Teaching – Institute of Statistics – Leibniz University Hannover

COURSES OF STUDIES OF THE INSTITUTE

Bachelor

ECONOMICS AND MANAGEMENT (6 SEMESTER)

Master

ECONOMICS AND MANAGEMENT (4 SEMESTER)

ENGINEERING AND BUSINESS ADMINISTRATION (4 SEMESTER)

Summer term 2022

Bachelor Wirtschaftswissenschaft, PO 2017

Statistik

Schließende Statistik (270158)

	Termine:	Lehrpersonen:
	Mi. 09:15 - 10:45 \| VII-002 (Gruppe 1)	Sibbertsen
	Fr. 09:15 - 10:45 \| VII-201 (Gruppe 2)	Lehne
Inhalt:	Normalverteilung Binomialverteilung Stichproben Punktschätzung Intervallschätzung Statistische Tests Regressionsanalyse
Literatur:	Sibbertsen, P./Lehne, H. (2014) Statistik, 2. Auflage, Berlin. Fahrmeir, L. et al. (2004) Statistik, 5. Auflage Berlin. Schlittgen, R. (2003) Einführung in die Statistik, 10. Auflage München.

Übung zu Schließende Statistik (270159)

Termine: Lehrpersonen:

Mi. 07:30 - 09:00 | VII-002 (Gruppe 1) Sibbertsen

Mo. 09:15 - 10:45 | VII-201 (Gruppe 2) Lehne

	Termine:	Lehrpersonen:
	Mi. 07:30 - 09:00 \| VII-002 (Gruppe 1)	Sibbertsen
	Mo. 09:15 - 10:45 \| VII-201 (Gruppe 2)	Lehne

Tutorium zu Schließende Statistik (270031)

	Termine:	Lehrpersonen:
	Mo. 12:45 - 14:15 \| I-401 (Gruppe 1)	Tutor
	Mo. 12:45 - 14:15 \| I-442 (Gruppe 2)	Tutor
	Mo. 12:45 - 14:15 \| I-332 (Gruppe 3)	Tutor
	Mo. 14:30 - 16:00 \| I-442 (Gruppe 4)	Tutor
	Mo. 14:30 - 16:00 \| I-332 (Gruppe 5)	Tutor
	Mo. 14:30 - 16:00 \| I-063 (Gruppe 6)	Tutor
	Di. 09:15 - 10:45 \| I-442 (Gruppe 7)	Tutor
	Di. 11:00 - 12:30 \| III-115 (Gruppe 8)	Tutor
	Di. 16:15 - 17:45 \| I-063 (Gruppe 9)	Tutor
	Mi. 11:00 - 12:30 \| I-442 (Gruppe 10)	Tutor
	Mi. 14:30 - 16:00 \| III-115 (Gruppe 11)	Tutor
	Mi. 14:30 - 16:00 \| I-063 (Gruppe 12)	Tutor
	Mi. 16:15 - 17:45 \| I-442 (Gruppe 13)	Tutor
	Do. 09:15 - 10:45 \| II-013 (Gruppe 14)	Tutor
	Do. 09:15 - 10:45 \| VII-005 (Gruppe 15)	Tutor
	Do. 09:15 - 10:45 \| I-332 (Gruppe 16)	Tutor
	Do. 11:00 - 12:30 \| II-013 (Gruppe 17)	Tutor
	Do. 11:00 - 12:30 \| III-115 (Gruppe 18)	Tutor
	Do. 11:00 - 12:30 \| I-063 (Gruppe 19)	Tutor
	Do. 14:30 - 16:00 \| I-442 (Gruppe 20)	Tutor
	Fr. 07:30 - 09:00 \| I-063 (Gruppe 21)	Tutor
	Fr. 11:00 - 12:30 \| I-332 (Gruppe 22)	Tutor
	Fr. 11:00 - 12:30 \| I-063 (Gruppe 23)	Tutor
	Fr. 14:30 - 16:00 \| I-063 (Gruppe 24)	Tutor
Bemerkungen:	Bereitschaft zur aktiven Mitarbeit in den eingeteilten Tutoriumsgruppen wird erwartet. Die Gruppeneinteilung findet über Stud.IP am Mi. 13.04.2022 ab 11:30 Uhr statt.

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

Seminar Ökonometrie (273002)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	(Gruppe 2)	Mboya, Meier
Inhalt:	Thema des Seminars im Wintersemester 2018/2019 ist "Regressionsanalyse"
Bemerkungen:	Das Seminar wird als Blockveranstaltung durchgeführt. Nähere Angaben zur Themenvergabe und zum Zeitpunkt der Veranstaltung werden auf der Internetseite des Instituts für Statistik bekannt gegeben. Prüfer: Prof. Dr. Sibbertsen

Master Wirtschaftswissenschaft, PO 2018

Area Empirical Economics and Econometrics

Statistical Programming (373005)

	Termine:	Lehrpersonen:
	Do. 14:30 - 16:00 \| II-214	Flock
Inhalt:	Data Structures Functions and Loops Handling Data Graphics Linear Regression Numerical Optimization Monte Carlo Methods
Literatur:	Ligges (2007) Programmieren mit R, Berlin, Springer. Braun / Murdock (2007) A first course in statistical programming with R, Cambridge University Press. Rizzo (2008) Statistical Computing with R, Chapman & Hall.

Computerintensive Statistics (373015)

	Termine:	Lehrpersonen:
	Mo. 09:15 - 10:45 \| I-332	Mboya
Inhalt:	Metropolis Algorithm Adaptive Metropolis Algorithm Delayed Rejection Adaptive Metropolis Metropolis-Hastings Algorithm Gibbs Sampling
Literatur:	Albert, Jim (2007): Bayesian Computation with R, Springer

Seminar Applied Econometrics (373016)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	(Gruppe 2)	Flock, Less
Inhalt:	The seminar will be on "Machine Learning"
Bemerkungen:	Further Information about the seminar can be found on the website of the Institute for Statistics. Prüfer: Prof. Dr. Sibbertsen

Machine Learning (373024)

	Termine:	Lehrpersonen:
	Di. 12:45 - 14:15 \| I-342	Meier
Inhalt:	The term machine learning summarises a wide range of statistical methods used for pattern recognition, classification and prediction. Applications encompass the recognition of text, speech and images, spam and fraud detection, recommendation systems for customers, as well as generating information from large quantities of data or predicting stock prices. This lecture covers a selection of common supervised to unsupervised learning algorithms. These refer to clustering and regression problems, and clustering and dimensionality reduction methods respectively. Examples of covered statistical methods include: Linear and logistic regression K-nearest neighbours Naïve Bayes Model selection and cross validation Tree-based methods Support vector machines Principal component analysis Neural networks The lecture includes applications in R. Previous experience with R is helpful, but not required.
Literatur:	Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer. Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Termine:

Lehrpersonen:

Di. 12:45 - 14:15 | I-342

Meier

Inhalt:

The term machine learning summarises a wide range of statistical methods used for pattern recognition, classification and prediction. Applications encompass the recognition of text, speech and images, spam and fraud detection, recommendation systems for customers, as well as generating information from large quantities of data or predicting stock prices.

This lecture covers a selection of common supervised to unsupervised learning algorithms. These refer to clustering and regression problems, and clustering and dimensionality reduction methods respectively. Examples of covered statistical methods include:

Linear and logistic regression
K-nearest neighbours
Naïve Bayes
Model selection and cross validation
Tree-based methods
Support vector machines
Principal component analysis
Neural networks

The lecture includes applications in R. Previous experience with R is helpful, but not required.

Literatur:

Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer.
Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Mehrere Areas

Time Series Analysis (379016)

	Termine:	Lehrpersonen:
	Mi. 12:45 - 14:15 \| I-342	Sibbertsen
Inhalt:	Stationarity Autoregressive und Moving Average Models Non-Stationarity Forecasting Spectral Analysis Long Memory Time Series.
Literatur:	Hamilton, J. D. (1994): Time Series Analysis, Princeton. Schlittgen, R., Stritberg, H. J. (2003): Zeitreihenanalyse, Oldenbourg.