# Statistics (dt. Statistik)

 Level, degree of commitment Advanced module, compulsory elective module Forms of teaching and learning,workload Lecture (4 SWS), recitation class (2 SWS), 270 hours (90 h attendance, 180 h private study) Credit points,formal requirements 9 CP Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises. Examination type: Written or oral examination (individual examination) Language,Grading German/English,The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Business Mathematics. Duration,frequency One semester, Regularly alternating with other advanced modules Person in charge of the module's outline Prof. Dr. Hajo Holzmann

## Contents

• Fundamentals of statistics: statistical models, estimating, testing, confidence intervals
• the multivariate normal distribution
• Statistics in the linear model
• Exponential families and generalized linear models
• Unbiased estimation and the Cramer - Rao bound
• Bayes estimators, shrinkage estimators and thresholding
• Fundamentals of Classification
• Elements of high-dimensional statistics

## Qualification Goals

Students will

• know important statistical procedures and can analyze them mathematically,
• can apply the procedures to data sets using the statistical software R,
• Have further developed their understanding of data analysis and statistics,
• have improved their oral communication skills in exercises by practicing free speech in front of an audience and in discussion.

## Prerequisites

None. The competences taught in the following modules are recommended: Elementary Probability and Statistics, Internship Stochastics.

## Applicability

Module imported from B.Sc. Business Mathematics.

It can be attended at FB12 in study program(s)

• B.Sc. Data Science
• B.Sc. Mathematics
• M.Sc. Data Science
• M.Sc. Computer Science
• M.Sc. Mathematics
• LAaG Mathematics

When studying M.Sc. Computer Science, this module can be attended in the study area Profile Area Mathematics.

• Trabs, M., Jirak, M., Krenz, K., Reiß, M., "Statistik und maschinelles Lernen", Springer 2020
• Richter, S. "Statistisches und
• maschinelles Lernen. Gängige Verfahren im Überblick", Springer, 2019
• Wasserman, L. "All of Statistics", Springer, 2003