# 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:
• 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

67919

3.5

##### Responsible teacher

Luís Miguel Rainho Catela Nunes

##### Hours

Weekly - Available soon

Total - Available soon

English

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)

## Programs

Programs where the course is taught: