MINITAB ASSIGNMENT ECO FORCASTING (FOR JAM007 ONLY)

FOR JAM007 ONLY PLEASE

Chapter 4: Chapter 4 – Assignment 4

Assignment 4 Project Part 2A — The Exponential Smoothing Forecast.   Due by midnight 10/14.  Get it in on time or it will not be graded.  This part of the assignment is worth up to 2.5 extra credit points and can serve as the exponential smoothing part of your class project. 
 Show your work and submit it to the Chapter 4 Assignment 6 Dropbox. 
This assignment addresses forecasting your selected Y data (dependent variable) using an exponential smoothing technique.  Note:  Do not use the X (independent) variables in this exercise.  Use only one exponential smoothing method — the best that applies. Do not use any other forecasting techniques in this assignment.  Turn in only the one best model that you develop.
(Remember– 1. Do not show failed models in business reports.  Share your failures with your family if you wish and not with your boss or instructor.and 2. Never use Y hold out data observations in any forecast model.)
a) Tell me why you selected the appropriate exponential smoothing method by commenting on your Y data characteristics. (you should use a time series plot and autocorrelations to do this), 
b) Apply the appropriate exponential smoothing forecast technique to your Y variable excluding the last two years of data (8 quarter hold out period).  Show the Y data, fitted values and residuals in excel format and show your exponential smoothing model coefficients. (Find the correct coefficient and not just use the default values.)
c) Evaluate the “Goodness To Fit” using at least two error measures — RMSE and MAPE.
d) Check the “Fit” period residual mean proximity to zero and randomness with a time series plot; check the residual time series plot and autocorrelations (ACFs)  for trend, cycle and seasonality.  
e) Evaluate the residuals for the “Fit” period by indicating the residual distribution using a histogram (normal or not and random or not), 
f) Comment on the acceptability of the model’s ability to pick up the systematic variation in your Fit period actual data.
g) Develop a two year quarterly forecast (for the hold out period). 
h) Evaluate the “Accuracy” of the forecast for the “hold out period” using RMSE and MAPE error measures used from forecast period residuals and comment them.  
i) Do the forecast period residuals seem to be random relative to the hold out period data? Check the forecast period time series plot of the residuals.
j) Did the error measures get worse, remain the same or get better from the fit to the hold out period?  Do you think the forecast accuracy is acceptable?
Show your work and graphs in a Word document.  Make sure that you comment on statistics and graphs relevant to answering the above questions.  DO NOT leave statistics and graphs stranded.  If you show something write about it.  Note that this work will become part of your class project so do a good job on it.
 

Chapter 5: Chapter 5 – Chapter 5 Assignment 5

Assignment 5 Project Part 2B — The Decomposition Forecast.  Due by midnight 10/21.  Get this in on time or it will not be graded.  This part of the assignment is worth up to 2.5 extra credit points and can serve as the Decomposition part of your class project. 
Show your work and submit it to the Chapter 5 Assignment 7 Dropbox.  (Again — 1. Do not show failed models in business reports.  Share your failures with your family if you wish and not with your boss or instructor.and 2. Never use Y hold out data observations in any forecast model.)
a) Perform Time Series Decomposition on your project Y variable excluding the hold out period.  Show me the smoothed Trend Values (TREN in Minitab) , Smoothed Cycle Values (use Minitab Calculator to DESE/TREN for Cycle Factors)  and Seasonal Indexes (SEAS in Mintab).  
— note that you must use the last cycle factor and multiply each forecast observation by it to get a cycle adjusted forecast.  Since this is a multiplicative decomposition model this must be done Minitab result to obtain a reasonable forecast.  We have discussed this procedure in class.

b) Show the seasonal indices (SEAS in Minitab) and develop a one year time series plot of them.  Do they indicate strong seasonality?  How can you tell?

c) Evaluate the “Goodness To Fit” using RMSE and MAPE error measures .

d) Evaluate the residuals for the “Fit” period by indicating the residual distribution (random or not).  Use a fit period residual time series plot, residuals ACFs and a histogram to determine if the Fit period residuals are random.  If the residuals are not random state if you detect any trend, cycle and seasonality autoregressive characteristics.  (Note: you expect to see only cycle in the residuals — any T or S is a signal that the model did not use this information.  You will adjust the cycle component in the forecast by using the last cycle factor in the forecast.)

e) Develop a two year quarterly forecast (for the hold out period) using the time series decomposition model you evaluated in c) above and adjust the forecast with the last cycle factor.  Evaluate the reasonableness of the forecast by appending the cycle adjusted decomposition forecast to the Y data and developing a time series plot.

f) Evaluate the “Accuracy” of the model for the “hold out period” using the RMSE and MAPE measures used in part b) and comment on them.  Did the error measures increase, remain the same or decrease from the “Fit” to “Hold Out” or forecast period?

Show your work and graphs in a Word document.  Make sure that you comment on statistics and graphs relevant to answering the above questions.  Again, this will be the decomposition portion of your class project.