## Topic outline

• Painless biostatistics

or

BIOLOGICAL DATA ANALYSIS USING R

to perform by yourself the statistical analyses of your thesis.

to be taught to PhD students at SLU-Alnarp,
20-24 june 2005

Earn 2 credits by completing the course!

• ### Topic 1

Biological hypotheses and their translation into statistical models

After this class you will be able to
• formulate scientific hypotheses with biological meaning
• include such hypotheses into a cartesian plot, by identifying which are the (dependent and independent) variables involved and what are the mathematical relationship between such variables

• ### Topic 2

R system and Moodle basics

After this class will you be able to
• access the course's site and interact with Moodle System, changing your personal profile, reading text, posting questions and exercices, getting files
• perform basic computations in R
• plot simple graphs
• read and include external data files into R enviroment
• learn how to learn R-system

Crawley, chapter 2
• ### Topic 3

Probability, variance, and how to measure them.

After this class you will be able to:
• Measure the variability of your data
Anova

After this class you will be able to:
• Measure the degree of uncertainty when stating that two sets of data belong to the same probability distribution
• ### Topic 4

Nested and Split Plot anovas

After this class you will be able to:
• Avoid running into pseudoreplication when analysing a data set composed by hierarchicaly structured experiments, whose variables come from different spatial scales.
• ### Topic 5

Regression models

After this class you will be able to:
• analyse models whose independent variable(s) is(are) quantitative (that is, x-var is continuous)
• ### Topic 6

Ancova models

After this class you will be able to:
• Analyse models composed by a quantitative and a qualitativa variable
• Interpret the biological meaning of a statistical interaction
• ### Topic 7

Statistical modelling: building, criticising and simplifying models

After this class you will be able to:
• translate biological questions into statistical models
• build different statistical models for the same set of variables
• check whether the model achieved is coerent to its underlying statistical assumptions
• propose alternative, simpler and statistically sound models which could explain the same data set
Crawley, chap 13
• ### Topic 8

Dealing with non-normal data

After this class you will be able to:
• perform a statistical analysis of a non-normal data set, without having to force normality via y-var transformations
• ### Topic 9

Count data models

After this class you will be able to:
• Analyse data whose response variable is in the form of counts (ex. number of species, number of times something happened)
• ### Topic 10

Proportion and binary data

After this class you will be able to:
• Analyse data whose response variable is a proportion (ex. percent of insects choosing left arm of a y-olfactomer)
• Analyse data whose response variable binary (ex. effect of predation on presence/absence of prey)
• ### Topic 12

Survival analysis

After this class you will be able to:
• Analyse data on deaths or failures (time to death of insects submitted to insecticide, time to final decay of a pheromone dosis)
• ### Topic 13

Experimental design

After this class you will be able to

• design an experiment assurring true replication and randomization
• spot pseudoreplication