Methods of PPE II


Course Objective

Knowledge of statistics is crucial for performing empirical research and
for understanding the academic literature related to PPE. The objective
of this course is to provide students with the essential knowledge of
statistics and to introduce them to the basics of econometrics. The
course also provides essential tools for carrying out practical
research, as part of the next years in the curriculum.
Specific learning outcomes upon completion of this course are:
• Understanding of key concepts in probability theory and statistics
• Knowledge of basic statistical/econometric techniques
• Ability to interpret descriptive statistics and results of
statistical/econometric analyses; understanding what conclusions can and
cannot be drawn from such analyses
• Understanding the difference between correlation and causation
• Ability to formulate a regression model and estimate its parameters to
answer a quantitative research question
• Ability to perform simple analysis using statistical software

Course Content

This course familiarizes PPE-students with both the theory and practice
of statistics. They will be trained in formulating a research question
into a model specification and in translating empirical results into
policy recommendations, skills that are valuable both in PPE-studies and
thereafter. The course starts with discussing key concepts in
probability theory and statistics, like distributions, expectation and
variance. Building on that, the students learn about estimating
parameters, confidence intervals, testing hypotheses and the
interpretation of significance. The second part of the course provides
an introduction to econometrics. The most frequently used econometric
technique is studied: the linear regression model. It is shown how
different types of variables can be included in these models, in
particular dummy variables. Special attention is given to the
interpretation of the model parameters, whereby we distinguish
correlation from causation and discuss omitted variables and
multicollinearity. The latter issues are important for drawing
conclusions and making policy recommendations based on empirical
Throughout the course, the theory will be applied to real data using
statistical software in a series of problem sets.

Teaching Methods

Lectures and seminars (maths labs and active learning groups). Please
note that participation in the seminars is mandatory.

Method of Assessment

Written exam (70%) and software assignments (30%).

Entry Requirements



An Introduction to Statistical Methods & Data Analysis. Seventh Edition.
R. Lyman Ott & Michael Longnecker, Cengage.

Target Audience

First year PPE students

Custom Course Registration

There is a slightly different enrollment procedure for this module. The standard procedure of the Faculty of Humanities has students sign up for (i) the module, (ii) the form of tuition (lecture and/or preferred seminar group), and (iii) the exam. However, for this module the instructor will assign the students to the seminar groups. Therefore, students should sign up for (i) the module, (ii) lecture and (iii) the exam, but not for the seminar groups.

General Information

Course Code W_JSM_107
Credits 6 EC
Period P4
Course Level 100
Language of Tuition English
Faculty Faculty of Humanities
Course Coordinator dr. R. Heijungs
Examiner dr. R. Heijungs
Teaching Staff dr. R. Heijungs

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Seminar*

*You cannot select a group yourself for this teaching method, you will be placed in a group.

Target audiences

This course is also available as: