3613 Multivariate Data Analysis , 8 sp
The course is an introduction to multivariate analysis (i.e. statistical techniques that simultaneously analyse multiple measurements on individuals or objects). The course covers techniques such as MANOVA, Principal Component Analysis, Factor Analysis, Discriminant Analysis, Logistic Regression, and Cluster Analysis. Statistical program is used in the course.
The course is built on the concept of cooperative learning in small teams. You will get a joint grade for the overall performance of your team.
Students with Statistics as their minor are required to include this course in their bachelor's studies. This course can also be taken as an elective course at the bachelor's level.
You are familiar with the features of multivariate data, can explain the logic behind multivariate analysis and have a working knowledge of the multivariate techniques.
- examine and prepare data for multivariate analysis
- apply multivariate methods to well-defined research questions and can carry out multivariate data analysis using statistical software
- interpret and utilize the analysis results
- dissect and evaluate research reports where multivariate analyses are applied
Examples involving problem-solving in an international context are employed.
The Hanken course 7777 FUM or 7778 Statistisk analys completed or similar skills in hypothesis testing.
ANOVA and regression analysis are recommended.
Lectures & computer labs.
Scheduled (contact hours): 42h
Reading about the concepts covered during the lectures, analyzing examples, interpreting solutions, watching videos, producing analyses, 86h
Preparing solutions of the HW assignments, 50h
Organizing, combining and relating the concepts in the run-up to the final exam, 36h
Final exam 40% (open book)
Homework assignments 60%
- Hair, J. F., Black, W.C, Babin, B. J. & Anderson, R. E. (2018). Multivariate data analysis: (a global perspective (subtitle in earlier versions)). 8th ed. or earlier. Upper Saddle River (N.J.): Prentice Hall.
- James, G., Witten D., Hastie, T. & Tibshirani, R. (2017). An Introduction to Statistical Learning. 7th ed. New York: Springer.
Selected chapters as specified by the instructor.
Student who have completed the 5 ECTS-version of MDA (course code 3677) cannot take this 8 ECTS version.
Open university quota: 3
Quota for JOO-students: 3