Studienhandbuch | VL Statistical Inference

Inhalt

^(*) Unfortunately this information is not available in english.

Workload	Education level	Study areas	Responsible person	Hours per week	Coordinating university
5 ECTS	B2 - Bachelor's programme 2. year	Statistics	Werner Müller	4 hpw	Johannes Kepler University Linz

Detailed information

Pre-requisites

^(*)keine

Original study plan

Bachelor's programme Statistics and Data Science 2025W

Learning Outcomes

Competences
The courses VL and UE Statistische Inferenz form an inseparable didactic unit. The learning outcomes described below are achieved through the interaction of the two courses: Students are able to mathematically justify and possibly improve methods of statistical inference.

Skills	Knowledge
Knowing and understanding of the basic problems, concepts and procedures of statistical inference (k1,k2) Classify the concepts in their historical and contemporary meaning (k1) Derive mathematical facts and theorems (k3,k4,k5) Use of the Mathematica program package (k3)	Properties of a random sample Order statistics Principles of data reduction (sufficiency, likelihood, equivariance) Point estimation Hypothesis testing Interval estimators Statistical methods in Mathematica

Criteria for evaluation

Exam

Methods

Lecture

Language

German

Study material

G. Casella and R.L. Berger, Statistical Inference. Robert Hafner, Wahrscheinlichkeitsrechnung und Statistik.

Changing subject?

Corresponding lecture

^(*)4MSMS1V: VL Mathematische Statistik I (8 ECTS)

On-site course
Maximum number of participants	100
Assignment procedure	Assignment according to priority