This course provides an introduction to statistics and probability theory. It explores how these two disciplines combine to offer a versatile framework for modeling uncertainty and how various existing tools can be used to support effective decision-making in different business contexts, among others.
Titular Professors
Professors
None
- Understand the fundamental concepts of probability and statistics.
- Solve problems in both univariate and multivariate situations.
- Learn about the main statistical distributions and how to use their tables.
- Introduce students to linear data modeling.
- Improve competence in data management and spreadsheet use.
1. Descriptve Statistics
2. Probability
3. Random variables
4. Statistical Inference
5. Linear Regression
Weekly teaching will consist of three lecturing sessions to explain basic concepts to apply knowledge to practical situations. Exercises in class will be solved and problems will also be proposed so that students can apply the concepts learned.
Each topic will have the following elements:
- Theory: Lectures
- Case resolution: Problem-solving
- Concepts review: Evaluative activities
There will be two evaluation systems: Continuous Assessment (CA) and Final Exam (FE). The final grade will be the maximum value between CA and FE.
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- Bowerman, B. L., O'Connell, R. T., Murphree, E., Huchendorf, S. C., Porter, D. C., & Schur, P. (2003). Business statistics in practice. New York: McGraw-Hill/Irwin.
- Newbold, P., Carlson, W. L., & Thorne, B. M. (2013). Statistics for business and economics. Pearson.
- Johnson, R. A., & Bhattacharyya, G. K. (2019). Statistics: principles and methods. John Wiley & Sons.
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