Detailed information about the module 
Module title:
Computational Statistics with R
Module number:
Bachelor (UNI)
Organised by:
Wirtschaftswissenschaftliche Fakultät
Instructors responsible:
Küsters, Ulrich
Credit points (ECTS):
- Students acquire both baseline information and knowledge of selected high-level programming techniques by using the statistical software environment R.
- The statistical analysis of data using R enables students to appropriately treat, prepare and graphically display empirical data.
- By addressing problems in the broad field of business and economics (i.e. statistical hypothesis testing, linear regression etc.), students gain decision making skills enabling them to conduct an analysis using Rin a self-directed and aim-oriented manner.
course content/topics:
- Basics
- Objects and data structures in R und how to manage them
- Vectors
- Matrizes
- Arrays
- Lists
- Data Frames
- Logic and missing values
- Constructs for program control
- Conditional statements (if ... else and the like)
- Loops
- Character strings
- Date and time
- Data input and output
- ASCII files
- Read and write R objects
- Special file formats
- Database access
- Working with Excel-Data
- Details of the R language
- Functions
- Lazy Evaluation
- Environments and scoping
- Error management and debugging
- Graphics with R
- Statistics mit R
- Basic functions
- Random numbers
- Distributions and samples
- Linear models
- Overview: further special models
- Case study: analysis of an empirical dataset
formal requirements of admission:
recommended qualifications:
- Mathematics for Business
- Descriptive Statistics and Probability Theory
- Statistical Inference and Multivariate Statistics
Lesson / exam language:
Lehr- und Lernformen/Lehrveranstaltungstypen:
requirements for the attainment of ECTS points:
Because of the competence orientation of this course it is mandatory to combine a written exam with programming homework assignments or a programming project.
- Final exam: assessment of theoretical and technical aspects of the programming language respectively of the statistical topics covered in class.
- Homework assignments: assessment of the practical implementation of the topics covered in class.
- Programming project: programming code based solution of a given problem to assess the capability to apply programming knowledge to distinct analytical problems and illustrate the solution in a context that is broader compared to that offered by other assessment alternatives.
workload/distribution of ECTS points within the module:
42 h = Time of attendance
28 h = Preparation and post-processing
61 h = Homework assignment/ Programming project
19 h = Exam preparation
150 h = Total workload
calculation of module marks:
- Final exam (50 %)
- Homework assignments (25 %)
- Programming project (25 %)
teaching/learning method:
- Integrated Lecture and Exercise
- Programming projects
compatibility with other courses of study:
Turnus des Angebots:
Beteiligte Fachgebiete:
*Beschränkung aufgrund bestehender Kapazitäten in den PC Pools (gesondertes Zulassungsverfahren).