Course title                      Data Analysis 2 - Pattern discovery and regression analysis   

Instructor                         Tímea Laura Molnár


Office                                A509 on QS 3rd floor

Office Hours                      Wednesdays, 5pm-5.45pm

Teaching Assistant          Costanza De Acutis


(Optional) TA Discussion Group//Office Hours: 5 Mondays on Nov6 (C-323),13,20,27,Dec11 (D108)

Credits                              2 US credits (4 ECTS credits)

Module                             Mandatory in: Master of Arts in Economic Policy in Global Markets

Term and Time                Fall 2 term in AY 2023-2024, Tue and Thu 13.30-15.20 (with 10 mins break after 50 mins); venue as indicated in your schedule

Course level                     Master

Prerequisites                   Data Analysis 1

Uncovering patterns in the data can be an important goal in itself, and it is the prerequisite to establishing cause and effect and carrying out predictions. The course starts with simple regression analysis, the method that compares expected y for different values of x to learn the patterns of association between the two variables. It discusses nonparametric regressions and focuses on the linear regression. It builds on simple linear regression and goes on to enriching it with nonlinear functional forms, generalizing from a particular dataset to other data it represents, adding more explanatory variables, etc. We also cover regression analysis for binary dependent variables, as well as nonlinear models such as logit and probit. We will discuss selected case studies in lectures, and interpretation and coding solutions will both be discussed using STATA.

Grading will be based on the total score out of 100, in line with CEU’s standard grading guidelines, as in:

Homework (HW) Assignments (see schedule below)                                          50% (5 in total, each worth 10%)

Final Exam: on Wednesday, December 13, 2023, 9am-noon:                           50%

Students have to score at least 50% for both the HW Assignments and the Final Exam to pass the course.

 - Regular class attendance is a precondition for course completion. Students who miss 3 or more classes, either excused or unexcused, cannot receive a passing grade. In line with the departmental no-phone-policy regulation, no cell phones are allowed to be used in class (and computers/tablets only for note-taking if needed).

- The Teaching Assistant (TA) for the course is PhD student Costanza De Acutis (email: The TA will hold TA Discussion Groups on selected Monday afternoons on which attendance is not compulsory but highly recommended, as the solution of the assignments will be presented and discussed, as well as students can ask the TA prior HW submission for hints and clarifications.

- Questions regarding the assignments, the assignment solutions and grading should be directed to the TA.

- All teaching materials (lecture notes, assignments, assignment solutions, more practice problems and other materials) will be uploaded to Moodle.

- Students are allowed to work in study groups (discussion groups) on homework assignments; but everyone is required to submit her/his/their own assignment spelling out the solution on her/his/their own; copying from other students will lead to zero grade for that HW assignment for all parties involved. Late assignments receive 0 credit.