# Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for

Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 33 sales periods. Each sales period is defined as one month. The variables are as follows:

**Demand** = Y = demand for a large size bottle of Fresh (in 100,000)

**Price** = the price of Fresh as offered by Ent. Industries

**AIP** = the average industry price

**ADV** = Ent. Industries Advertising Expenditure (in $100,000) to Promote Fresh in the sales period.

**DIFF** = AIP - Price = the "price difference" in the sales period

6. Use Exponential smoothing forecasts with an alpha of 0.1, 0.2, ..., 0.9 to predict March 2019 demand. Identify the value of alpha that results in the lowest MAD.

7. Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de-seasonalized demand values by dividing monthly demand by corresponding seasonal indices.

8. Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD and explain the Excel Regression output (trend equation, r, r-squared, goodness of model).

9. Find the seasonally adjusted trend forecasts for March through May 2019.

10. Perform simple linear regression analysis with ADV as the independent variable. Write the complete equation, find MAD and explain the Excel Regression output. Make sure to use the deseasonalized demand data for this model and all future models.

11. Repeat part (10) with DIFF as the independent variable.

12. Construct multiple linear regression model with Period, AIP, DIFF, and ADV as independent variables. Formulate the equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

13. Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variables. Formulate the equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

14. Use the model obtained in parts 13 and make forecasts for the following months. Make sure to seasonalize final forecasts.

**Period Year Price AIP ADV_**

March 2019 ** **$6.10 ** **$6.50 ** **$10.3

April 2019 ** **$6.30 ** **$6.60 ** **$10.7

May 2019** ** $6.50 ** **$7.10 ** **$11.1

- Expert Answer

## 1 Interpretation Only the trend of PRICE is negative Other four variables have positive trends However the R 2 values suggest that for ADV and DEMAND View the full answer

**Related Book For**

## Probability and Statistics for Engineers and Scientists

ISBN: 978-0495107576

3rd edition

Authors: Anthony Hayter

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