Course: Statistical Methods - Marketing Applications

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Course title Statistical Methods - Marketing Applications
Course code KMI/YSTMA
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 5
Language of instruction English
Status of course unspecified
Form of instruction unspecified
Work placements unspecified
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • Rost Michael, doc. Ing. Ph.D.
Course content
Lectures: 1 - Introduction, sources of economic data, statistical software used for analysis, 2 - some aspect of inductive statistical methods 3 - the programming environment R, data imports to R 4 - non-parametric tests, 5 - specific diagnostics tests 6 - some normality tests and how to carry them out in R 7 - introduction into categorical data analysis, 8 - visualisation of categorical data 9 - introduction to multivariate data analysis, matrix algebra, the multivariate t-test 10 - distance 11 - hierarchical cluster analysis 12 - non-hierarchical data analysis 13 - logistic regression Seminars: 1 - Introduction, sources of economic data, statistical software used for analysis, 2 - some aspect of inductive statistical methods 3 - The programming environment R, data imports to R 4 - two way analysis of variance, how to carry out such types of analysis in R 5 - non-parametric tests, 6 - some normality tests and how to carry them out in R 7 - introduction to categorical data analysis, 8 - visualization of categorical data 9 - introduction to multivariate data analysis, matrix algebra, the multivariate t-test 10 - distance 11 - hierarchical cluster analysis 12 - non-hierarchical data analysis 13 - logistic regression

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming)
  • Class attendance - 56 hours per semester
  • Preparation for credit - 20 hours per semester
  • Preparation for exam - 40 hours per semester
  • Preparation for classes - 24 hours per semester
Learning outcomes
The aim of the course is to introduce students to some advanced statistical methods applicable in marketing research. The course will cover specific diagnostic methods, formal methods for data checking, explorative methods, analysis of categorical data - contingency tables, tests for independence in contingency tables, cluster analysis and logistic regression. Part of the course will be focused on multivariate statistical methods.
Students will understand the basic principles of advanced statistical methods. Students are able to communicate with the programming environment R.
Prerequisites
The course has no prerequisities.

Assessment methods and criteria
Combined exam, Test

Credit Requirements: Seminars are mainly oriented toward unsupported individual work with the teacher. Seminars are tightly linked to the content of lectures. The main part of the work in seminars could be described as "problem solving". Themes of seminar tasks correspond with explained topics in lectures. For more details see the summary of the subject. Students work with the programming environment R during the seminars. Students obtain credit on the base of the submission of one seminar work, attendance and passing the test. In some cases individual work could be assigned. Examination Requirements: To pass written part of exam you have to solve absolute majority of problems.
Recommended literature
  • Dalgaard P. Introductory Statistics with R. Springer, 2002. ISBN 0-387-95475-9.
  • Everitt B. S. An R and S-Plus Companion to Multivariate Analysis. Springer, 2005. ISBN 1-85233-882-2.
  • Faraway, J. Linear Models with R. Boca Raton : Chapman & Hall/CRC, FL, 2004. ISBN 1-584-88425-8.
  • Fox J. An R and S-Plus Companion to Applied Regression. USA: Sage Publications, Thousand Oaks, CA, 2002. ISBN 0-761-92279-2.
  • Heiberger, R. M., Holland, B. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer, 2004. ISBN 0-387-40270-5.
  • Maindonald, J., Braun, J. Data Analysis and Graphics Using R. Cambridge : Cambridge University Press, 2003. ISBN 0-521-81336-0.
  • Simonoff, J.S. Analyzing Categorical Data. New York : Springer, 2003. ISBN 0-387-00749-0.
  • Venables, W., N., Ripley, B.D. Modern Applied Statistics with S. New York : 4th ed, 2002. ISBN 0-387-95457-0.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester