Uncertainty and Decision Making
Objectives
On completion of this course a student should be able to:
A. General Knowledge and Understanding:
• Use and interpret business data
• Understand quantitative models applied to business
• Apply a business model to a particular data set
• Interpret the empirical results of a model
B. Subject-Specific Skills:
• Represent business data graphically
• Compute descriptive statistics of business data
• Interpret the model parameters
• Use the computer to explore relationships between business variables
• Test the statistical significance of the estimates
• Test specific values for the model parameters
• Statistically evaluate the quality of the estimation
• Choose between alternative model specifications
• Understand the difference between correlation and causality
C. General Skills:
• Choose an appropriate quantitative model to illustrate a
theoretical relation
• Interpret the estimated model.
General characterization
Code
67919
Credits
3.5
Responsible teacher
Luís Miguel Rainho Catela Nunes
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
N/A
Bibliography
Newbold, Carlson and Thorne, Statistics for Business and Economics, Pearson Education, 8th Edition, 2013. Chapter 18
(http://www.pearsonhighered.com/newbold/).
Teaching method
The course combines theory and practice. Participants will have an opportunity in the course to apply theory, concepts,
skills, and management tools to real situations. The course will be based on lectures, applications, and exercises. An
important component of the learning process involves the completion of several assignments while working in a team.
Evaluation method
The final grade will be based on group assignments (50%) and a final exam (50%).
Subject matter
The following topics are covered in this course (in parenthesis are the corresponding chapters of the main textbook): 1.
Descriptive Statistics: Graphical and Numerical (1, 2); 2. Probability Theory (3); 3. Random Variables and Probability
Distributions (4.1-4.4, 4.7, 5.1-5.3, 5.6); 4. Decision Theory (18) 5. Statistical Inference (6.1-6.3, 7.1-7.4, 7.6-7.7, 8, 9.1-9.4,10.1-
10.3) 6. Regression Analysis (11, 12, 13.1-13.2, 13.4-13.5) 7. Categorical Data Analysis (14.1,14.3)