Implementation of quantitative methods of business administration with R

Vorstellung des Seminars © Sven Schulz & Nadine Schiebold

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Dozentin

Nadine Schiebold
Modul
 

D-WW-MG
MA-WW-MG
Umfang und Art 2 hours per week, seminar
Prüfungsleistung
 		 Assignments during the semester and a computer-based exam (90 min)
Seminar

Mi 5. DS, SCH A200a
The enrollment starts on 05.04.2024 / 9.00 a.m. at OPAL.

First lecture: 10.04.2024
Lehrmaterial The lecture notes are available at OPAL.
Inhalt der Lehrveranstaltung














 		 The course "Implementation of quantitative methods in business administration with R" is dedicated to the programming language R, an open-source software for (statistical) data analysis and graphics.

With the help of quantitative models and methods, complex real problems can be analyzed, modeled, and evaluated in a structured way in order to determine the best possible solution. The programming software R offers a flexible and user-friendly tool to collect such problem data, evaluate them or, if necessary, to perform further simulations with them.

In this course, in addition to the basics and structure of the language, particular focus will be placed on how to apply it in a scientific paper.
 

1. Introduction
  1.1 General Information
  1.2 Advantages and Disadvantages
  1.3 Installation
  1.4 Editors
  1.5 Literature

2. Grundlagen
  2.1 Basic Operators, Functions, and Values
  2.2 Objects
  2.3 Workspace
  2.4 Help System
  2.5 Logic and Missing Values
  2.6 Data Types
  2.7 Vectors
  2.8 Matrices
  2.9 Arrays
  2.10 Lists
  2.11 Data Frames
  2.12 Constructs
  2.13 Strings

3. Data Input and Output
  3.1 Text Files
  3.2 Binary Files, R Objects
  3.3 Excel Files

4. Details of the Language R
  4.1 Functions
  4.2 Lazy Evaluation
  4.3 Environments, Scoping Rules
  4.4 Installing Packages
  4.5 Loading Packages and Data

5. Efficient Programming
  5.1 Vector-valued Programming and Loops
  5.2 Programming Style
  5.3 Time Measurement

6. Extract: Statistics with R
  6.1 Sort, Order, and Rank
  6.2 Statistical Dispersion, Central Tendency, and Measures of Correlation
  6.3 Random Numbers, Distributions, and Samples
  6.4 Linear Regression
  6.5 Statistical Tests (t-Test)
  6.6 Analysis of Variance (ANOVA)

7. Graphics
  7.1 Devices
  7.2 High-level Graphics Functions
  7.3 Parameters
  7.4 Low-level Graphics Functions
  7.5 Graphics with ggplot2
  7.6 Interactive Graphics
   

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Lehrstuhl Industrielles Management
Last modified: Mar 26, 2024