Multivariate Statistical Analysis
Course code
IFI6257.DT
old course code
Course title in Estonian
Mitmemõõtmeline statistiline analüüs
Course title in English
Multivariate Statistical Analysis
ECTS credits
6.0
Assessment form
Examination
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
Create opportunities for conducting significance tests in inferential statistics, as well as for calculating and interpreting multivariate models.
Brief description of the course
Principles and use of significance tests in data analysis. Parametric and non-parametric analysis methods. Correlation analysis. Linear and logistic regression analysis. Cluster analysis, factor analysis. Multidimensional scaling.
Learning outcomes in the course
Upon completing the course the student:
- formulates analysis questions that require statistical generalization based on data;
- uses data analysis software to perform analysis based on inferential statistical methods;
- interprets the results of inferential statistical analysis and presents them appropriately in the written report of the research work;
- calculates the appropriate correlation coefficient based on the data and interprets the results;
- Creates various regression analysis models, critically evaluates their content, and interprets the results;
- understands methods for aggregating different variables/data and applies them according to the data and objectives. Evaluates the suitability of the results and interprets them;
- applies acquired knowledge and skills in practical situations, providing solutions to problems based on data-driven decision making, respecting data protection rules.
- formulates analysis questions that require statistical generalization based on data;
- uses data analysis software to perform analysis based on inferential statistical methods;
- interprets the results of inferential statistical analysis and presents them appropriately in the written report of the research work;
- calculates the appropriate correlation coefficient based on the data and interprets the results;
- Creates various regression analysis models, critically evaluates their content, and interprets the results;
- understands methods for aggregating different variables/data and applies them according to the data and objectives. Evaluates the suitability of the results and interprets them;
- applies acquired knowledge and skills in practical situations, providing solutions to problems based on data-driven decision making, respecting data protection rules.
Teacher
Kairi Osula
Prerequisite course 1
Study programmes containing that course