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  4. Data Analysis and Probability

Data Analysis and Probability

Objectives

The course provides students with essential tools for the analysis of different data types and basic knowledge of probability theory and random variables. Students will use both computational and graphical methods to analyze the necessary information that will enable decision-making in Economics and Management.

General characterization

Code

1304

Credits

7.5

Responsible teacher

Patrícia Ferreira Ramos

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese and English

Prerequisites


Bibliography

Newbold, P., Carlson, W. and Thorne, B. Statistics for Economics and Management: Global Edition. Pearson, 2013. ISBN: 9780273767090

Teaching method

Lectures, practical lessons in the classroom and practical classes with the use of Excel.

Evaluation method

The Final Exam is mandatory and must cover the entire span of the course. Its weight in the final grade can be between 30 to 70%. The remainder of the evaluation can consist of class participation, midterm exams, in class tests, etc. Overall, written in class assessment (final exam, midterm) must have a weight of at least 50%.

Regular Exam Period
Continuous assessment elements (and their weights): Midterm1 (15%), Midterm2 (15%) and group project (15%).


Final exam (and their weighting): final exam (55%), with a minimum grade of 7.5


Resit Exam Period
Continuous assessment (and their weights) if different than 100%:

Final exam (and its weight): final exam (100%)


Grade Improvement in Regular Period
Continuous assessment (and their weights) if the scanning feature doesn’t count 100%: Midterm1 (15%), Midterm2 (15%) and group project (15%).

Final exam (and its weight): final exam (55%), with a minimum grade of 7.5


Grade Improvement in Resit Period
Continuous assessment (and their weights) if different than 100%: Final exam (and their weighting): final exam (100%)

Subject matter

0: Introduction to Excel
1: Descriptive Statistics – frequency distribution
2: Descriptive Statistics – numerical measures
3: Simple linear regression
4: Time series
5: Probability
6: Discrete probability distributions
7: Continuous probability distributions