| Course Objectives: |
Quantitative methods and spreadsheet skills to support management practice and decision making. Topics include statistical hypothesis testing, confidence intervals, regression analysis, optimization modeling, decision analysis and risk analysis. Therefore, this course develops quantitative methods and spreadsheet skills to support management practice and decision making including: hypothesis testing, confidence intervals, regression analysis, decision analysis, optimization and risk analysis. The course goals are: 1) Demonstrate the wide range of situations in which quantitative analysis improves decision making and creates competitive advantages; 2) Develop students’ analytical thinking skills. 3) Develop mastery of analysis using spreadsheet models, and effective communication of results. |
| Course Content: |
Upon completing the course, the student should be able to: 1. Describe a set of data using histograms, scatter diagrams and summary statistics. 2. Compute statistics from sample data to support confidence interval estimation, hypothesis testing and regression analysis. 3. Infer the statistical precision of insights derived from confidence interval estimation, hypothesis testing and regression analysis. 4. Construct effective models of decision making situations using principles of professional spreadsheet design. 5. Compute optimal solutions to decision making models for the management of a wide range of situations in which quantitative analysis improves decision making. 6. Analyze spreadsheet simulation models and decisions with uncertain outcomes by using multiple criteria for optimality and risk. |
| Week |
Subject |
Related Preparation |
| 1) |
Chapter 1 (Introduction to Quantitative Analysis) |
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| 2) |
Chapter 2 (Probability Concepts and Applications) |
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| 3) |
Chapter 2 (Probability Concepts and Applications) |
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| 4) |
Estimation & Confidence Intervals |
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| 5) |
Estimation & Confidence Intervals |
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| 6) |
Chapter (Regression Models) |
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| 7) |
Chapter (Regression Models) |
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| 8) |
Chapter (Decision Analysis)
|
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| 9) |
Linear Programming Models: Graphical & Computer Methods |
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| 10) |
Linear Programming Applications |
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| 11) |
Simulation Modeling |
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| 12) |
Introduction to probability, Bayes’ Theorem |
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| 13) |
Introduction to probability, Bayes’ Theorem |
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| 14) |
Game theory |
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