Excel file Energy Production & Consumption provides production, imports, exports, and consumption data.

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

PROBLEM #1 (10 marks): Problem #4 in Ch. 9 on page 352 using the Excel file ‘EnergyProduction &Consumption’. 

The Excel file Energy Production & Consumption provides production, imports, exports, and consumption data.

1. Develop line charts for each variable and identify the characteristics of the time series (that is, random, stationary, trend, seasonal, or cyclical).

2. In forecasting the future, discuss whether all or only a portion of the data should be used.

PROBLEM #2 (10 marks): Problem #8 in Ch. 9 on page 353 using the Excel file ‘ClosingStockPrices’. For #8b, compute and compare MAD and MSE only. 

The Excel file Closing Stock Prices provides data for four stocks and the Dow Jones Industrial Average over a one-month period.

3. Develop a spreadsheet for forecasting each of the stock prices and the DJIA using a simple two-period and three-period moving average.

4. Compute MAD, MSE, and MAPE and determine whether two or three moving average periods is better.

PROBLEM #3 (10 marks): Problem #9 in Ch. 9 on page 353 using the Excel file ‘ClosingStockPrices’. Don’t do #9b. For #9c, compute and compare MAD and MSE only. 

The Excel file Closing Stock Prices provides data for four stocks and the Dow Jones Industrial Average over a one-month period.

5. Develop a spreadsheet model for forecasting each of the stock prices using simple exponential smoothing with a smoothing constant of 0.3.

6. Compare your results to the output from Excel’s Data Analysis tool.

7. Compute MAD, MSE, and MAPE.

8. Does a smoothing constant of 0.1 or 0.5 yield better results?

PROBLEM #4 (5 marks): Problem #14 in Ch. 9 on page 353 using the Excel file ‘ConsumerPriceIndex’.

Consider the data in the Excel file Consumer Price Index. Use simple linear regression to forecast the data. What would be the forecasts for the next two years?

PROBLEM #5 (5 marks): Problem #30 in Ch. 9 on page 354 using the Excel file ‘MicroprocessorData’.

Data in the Excel File Microprocessor Data shows the demand for one type of chip used in industrial equipment from a small manufacturer.

9. Construct a chart of the data. What appears to happen when a new chip is introduced?

10. Develop a causal regression model to forecast demand that includes both time and the introduction of a new chip as explanatory variables.

11. What would the forecast be for the next month if a new chip is introduced? What would it be if a new chip is not introduced?

PROBLEM #6 (5 marks): Problem #14 in Ch. 11 on page 415. Hint: The relationship may not be linear.

 

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