t.BA.WI.WAST1.19HS (Probability and Statistics 1) 
Module: Probability and Statistics 1
This information was generated on: 24 April 2024
No.
t.BA.WI.WAST1.19HS
Title
Probability and Statistics 1
Organised by
T IDP
Credits
4

Description

Version: 2.0 start 01 February 2019
 

Short description

 The Probability and Statistics 1 module introduces students to the basics of descriptive statistics. In this module, they learn to perform descriptive data analyses, which includes preparing, visualizing and describing the data with key figures using the statistical software R.

Module coordinator

 Hofer Christoph (hofc)
 

Learning objectives (competencies)
Objectives Competences Taxonomy levels
Students develop an understanding of the purpose of a statistical investigation. F, M K1, K2
Students are able to determine meaningful key figures from a given data set and create appropriate, univariate, bivariate and multivarite desired graphs with the help of the statistic software R. F, M K2, K3
Students are able to independently conduct a descriptive analysis of a given dataset. F, M K3, K4
Students are able to read, understand and evaluate graphical data analyses conducted by third parties. F, M K2, K3, K4

Module contents

 The students learn descriptive statistics methods to visualize and describe data with statistical key figures.
 
The lessons are divided into the following blocks:
  • Basic concepts of data collection
  • Data types
  • Statistical key figures and graphical representation for univariate data (e.g. location and dispersion parameters, bar chart, histogram, empirical cumulative distribution function, box plot, ...)
  • Statistical key figures and graphical representation for bivariate and multivariate data (e.g. crosstabs, scatter plots, correlation, comparative box plots or bar charts for grouped data)
  • Linear and monotonic data transformations
 
The lab is divided into the following blocks:
  • Introduction to the statistical software R and the development environment RStudio
  • Data structures in R
  • Import and export of data
  • Introduction to R Graphics
  • Functions in R
  • Data preparation in R
  • Alternatives to classic R graphics
  • Reproducible and dynamically customizable descriptive data analysis
  • Introduction to the development of web-based analysis and evaluation tools with R

Teaching materials

 lecture notes, introduction to the statistical software R notes, presentation documents, exercises

Supplementary literature

 Fahrmeir, L., Künstler, R., Pigeot, I., Tutz, G. (1997). Statistik. Der Weg zur Datenanalyse, Springer.

Stahel, W. (2008). Statistische Datenanalyse. Eine Einführung für Naturwissenschaftler, 5. Auflage, Vieweg, Wiesbaden.

Wollschläger, D. (2). Grundlagen der Datenanalyse mit R, 4. Auflage, Springer
 

Prerequisites

  

Teaching language

 (X) German ( ) English

Part of International Profile

 ( ) Yes (X) No

Module structure

 Type 3a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025

Exams
Description Type Form Scope Grade Weighting
Graded assignments during teaching semester see module description        
End-of-semester exam exam written see module description mark see module description

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.WAST1.19HS.P
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.
Course: Modeling and Simulation of Transport Systems - Vorlesung
No.
t.BA.WI.WAST1.19HS.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.