USING LINEAR AND MIXED MODELS FOR ANALYSIS OF BIOLOGICAL DATA

Course ID: BBI-SE>MODLINADB
Course title: USING LINEAR AND MIXED MODELS FOR ANALYSIS OF BIOLOGICAL DATA
Semester: Winter
ECTS: 2
Lectures/Classes: 15 / 15 hours
Field of study: Bioinformatics
Study cycle: 1st cycle
Type of course: optional
Prerequisites:
Contact person: dr Tomasz Suchocki tomasz.suchocki@upwr.edu.pl
Short description: The student after the course will know theoretical and practical aspects of modelling bioilogical data using linear and mixed models with special emphasis on analysing real data sets in statistical package R.
Full description: Introduction to R package, linear models, analysis of variance, AIC, BIC and mBIC criterions, stepwise regression, linear mixed models, testing of significance, LRT test, estimating variance parameters in mixed model.
Bibliography: Crawley M. J. (2007). The R Book. Wiley.
Learning outcomes: Knowledge: The student knows the theoretical and practical aspects of modeling of the biological data using linear and mixed models. BI_W9, Student can apply theoretical knowledge of statistical methods to real data sets in a statistical package R. BI_W13, BI_W14, BI_W15, Skills: The student knows the computer lab equipment and the specificity and safety rules in the computer lab, - has basic knowledge of statistical modeling of biological data. BI_U01, BI_U03, BI_U07, The student can perform statistical analysis of real data set in statistical package R. BI_U03, BI_U04, BI_U05, BI_U07, BI_U12, Social competences: The student understands the analysed biological phenomenon – can work in a team. BI_K02, BI_K07,The student is responsible for the computer equipment – knows the BHP rules in computer lab. BI_K07,
Assessment methods and assessment criteria: Exercises: Rating based on two separate projects made in working groups. The presence of the exercises is compulsory, the student may have only one unauthorized absence. In the case of excused absences the student is required to pass the relevant part of the material. Completion of exercises based on the average ratings. Lecture: Based on rating from exercises.

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