; (Sales forecast, CMA adapted) Bailment Company is a temporary employment fimri that has offices throughout the United States. Bailment is a well established company in a highly competitive industry. It provides both clerical and professional workers to a broad range of customers. The temporary workers are hired for the day, a week, or a month at rates commensurate with each worker's skill level.

Bailment top management wants to develop a forecasting system in order to estimate revenue and to plan workforce levels. Management wants to forecast its revenues for each quarter during the current year as well as the annual revenue for each of the next five years.

Kathy Gregsen, Budget Analyst in the Bailment Accounting Department, has been given the responsibility for this project. She has detenmined that Bailment has historical data by month for at least the last 10 years. Her cursory review of the monthly revenue history indicates that Bailment business may have a pronounced seasonal pattem. She also suspects that the business may be cyclical in nature.

Gregsen has concluded that she would like to develop a statistically based sales forecasting system. She is considering the applicability of simple linear regression analysis and/or time series analysis. One of the things she would like to do is compare Bailment revenue to the Index of Industrial Production and to Gross National Product (GNP).

REQUIRED:

1 Kathy Gregsen needs to identify all of the things required to develop a forecasting system. Identify the typical things Gregsen needs in order to develop a forecasting system. 2 . For each of the two statistical techniques identified by Kathy Gregsen, i.e., simple linear regression analysis and time series analysis.

a. Describe the technique, being sure to include the purpose of the technique.

b. Identify the variable and/or data required to use the technique.

c. Explain how Gregsen will be able to evaluate the results of the analysis.

d. Discuss the strengths and weaknesses of the techniques in general.

e. Discuss the strengths and weaknesses of the technique in tenns of its relevance for varying lengths of forecasts and its reliability in forecasting cyclical turning points.