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040639 UK Exact Tests not only for Experimental Economics (MA) (2015W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 14.09.2015 09:00 to Th 24.09.2015 14:00
- Deregistration possible until We 21.10.2015 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
07.10.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
14.10.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
21.10.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
28.10.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
04.11.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
11.11.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
18.11.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
25.11.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
02.12.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
09.12.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
16.12.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
13.01.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
20.01.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday
27.01.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
The grade is made up of a) a midterm, b) a final and c) homeworks that involve finding
data sets, and analyzing data sets. Each of these three parts will be separately graded and
counts equally towards the final grade.
Prerequisites: knowledge of statistics at an undergraduate level.
data sets, and analyzing data sets. Each of these three parts will be separately graded and
counts equally towards the final grade.
Prerequisites: knowledge of statistics at an undergraduate level.
Minimum requirements and assessment criteria
In this course we will give an overview and understand of existing and new methods for
testing hypotheses and running regressions that are exact. One goal of this course is to teach
students how to use R in order to analyze data sets. Laptops will be used in class to
demonstrate methods. Students will learn how to analyze data sets and how to read and
understand empirical papers.Who is this course for? Anyone who is curious and
who is genuinely interested in uncovering what is hidden in the data and who is interested
in making mathematically sound claims. Of course many applications cannot be dealt (yet)
with an exact method as often there is too much going on. However this course will
demonstrate that there are lots of relevant areas where one can make exact statements,
including running linear regressions.
testing hypotheses and running regressions that are exact. One goal of this course is to teach
students how to use R in order to analyze data sets. Laptops will be used in class to
demonstrate methods. Students will learn how to analyze data sets and how to read and
understand empirical papers.Who is this course for? Anyone who is curious and
who is genuinely interested in uncovering what is hidden in the data and who is interested
in making mathematically sound claims. Of course many applications cannot be dealt (yet)
with an exact method as often there is too much going on. However this course will
demonstrate that there are lots of relevant areas where one can make exact statements,
including running linear regressions.
Examination topics
Statistics is a science about how to analyze data. Classical statistical methods often, in fact
most statistical methods typically make claims about data sets that are not in accordance
with the underlying theory and methodology. This is because they make claims about
significance that are based on assuming that the data is infinitely large (they are based on
asymptotic theory). Remember that typically we do not think that the data is normally
distributed, but that is approximately and we will talk about why this sort of approximation
is not what one needs.
most statistical methods typically make claims about data sets that are not in accordance
with the underlying theory and methodology. This is because they make claims about
significance that are based on assuming that the data is infinitely large (they are based on
asymptotic theory). Remember that typically we do not think that the data is normally
distributed, but that is approximately and we will talk about why this sort of approximation
is not what one needs.
Reading list
Association in the course directory
Last modified: Mo 07.09.2020 15:29
theory and understanding which approaches do what they say they do. Exact testing refers
to methods do exactly this, they have properties that can be formally proven. Claims that
are not based on a handful of simulations when the underlying set of possible data
generating processes is so rich that one can never simulate many.