Develop a regression model to forecast demand

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Reference no: EM133414858

1. The stock data provides the closing prices for one stock and the stock exchange over 12 days

a. Use Excel's Data Analysis Exponential Smoothing tool to forecast each of the stock prices using simple exponential smoothing with a smoothing constant of 0.3. (Hint: In Excel, the Damping factor = 1- the smoothing constant)

b. Compute the MAD (mean absolute deviation), MSE (mean square error), and MAPE (mean square error) for the above model.

c. Does a smoothing constant of 0.1 or 0.5 yield better results (Hint: compare the MAD, MSE and MAPE of the three models)?

2. The chip demand data shows the demand for one type of chip used in industrial equipment from a small manufacturer.

a. Create a line chart for the demand data overtime, what pattern do you observe? Inspect your data, what happens when new chips are introduced?

b. Develop a regression model to forecast demand that includes both time and the introduction of a new chip as explanatory variables (Hint: run the regression analysis and write out the equation of the predictive model for chip demand).

c. 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?

3. The coal consumption data shows the monthly coal consumption (in trillions of BTUs) over the course of three years.

a. Create a line chart for the demand data overtime, what pattern do you observe?

b. Develop a multiple regression model with variable month and categorical variables that incorporate seasonality for forecasting coal consumption, where December is the reference month. (Hint: run the regression analysis and write out the equation of the predictive model for coal consumption).

c. Was the effect of month significant at a = 0.05? What's the effect of month on coal consumption? What does it mean?

d. Based on your multiple regression model, predict the coal Consumption in March 1993.

Reference no: EM133414858

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