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040639 UK Exact Tests not only for Experimental Economics (MA) (2013W)
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 Fr 06.09.2013 09:00 to Fr 20.09.2013 14:00
- Registration is open from We 25.09.2013 09:00 to Th 26.09.2013 17:00
- Deregistration possible until Mo 14.10.2013 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Monday 07.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 14.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 21.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 28.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 04.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 11.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 18.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 25.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 02.12. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 09.12. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 16.12. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 13.01. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Monday 20.01. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 28.01. 14:00 - 16:00 Seminarraum 3 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
Overview of Material:
Basics: Concepts: Null and alternative hypothesis, type I and II error, level, size, power, pvalue,
confidence interval. Data types: Single sample, independent samples, matched pairs,
Discussion of the usefulness of normality for large data sets.
Existing tests:
Binomial test with and without assuming identically distributed data, sign test, confidence
interval for the median and other quantiles, confidence interval of distributions,
permutation tests including Wilcoxon Mann Whitney and spearman rank correlation test.
New tests:
Test for the mean of a single sample, for comparing means given two independent samples
and for matched pairs, for the variance of a single sample and for comparing variances and
for analyzing covariance, for investigating tendencies described by a stochastic inequality,
and for running linear and ordinal regressions.
Reading material
Motulsky, Harvey (1995) "Intuitive Biostatistics," Oxford: Oxford Univ. Press.
- a bit vague but precise
Lehmann and Romano (2005) Lehmann, E. L. and Romano, J. P. (2005), Testing Statistical
Hypotheses. New York: Springer.
- very precise but too mathematical for the applied
Schlag (2013): Exact Hypothesis Testing without Assumptions - New and Old Results
not only for Experimental Game Theory
http://homepage.univie.ac.at/karl.schlag/research/statistics/exacthypothesistesting.pdf
and then there are original papers that some lectures will be based on:
Gossner and Schlag (2013): Finite-sample exact tests for linear regressions with bounded
dependent variables, Journal of Econometrics 177, 75-84.
Hoeffding, W. (1956), "On the distribution of the number of successes in independent
trials." The Annals of Mathematical Statistics, 27, 713-721.
Massart, P. (1990), "The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality," The
Annals of Probability 18, 1269-1283.
Basics: Concepts: Null and alternative hypothesis, type I and II error, level, size, power, pvalue,
confidence interval. Data types: Single sample, independent samples, matched pairs,
Discussion of the usefulness of normality for large data sets.
Existing tests:
Binomial test with and without assuming identically distributed data, sign test, confidence
interval for the median and other quantiles, confidence interval of distributions,
permutation tests including Wilcoxon Mann Whitney and spearman rank correlation test.
New tests:
Test for the mean of a single sample, for comparing means given two independent samples
and for matched pairs, for the variance of a single sample and for comparing variances and
for analyzing covariance, for investigating tendencies described by a stochastic inequality,
and for running linear and ordinal regressions.
Reading material
Motulsky, Harvey (1995) "Intuitive Biostatistics," Oxford: Oxford Univ. Press.
- a bit vague but precise
Lehmann and Romano (2005) Lehmann, E. L. and Romano, J. P. (2005), Testing Statistical
Hypotheses. New York: Springer.
- very precise but too mathematical for the applied
Schlag (2013): Exact Hypothesis Testing without Assumptions - New and Old Results
not only for Experimental Game Theory
http://homepage.univie.ac.at/karl.schlag/research/statistics/exacthypothesistesting.pdf
and then there are original papers that some lectures will be based on:
Gossner and Schlag (2013): Finite-sample exact tests for linear regressions with bounded
dependent variables, Journal of Econometrics 177, 75-84.
Hoeffding, W. (1956), "On the distribution of the number of successes in independent
trials." The Annals of Mathematical Statistics, 27, 713-721.
Massart, P. (1990), "The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality," The
Annals of Probability 18, 1269-1283.
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.