BUSINESS ANALYTICS: DATA, MODELS AND DECISIONS

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Homework 9

  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)?

  • 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?

  • 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.