Introductory statistics – Econ 2140
Syllabus – Fall 2024
General Description and objective
This is a first course in statistics for undergraduate economics and business students. The course is also adequate for a broader social science audience (sociology, political science, psychology, …).
Prerequisites:
The course is self-contained as much as possible but does assume some degree of comfort with intermediate algebra. The university math core requirement is advised as a prerequisite.
Philosophy and content overview:
The course ultimately aims to provide working knowledge of statistics and therefore will privilege applications over theory. That does not mean theory is neglected as sound applications do require laying sound theoretical foundations. Therefore, we will balance theory and applications, such that applications can be fully understood and appraised with a critical mind.
The course will start with elements of descriptive statistics. That is, we will study how to represent and summarize data. In business, economics and finance (to mention just a few fields), there is a widespread need to deal with datasets, sometimes huge ones. For example, one may need to make sense of data accumulated on social networks for millions of users, data relative to financial markets, data relative to macroeconomic variables for the world’s countries … Once such data is collected, it is quite useless unless one can summarize it with a few meaningful statistics that reveal the data content. We will study methods towards that goal.
Furthermore, the course introduces students to statistical inference. Statistical inference deals with the following question: “how to say something about a population given that we only observe a (potentially small) part of it” . For example, in order to learn US voters’ intentions for the next election, it is quite an impossible to ask all of them their voting intentions. We only have access to surveys asking voting intentions of only a (sometimes small) subset of US voters.
Then, what can be said about the population of US voters, given that we only observe a sample of them? We will learn that, despite the fact that we only observe a small part of the population, not only can we say something about the population, but we can also quantify the degree of certainty with which we can make population statements.
Statistical inference is somewhat more involved theoretically than the first descriptive part of the course. We will study tools that are required to perform. statistical inference. These tools include notably random variables, their distribution (e.g. the normal distribution) and the so-called central limit theorem.
Importance of a quantitative education
The inferential part of the course is the foundation for most empirical practice in economics, business, and social science in general. It is therefore of substantial importance, for anyone who wishes to make a career in related field, such that one can appraise these issues with a critical mind.
Even if one does not make a career in a field that directly requires statistics, statistical education is of great importance, as with statistics, one learns how to think rationally about randomness or uncertainty, which is valuable to anyone, working in the statistical field, or not.
Moreover, the course aims to develop critical thinking. That is, we will learn how to judge if numbers we are usually confronted with in everyday life (for example a newspaper reporting survey results, a portfolio manager advertising past portfolio performance, a real estate agent reporting average selling price in a neighborhood,…) make sense, or not.
Requested text
OpenStax College, Introductory Statistics. 2nd edition. OpenStax College.2023.
https://assets.openstax.org/oscms-prodcms/media/documents/Introductory_Statistics_2e_-_WEB.pdf
Software
We may use occasionally statistical software to work out some applications. Core software: Excel, R
Other excellent software: Eviews, Oxmetrics (there is a free version of it, as well as student prices), Matlab, Stata, Python.
Course requirements:
Students are expected to attend classes and master the covered material. That requires studying the material after each class, using your own notes, but also the textbook. Attendance is mandatory and active participation highly valued and encouraged. Good note taking is essential.
Students should be able to re-do every exercises covered in class and should ask the help of the instructor if they have any issues in that process. No material should be left misunderstood.
One midterm and one final tests are organized. Each test is given a weight of 50%.
Exams dates are strict. As a fairness matter, no make-up exams will be accepted, as everybody is given the same preparation time.
Besides tests, training exercises will be proposed each week and solved in class. It is student’s responsibility to prepare this material carefully and regularly.
Topics covered (tentative):
Week 1 (Sep 3) Introduction – sampling and data Week 2 (Sep 10) Descriptive statistics
Week 3 (Sep 17) Descriptive statistics / Probability topics Week 4 (Sep 24) Probability topics
Week 5 (Oct 1) Discrete random variables Week 6 (Oct 8) Midterm preparation
Week 7 (Oct 15) Test (tentative)
Week 8 (Oct 22) Discrete random variables
Week 9 (Oct 29) Continuous random variables
Week 10 (Nov 5) University closed – presidential election
Week 11 (Nov 12) Continuous random variables Week 12 (Nov 19) The CLT
Week 13 (Nov 26) Hypothesis testing / confidence intervals Week 14 (Dec 3) Hypothesis testing / confidence intervals Week 15 (Dec 10) Final preparation / Last class
Final test week: Dec 13 until Dec 20. It is an absolute obligation to be present during midterm and final exam weeks.
Further important rules:
Exam dates are strict. No delayed exams are accepted unless force majeure.
Taking pictures or recording anything in any form. (sound, video) is strictly prohibited. Should lectures recorded by the professor be made available, students must follow the University protocol regarding the handling of such recordings, i.e. they cannot be shared or made public in any ways.
Generative AI tools are not permitted in this course. Students must rely on their own originality, creativity and critical thinking skills to complete assignments and engage with course material.