Mathematical Statistics II 2400-IiE3STM2
Introduction to the subject and the R program
Descriptive statistics in R - measures of position, diversity and asymmetry
Parametric and non-parametric tests
The first evaluation work
Introductory issues: the essence and scope of statistical multivariate analysis, basic methods of multivariate analysis, the essence of multivariate observations, the variance-covariance matrix, correlation matrix, correlation coefficients (Pearson, Spearman rank, Kendal tau-B, Gamma), partial correlation,
Analysis of variance and covariance
Principal component analysis: the essence and purpose of the principal components analysis, the Hotelling method of determining the main components, geometric interpretation
Principal component analysis: stages of analysis, interpretation of results, examples of applications
The second evaluation work
Cluster analysis: essence, application areas and goals of cluster analysis, grouping types (by division, by hierarchy), measures of similarity and diversity of objects, metrics, normalization procedures of variables
Division grouping: the idea of partition grouping, iterative transfer methods, the k-means method
Linear ordering: the essence, the pattern method
The third evaluation work
Type of course
Prerequisites (description)
Course coordinators
Mode
Learning outcomes
A) Knowledge
The student has a basic knowledge of the place of multivariate statistics in the system of social and economic sciences.
Student understands the use multivariate statistics in business practice
The student knows and understands the limitations of methods used in multivariate analysis
The student knows and understands the need for basic tools to reduce the dimension of multivariate data, i.e. analysis of the principal components
The student understands the goals and ways of classifying multivariate objects
The student understands the idea of clustering
The student has knowledge about linear ordering of multivariate objects, knows the standard method
The student has knowledge about the methods of data acquisition and processing, as well as related limitations
S1A_W01, S1A_W04, S1A_W06, S1A_W10
B) Skills
The student can use the acquired basic theoretical knowledge and obtain data to analyze specific social processes and phenomena
The student is able to interpret the results obtained and draw conclusions based on them
The student is able to use the known methods of multivariate statistics to analyze the basic socio-economic problems
S1A_U01, S1A_U02, S1A_U07, S1A_U08, S1A_U10
C) Social competences
The student understands the need to learn throughout life
The student is able to interact and work in a group
The student is able to properly determine the priorities for the implementation of a task set by himself or others
The student is able to supplement and improve acquired knowledge and skills
S1A_K01, S1A_K02, S1A_K03, S1A_K06
Assessment criteria
Completion of the course takes place on the basis of a written exam, which is a prerequisite for receiving the final positive grade and constitutes 70% of the grade. In addition, 30% of the final grade is three activity during classes (performing tasks on computers).Two absences are allowed.
Bibliography
Andrzej Balicki, Statystyczna analiza wielowymiarowa i jej zastosowania społeczno-ekonomiczne, Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk 2009
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: