Course code MateD001

Credit points 3

Multivariate Data Analysis II

Total Hours in Course81

Number of hours for lectures16

Number of hours for seminars and practical classes16

Independent study hours49

Date of course confirmation04.09.2019

Responsible UnitInstitute of Computer Systems and Data Science

Course developers

author prof.

Līga Paura

Dr. agr.

author prof.

Irina Arhipova

Dr. sc. ing.

Prior knowledge

MateD005, Multivariate Data Analysis I

Course abstract

PhD student acquire multivariate data analyses methods, as also check the model assumptions violations. The study course is oriented to multivariate methods choice acquisition and methods comparison. During the studies the real examples related to biology, agriculture and other sciences are using. Statistical software is provides for problem tasks solving. The course topics are the following: analyse of variance (ANOVA), analyse of covariance (ANCOVA), principal component analyse (PCA), factor analyse, cluster analyse, two group discriminate analyse, multivariate analyse of variance.

Learning outcomes and their assessment

After completing the course Ph.D. student will have:
• knowledge about multivariate data analysis methods for emerging scientific theories and its application for PhD research on professional fields (during the practical classes the necessary multivariate methods for testing the hypothesis of the doctoral thesis are analyzed);
• skills individually evaluate and select multivariate data analysis methods for scientific researches of important, original and international cited researches (during the practical classes the necessary multivariate methods for the data analysis of the doctoral thesis have been selected);
• competences in collaboration with PhD supervisor individually perform critical analysis and evaluation. Solve important scientific or innovative tasks and individually propose research ideas (independent work has been developed and during the practical classes defended).

Course Content(Calendar)

1.Regression analysis [L/P - 2h].
2.Multiple regression analysis [L/P - 2h].
3.Regression analysis and Analysis of Variance (ANOVA) [L/P - 2h].
4.Functional forms of regression analysis [L/P - 2h].
5.Assumptions of the regression model [L/P - 2h].
6.Analysis of covariance (ANCOVA) [L/P - 2h].
7.Analysis of covariance and regression analysis [L/P - 2h].
8.Two-group discriminant analysis [L/P - 2h].
9.Multiple discriminant analysis [L/P - 2h].
10.Discriminant analysis and Analysis of Variance [L - 2h].
11.Multivariate analysis of variance (MANOVA) [L/P - 2h].
12.Methodology of Factor analysis [L - 2h].
13.Factor analysis and Principal component analysis [P - 2h].
14.Cluster analysis: Hierarchical clustering [L/P - 2h].
15.Cluster analysis: Non-Hierarchical clustering [L/P - 2h].
16.Defending of independent work [P - 2h].

Requirements for awarding credit points

Independent work have been developed and defended. At least three different statistical methods for the real data analysis should be used.

Description of the organization and tasks of students’ independent work

The organization of independent work during the semester is independently studying literature, using academic staff member consultations.

Criteria for Evaluating Learning Outcomes

The assessment of learning outcomes depends on the degree of development of the independent work. To obtain the minimal assessment it is necessary to formulate and test the hypotheses using at least 3 statistical methods, based on the data of the doctoral thesis: Analysis of variance, Regression analysis, Analysis of covariance, Discriminant analysis, Multivariate analysis of variance, Factor analysis or Cluster analysis.

Compulsory reading

1. Are Hugo Pripp. (2013) Statistics in food science and nutrition. New York : Springer, 66 pp.
2. François Chollet & J.J. Allaire (2018) Deep learning with R. Shelter Island, NY: Manning Publications Co., 335 pp.
3. James Gareth, at al. (2017) An introduction to statistical learning: with applications in R. New York : Springer, 426 pp.
4. Joseph F. Hair, Jr., William C. Black, Barry J. Babin, Rolph E. Anderson (2014) Multivariate data analysis. Harlow, Essex: Pearson, 734 pp.
5. Robert I. Kabacoff (2015) R in action: data analysis and graphics with R. Shelter Island, NY: Manning, 579 pp.

6. Siegmund Brandt. (2014) Data analysis: statistical and computational methods for scientists and engineer. Springer, 523 pp.

Further reading

1. John H. Schuenemeyer, Lawrence J. Drew. (2011). Statistics for Earth and Environmental Scientists. Hoboken, New Jersey: John Wiley & Sons, 407 pp.
2. Joseph F. Hair [et al.] (2010) Multivariate data analysis: a global perspective. Upper Saddle River [etc.]: Pearson, 800 pp.
3. Klaus Backhaus, Bernd Erichson, Wulff Plinke, Rolf Weiber (2000) Multivariate Analysemethoden: eine anwendungsorientierte Einführung. Berlin etc.: Springer, 661 pp.
4. Massimiliano Bonamente. (2013) Statistics and analysis of scientific data. New York: Springer, 301 pp.

5. Nathabandu T. Kottegoda, Renzo Rosso (2008) Applied statistics for civil and environmental engineers. Oxford; Malden, MA: Blackwell Publishing, 718 pp.

Periodicals and other sources

1. Statistical Analysis and Data Mining.Hoboken, Wiley-Blackwell. ISSN:1932-1864. E-ISSN:1932-1872

2. Electronic Journal of Applied Statistical Analysis. ESE - Salento University Publishing. ISSN:2070-5948


Elective course for all LLU doctoral programs.