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Module name (EN):
Name of module in study programme. It should be precise and clear.
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
International Tourism-Management, Bachelor, ASPO 01.10.2017
Module code: BITM-230
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4V (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
Semester: 2
Mandatory course: yes
Language of instruction:
Written exam

[updated 21.03.2018]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

BITM-230 (P420-0452) International Tourism-Management, Bachelor, ASPO 01.10.2013 , semester 2, mandatory course
BITM-230 (P420-0452) International Tourism-Management, Bachelor, ASPO 01.10.2015 , semester 2, mandatory course
BITM-230 (P420-0452) International Tourism-Management, Bachelor, ASPO 01.10.2017 , semester 2, mandatory course
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Teresa Melo
Lecturer: Prof. Dr. Teresa Melo

[updated 26.06.2017]
Learning outcomes:
After successfully completing this course students will be able to:
- describe basic economic concepts of descriptive statistics for
  univariate and bivariate data analysis,
- apply concepts for the graphical presentation of empirical data,
- select suitable methods for statistical data analysis and
  apply them independently to specific subjects,
- identify correlations and dependencies between
  statistical features,
- describe and model random phenomena using concepts from the probability theory,
- describe basic methods from the probability theory
  and apply them to exemplary economic situations,
- apply the most important discrete and continuous probability distributions
  (e. g. binomial and normal distributions),
- explain basic procedures of inferential statistics such as the principle of point
  and interval estimators,
- solve business practice problems with the help of adequate
  statistical methods and interpret the results obtained,
- understand possible applications in other fields of business studies and their practice,
- Students will know the limits of the statistical methodology used and be able to
  discuss them critically.

[updated 17.09.2018]
Module content:
Descriptive statistics:
- Classification of features
- Frequency tables for classified and nonclassified data
- Graphical representation of univariate data sets
- Description of univariate datasets using measures of location, dispersion and
- Bivariate data analysis: graphical representation of data sets and  
  investigation of the relationship of statistical characteristics (contingency, correlation,
  rank correlation)
- Linear regression
- Statistic software (for example: SPSS)
Probability calculation:
- Probability terms: Laplace distribution, statistical
  probability, Kolmogorov´s probability theory
- Elementary calculation rules, total probability theorem, Bayesian theorem
- Discrete and continuous random variables
- Special distribution models (e.g. binomial and normal distribution)
Inferential statistics:
- Point and interval estimations

[updated 21.03.2018]
Teaching methods/Media:
Lecture and discussion in a large group using transparencies (projectors) and the blackboard (theory and example calculations).
The lecture will be supplemented by exercises and tutorials. In order to support independent work, a large number of exercise sheets covering the wide range topics in this module will be provided. Afterwards, the solutions will be discussed with the students.
Both the lecture notes and the exercise sheets will be available to students in electronic form.
Case studies from the field of tourism, such as for example: competitor analyses, destination research and guest surveys

[updated 21.03.2018]
Recommended or required reading:
Caputo, Fahrmeir, Künstler, Lang, Pigeot-Kübler, Tutz: Arbeitsbuch Statistik, 5. Auflage, Springer, Berlin, 2009
Fahrmeir, Künstler, Pigeot, Tutz: Statistik: Der Weg zur Datenanalyse, 7. überarbeitete Auflage, Springer, Berlin, Heidelberg, 2010
Schira: Statistische Methoden der VWL und BWL: Theorie und Praxis, 3., aktualisierte Auflage, Pearson Studium, 2009
Toutenburg, Heumann: Arbeitsbuch zur deskriptiven und induktiven Statistik, 2. Auflage, Springer, Berlin, Heidelberg, 2009
Toutenburg, Heumann: Descriptive statistics: Eine Einführung in Methoden und Anwendungen mit R und SPSS, 7. aktual. u. erw. Auflage, Springer, Berlin, Heidelberg, 2009
Toutenburg, Heumann: Induktive Statistik: Eine Einführung mit R und SPSS, 4. überab. und erw. Auflage, Springer, Berlin, Heidelberg, 2008
English literature:
Bowerman, O´Connell, Murphree: Business Statistics in Practice, 6th edition, McGraw-Hill/Irvin, 2011
OpenStax College, Introductory Statistics, Rice University, Houston, Texas, U.S., 2013
Sweeney, Williams, Anderson: Fundamentals of Business Statistics, 6th edition, Cengage Learning Emea, 2011

[updated 21.03.2018]
[Tue Jul 16 10:56:36 CEST 2024, CKEY=isx, BKEY=itm4, CID=BITM-230, LANGUAGE=en, DATE=16.07.2024]