Statistics for Economics and Management
Objectives
On a daily basis, economists and managers are confronted with decision-making processes in contexts of uncertainty. Statistics offers a number of tools allowing the measurement of this uncertainty by making use of statistical data, thereby leading to better decisions. The Statistics for Economics and Management course focuses primarily on inferential statistics whose purpose is to better understand particular characteristics of a given population using only sample data, whether they are observational or experimental.
One of the main objectives of this course is that students develop the ability to think statistically. Statistical thinking means the understanding of the necessity, the advantages and the limitations of available data, and the ability to conduct a statistical study in its various phases, since the formulation of the problem, through data collection and statistical analysis, to the interpretation of results. It is intended that students recognize that the real world is a complex system of interconnected processes, that there is variability in all processes, and that understanding and reducing variability is a critical factor for success. Recognizing the omnipresence of variability is part of the essence of Statistics.
General characterization
Code
1313
Credits
7
Responsible teacher
Patrícia Ramos
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese | English
Prerequisites
Mandatory precedence: Data Analysis and Probability
Bibliography
Newbold, Carlson, and Thorne, Statistics for Business and Economics, Pearson Education, 2013.
Teaching method
The understanding of key statistical concepts and tools and the development of statistical thinking is achieved through the use of several real-world examples. Various problems and actual case studies are presented and explored in the lectures (2 per week) and practical sessions (1 per week). The fundamental concepts of statistics and the mathematical derivation of the fundamental results are covered in detail during the lectures. In the practical sessions, several problems and exercises are discussed, and it is important that students participate in the discussion by presenting their solutions, suggestions, questions, and comments. For some practical sessions, and according to the instructions of the teaching assistants, students should bring a laptop/tablet to work on projects that use data collected from the internet or databases in Excel. In the moodle page will be posted several "quizzes" for each topic that all students must answer for a self-evaluation of their knowledge. Students must form groups (5 students per group) to prepare and deliver the final course assignment.
Evaluation method
The final grade in the course is calculated according to the
following weights:
Midterm exam (individual): 30%
Weekly quizzes: 5% (only the best 10 will be considered)
Final exam (individual): 50%
Final course assignment (groups of 5 students): 15%
The final exam covers all topics of the course. If the grade in
the final exam is less than 8.0, the student will fail regardless of the
remaining components of the assessment.
A student that misses the midterm exam will have a grade of 0
(zero) on that midterm exam. If this absence is regarded as justified by the
Pedagogical Council, the final exam grade will replace the midterm exam grade.
If the final assignment is not delivered by the deadline announced
in moodle it will have a grade of 0 (zero).
Resit Exam Period
Students who undertake the resit and special final exams, can choose between the continuous assessment model or the grade on that exam counts 100% for the final grade.
Subject matter
• Discrete and continuous random variables and distributions,
• Hypothesis testing,
• Simple and multiple linear regression,
• Introduction to categorical data analysis.
A detailed calendar of all activities and topics covered throughout the semester will be posted on moodle.
Programs
Programs where the course is taught: