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Multivariate Biodata Analysis

Course developer

Course abstract

The aim of this course is to provide students with multivariate statistical methods, whereas most software in biology and agricultural science are based on statistical models. Statistical methods will be illustrated with biology and agriculture data examples. The course subjects are analytics methods of data classification and its using for data analysis.

Learning outcomes and their assessment

• Knowledge depth knowledge and critical understanding about parametric and nonparametric data analysis methods; choose and apply methods according to research task;
• skills able to discuss about principles of choice the methods and their application and implementation of the specific problem at study; to use R and SPSS software for data analysis;
• competence to realize data analysis in master work by using a data processing application software; to interpret the results and draw conclusions: to base decisions and to analyze them.

Compulsory reading

1. Dalgaard P. Introductory statistics with R. New York [etc.]: Springer, 2002. 267 p.
2. Engineering statistics handbook [tiešsaiste]. Pieejams:. http://www.itl.nist.gov/div898/handbook/index.htm [Skatīts 18.janvārī 2012.]
3. Krastiņš O., Ciemiņa I. Statistika: mācību grāmata. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp.
4. Zar J. H. Biostatistical Analysis. New Jersey: Prentice Hall, 1999. 591 p.

Further reading

1. An Introduction to R [tiešsaiste]. Pieejams: http://cran.r-project.org/doc/manuals/R-intro.html [skatīts 18.janvārī 2012.]
2. Levine D. M., Ramsey P. P., Smitd Smidt R. K. Applied statistics for Engineers and Scientist: Using Microsoft Excel and MINITAB. Upper Saddle River, New Jersey: Prentice Hall, 2001. 671 p.
3. Sprent P., Smeeton N.C. Applied nonparametric statistical methods. Boca Raton, London, New York, Washington D.C.: Chapman Hall/CRC, 2001. 461 p.