Bio_Stats_Course_UCA

Biological and Ecological Statistics using R - A Course of the UCA Biology Department

PLEASE NOTE THAT I AM REVISING THE .MD FILES AND FOR NOW, ONLY PDFs OF THE LECTURES ARE AVAILABLE

Experimental Design and Analysis in R

###Taught by: Maria Paniw, PhD student in Departamento de Biología (Área de Botánica), CASEM ###Taught for: Undergraduate, graduate, and postgraduate students/researchers in biology/ecology

###Course description: Experimental design is essential in all natural sciences. For decades, simple ANOVA designs have dominated literature. However, with advances in statistical applications, more complex, hierarchical designs are increasingly applied to separate treatment effects from noise. One statistical application that facilitates the analysis of experimental data is R and in this course, you will learn how to use several statistical packages in R to this goal. This is a hands-on applied course, where students will apply several basic statistical frameworks in R by working though examples, either using their own or simulated data.

###At the end of the course students should:

• Understand basic experiment design approaches
• Get a good idea of ANOVA and linear regression in R
• Become familiar with mixed effect models

###Prerequisites: A working knowledge of R.

###Syllabus:

• Overview of experimental designs

• Factorial or ANOVA designs

• Block and hierarchical designs

• Analysing factorial designs: ANOVA, linear regression, and ANCOVA

• A framework for factorial analysis: generalized linear models (GLM)

• Binomial models

• Poisson models

• Analysing block and hierarchical designs: linear mixed models (LMER)

• Plots as random effects

• Repeated-measure analysis

• Generalized linear mixed models (GLMER)

• Examples with nested data