Managerial Decision Making

Course Description

This course introduces a practical, analytical approach to managerial decision making. Analytic thinking, systems thinking, and creative thinking will be employed in the context of a variety of business problems. Topics include decision-making models; dealing with certain, uncertain, and unknowable; managing risk; sensitivity analysis; probabilistic decision models; decision trees; game theory; survey design; and regression analysis.

Topics and Objectives

Quantitative Methods for Problem Solving I

  • Define descriptive statistics, basic probability, normal distribution concepts, hypothesis testing, research instruments, and valid reliable surveys.
  • Recognize the applications of descriptive statistics, basic probability concepts, normal distribution concepts, and hypothesis testing in decision making.
  • Analyze data using descriptive statistics.

Problem Solving Analysis and Alternative Solutions I

  • Distinguish among what is knowable, unknowable, and researchable.
  • Apply inferential statistics in solving business problems.
  • Apply probability analysis to deal with uncertainty.
  • Interpret the results of business research in order to recommend appropriate strategies with incomplete information.

Critical Analysis of Problem Solutions I

  • Apply Analysis of Variance (ANOVA) in solving business problems.

Quantitative Methods for Problem Solving II

  • Define correlation, regression, risk analysis, decision trees, and game theory.
  • Recognize the applications of correlation, regression, risk analysis, decision trees, and game theory in decision making.

Problem Solving Analysis and Alternative Solutions II

  • Apply quantitative methods to assess uncertainty and risk.
  • Employ correlation in making business decisions.
  • Explain the uses of regression analysis.
  • Employ trend analysis and forecasting techniques in making business decisions.

Critical Analysis of Problem Solutions II

  • Evaluate strategic alternatives using game theory.
  • Evaluate business alternatives using sensitivity analysis.
  • Evaluate business alternatives using decision trees.
  • Analyze the output produced by regression analysis.

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