sales data. While
holding out the final six months of observations for validation
purposes, use the method of moving averages with one or more spans of
your choice to forecast U.S. retail sales for the next 12 months.
Comment on the performance of your model. What makes this time series
more challenging to forecast?
34.Consider
the American Express closing price data in the file
P12_16.xlsx.a.Create a time series chart of the data. Based on what you
see, which of the exponential smoothing models do you think should be
used for forecasting? Why?b.Use Holt’s exponential smoothing to forecast
these data, using no holdout period and requesting 20days of future
forecasts. Use the default smoothing constants of 0.1.c.Repeat part b,
optimizing the smoothing constants. Does it make much of an
improvement?d.Repeat parts a and b, this time using a holdout period of
50 days.e.Write a short report to summarize your results.
46.The
file P12_46.xlsx contains monthly time series data for total U.S.
retail sales of building materials, garden equipment, and supplies
dealers.a.Is seasonality present in these data? If so, characterize the
seasonality pattern.b.Use the Deseasonalize option in StatTools to
forecast the deseasonalized data for each month of the next year using
the moving average method with an appropriate span.c.Does Holt’s
exponential smoothing method, with optimal smoothing constants,
outperform the moving average method employed in part b? Demonstrate why
or why not.
60.The
file P12_60.xlsx lists annual revenues (in mil-lions of dollars) for
Nike. Create a time series graph of these data. Then superimpose a trend
line with Excel’s Trendline option. Which of the possible Trendline
options seems to provide the best fit? Using this option, what are your
forecasts for the next two years?
62.The file P12_62.xlsx contains data on a motel chain’s revenue and advertising.
b.Use
simple exponential smoothing to make predictions for the motel chain’s
revenues during the next four quarters.c.Use Holt’s method to make
forecasts for the motel chain’s revenues during the next four
quarters.d.Use Winters’ method to determine predictions for the motel
chain’s revenues during the next four quarters.
e.Which of these forecasting methods would you expect to be the most accurate for these data?
63.
The file P12_63.xlsx contains data on monthly U.S. permits for new
housing units (in thousands of houses).a.Using Winters’ method, find
values of α, β, and γthat yield an RMSE as small as possible. Does this
method track the housing crash in recent years?b.Although we have not
discussed autocorrelation for smoothing methods, good forecasts derived
from smoothing methods should exhibit no substantial autocorrelation in
their forecast errors. Is this true for the forecasts in part a?c.At the
end of the observed period, what is the forecast of housing sales
during the next few months?
THE QUESTIONS NEED TO BE ...Palisade outputs are all complete and correct, Functions and formulas are complete and accurate, Narrative answers to problems are flawless with extensive supporting details.