t.BA.WI.WAST3.19HS (Probability and Statistics 3) 
Module: Probability and Statistics 3
This information was generated on: 20 April 2024
No.
t.BA.WI.WAST3.19HS
Title
Probability and Statistics 3
Organised by
T IDP
Credits
4

Description

Version: 2.0 start 01 August 2020
 

Short description

 Probability and Statistics 3 introduces students to the fundamentals of statistical inference, i.e. techniques that permit inferences to be drawn about a population from a sample. Special emphasis is placed on computational methods that enable the theoretical concepts to be applied in practice.

Module coordinator

 Hofer Christoph (hofc)

Learning objectives (competencies)
Objectives Competences Taxonomy levels
Students know and understand the fundamental concepts of applied statistical inference. F, M K1, K2
Students know different approaches to point and interval estimation and can apply these to a sample to make inferences about unknown population parameters and assess statistical accuracy of the estimates. F, M K2, K3
Students know and understand fundamental concepts of statistical hypothesis tests and are able to apply suitable tests to practical problems. F, M K2, K3
     

Module contents

Students learn to distinguish between theoretical models and their parameters on the hand, and empirical data (samples) and quantities calculated from these on the other. Fundamental concepts and techniques for point and interval estimation and hypothesis testing are introduced. In addition to classical (analytical) solutions, the module emphasises modern computational techniques (numerical techniques, resampling) that allow for the methods to be widely applied in more complicated practical situations. 

 

Sampling and estimation:

  • sampling distributions
  • statistics and estimators
  • properties of estimators 
  • different approaches to point estimation
  • confidence intervals and their properties
 

Statistical hypothesis tests

  • Basic principles of hypothesis tests (null and alternative hypotheses, errors of the first and second kind, level and power of a test)
  • Special parametric and non-parametric tests for one- and two-sample problems
 

Students learn to apply the concepts and techniques in practical exercises using the R environment for statistical computing.

Teaching materials

 Lecture notes, slides presented in class, exercises

Supplementary literature

Chihara, L.M., Hesterberg, T.C. (2019). Mathematical Statistics with Resampling and R, 2nd edition, Wiley, Hoboken.

Rice, J.A. (2007). Mathematical Statistics and Data Analysis, 3rd edition, Brooks/Cole.

Prerequisites

  

Teaching language

 (X) German ( ) English

Part of International Profile

 ( ) Yes (X) No

Module structure

 Type 2a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025

Exams
Description Type Form Scope Grade Weighting
Graded assignments during teaching semester          
End-of-semester exam exam written 90 minutes Grading 100%

Remarks

  

Legal basis

 The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.
Course: Modeling and Simulation of Transport Systems - Vorlesung
No.
t.BA.WI.WAST3.19HS.V
Title
Modeling and Simulation of Transport Systems - Vorlesung

Note

  • No module description is available in the system for the cut-off date of 01 August 2099.