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Choose One of the Forecasting Methods and Explain the Rationale Behind Using It in Real Life. What Is the Difference Between a Causal Model and a Time- Series Model?

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Choose one of the forecasting methods and explain the rationale behind using it in real life.
I would choose to use the exponential smoothing forecast method because it weighs the most recent past data more strongly than more distant past data. This makes it so that the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be in higher demand in recent times than past.
Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model.
By looking at the data of other companies the fast food chain would be able to put together a forecast to determine if their business venture was viable. They could examine the sales data and determine through a exponential smoothing forecast if it made sense for them to enter into the market. This would show the trends and changes in the data more recently rather than in past time.

What is the difference between a causal model and a time- series model? Give an example of when each would be used.
The time–series model is based on using historical data to predict future behavior. This method could be used by a retail store, fast food restaurant or clothing manufacturer to predict sales for an upcoming season change.
The causal model uses a mathematical correlation between the forecasted items and factors affecting how the forecasted item behaves. This would be used by companies would do not have access to historical data therefore they would use a competitors available data.
What are some of the problems and drawbacks of the moving average forecasting model?
One problem with the moving average method is that it does not take into account data that change due to seasonal variations and trends. This method works better in short run forecast and is not reliable in predicting things too far in the future.
How do you determine how many observations to average in a moving average model?
In order to determine the number of observations in the moving average model it is done by suing the trial and error experimentation.
How do you determine the weightings to use in a weighted moving average model?
In order to determine the weightings to use trial and error experimentation is used. If the weightings are inaccurate, the results of the model will not properly predict the future or give an accurate forecast of what is to come. Taylor, B. M. (2010). Introduction to management science (10th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.

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