This course is an introduction to multivariate statistical analysis. The goal is to learn basics of common multivariate data analysis techniques and to use the methods in practice. Software R is used in the exercises of this course. The topics of the course are multivariate location and scatter, principal component analysis, bivariate correspondence analysis, multiple correspondence analysis, canonical correlation analysis, discriminant analysis, classification, and clustering.
Note that all the lectures and exercise classes are given on campus. Remote attendance is not possible.
Before the course starts, make sure that you know how to calculate the univariate means, medians, variances, and max and min values. Familiarize yourself with the correlation coefficients and common graphical presentations (boxplots, scatter plots, histograms, bar plots, pie charts) of data. Learn to calculate the multivariate mean vector and covariance matrix. Make sure that you know what is a cumulative distribution function, a probability density function, and a probability mass function. Make sure that you know what is the expected value of a random variable. Read about univariate and multivariate normal distributions and elliptical distributions. Make sure that you know what is meant by central symmetric distributions and skew distributions. Recall what are the determinant, eigenvectors and eigenvalues of a matrix and make sure that you know what is meant by a symmetric matrix and a positive definite matrix.
How to pass this course?
You are expected to:
-Attend the lectures and be active - not compulsory, no points, but highly recommended.
-Submit your project work on time - THIS IS COMPULSORY - max 6 points.
-Take the exam - max 24 points.
-Participate to weekly exercises (group 1, group 2, group 3 OR group 4) - not compulsory, but highly recommended - max 3 points.
-Be ready to present your homework solutions in the exercise group - not compulsory, but highly recommended - max 3 points.
Max total points = 6 + 24 + 3 + 3 = 36. You need at least 16 points in order to pass the course.
How to get a good grade?
-Attend the lectures and be active!
-Work hard on your project work.
-Be active in the exercises!
-Study for the exam!
Grading is based on the total points as follows: 16p -> 1, 20p -> 2, 24p -> 3, 28p -> 4, 32p -> 5.