GECN 501 Regression Project Harmon Foods, Inc. Analysis should include the following: A chart plotting case shipments (monthly from January 1984 to December 1987); a trend line should be shown as well. A chart showing the monthly Seasonality Index. Use case shipment information to create a new column – ‘Twelve month Moving Average (MA)’. Starting with December 1984, average the previous12 months including December 1984. For January

GECN 501

Regression Project
Harmon Foods, Inc.

Analysis should include the following:

A chart plotting case shipments (monthly from January 1984 to December 1987); a trend line should be shown as well.

A chart showing the monthly Seasonality Index.

Use case shipment information to create a new column – ‘Twelve month Moving Average (MA)’. Starting with December 1984, average the previous12 months including December 1984. For January 1985, average the previous 12 months including January 1985. Continue through December 1987. Create a chart that shows the smoothing effect on the data.

Use the monthly Seasonality Indices to create a seasonally-adjusted case shipment column.

5) The case suggests that promotional activities (consumer packs and dealer allowances, among others) affect overall demand, and may be felt over an extended period. As a result, the effects of promotional activities are felt during the month of the promotion and up to two months after the promotion. Create columns showing one-month and two-month lags of consumer packs and dealer allowances.

6) Create a trend variable indicating the month of the time series (month 1 for January 1984 thru month 48 for December 1987).

7) Perform a regression analysis with seasonally-adjusted case shipments as the dependent variable and trend, consumer pack (current and both lags), dealer allowance (current and both lags) as independent variables. Create a correlation table using the independent variables. Interpret results.

8) Use the residual results and create a column titled ‘Forecasted Case Shipments (SA)’. Multiply the results in this column by the respective monthly Seasonality Index/100 to create a column titled ‘Forecasted Case Shipments (NSA)’.

9) Perform a new regression analysis and create subsequent forecasted values after removing the insignificant variables from the first regression. Produce a new correlation table as well. Interpret the results.