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Course details

Presents a second course in the application of statistical methods to business problems. The course will provide detailed theoretical understanding and practical applications of two of the most commonly used techniques in mathematical modeling Linear Regression and Time Series Analysis. You will learn how to view business situations as mathematical models and formulate the equations required for the model solution. Extensive lab work using computer software will lead to theoretical solutions. You will then learn how to interpret these solutions as a guide to practical management action. The course provides the opportunity to use and evaluate current software.


  • Acceptance into the Bachelor of Business Administration program.



Not offered this term
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Learning Outcomes

Upon successful completion of this course the student will be able to:

  • Use smoothing methods such as moving averages, exponential smoothing, Holt's and Holt-Winters' methods to predict time series.
  • Use a scatter plot to identify trend or seasonality in a time series.
  • Choose an appropriate forecasting method based on the characteristics of a time series.
  • Use a runs test to determine if a time series has the characteristics of a random walk series.
  • Forecast a series where appropriate using the random walk model.
  • Use linear regression to forecast or model business situations.
  • Evaluate the validity of linear regression in a given situation.
  • Assess the usefulness of linear regression in a given situation.
  • Select the most appropriate method for forecasting or modelling in a given situation.
  • Interpret the meaning of the coefficients and statistics generated by different mathematical models.
  • Analyse business mathematical models using Microsoft Excel 2002 (XP) together with add-ins provided.
  • Convert theoretical solutions into practical management terms.

Effective as of Fall 2015


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