## Course description

The aim of this course is for students to gain a basic understanding of statistical and probability theory, to be introduced to the most commonly-used statistical methods within the biological sciences, to understand the assumptions associated with each statistical test, to gain practical experience in using these tests within either SPSS or R, and to learn how to choose the appropriate test for a given dataset.

## Lectures and exercises

Lecture 1: Introduction

Exercise 1: Introduction to R and SPSS

Lecture 2: Binomial and poisson distributions

Exercise 2: Merging and aggregating data

Lecture 3: Normal distribution and estimation

Exercise 3: Distributions, SE and SD, making simple figures

Lecture 4: Hypothesis testing and power analysis

Exercise 4: Two-sample tests, checking for normality

Lecture 5: Regression and correlation

Exercise 5: Correlation, linear regression, and model II regression

Lecture 6: Multiple regression

Exercise 6: Multiple regression, partial correlation, model selection, and non-linear regression

Lecture 7: One-way ANOVA

Exercise 7: One-way ANOVA, planned comparisons, and post-hoc tests

Lecture 8: Multifactor ANOVA

Exercise 8: Two-way ANOVA, fixed versus mixed models, nested effects

Lecture 9: Block designs and repeated measures

Exercise 9: Randomized block designs, repeated measures, and ANCOVA

Lecture 10: Analyzing frequencies

Exercise 10: Frequency data, pseudoreplication

Lecture 11: Logistic regression and GLM

Exercise 11: Log-linear models and logistic regression

Lecture 12: Multivariate analysis

Exercise 12: Principal components, discriminant function, and cluster analysis

Lecture 13: Survival analysis

Exercise 13: Survival analysis and Cox regression

**Exam ht 2016** — will be posted on Live@Lund for BIOS14 students. Sent by email to PhD students.

## Other resources

A short guide to running ANOVA:s in SPSS.

A basic introduction to R.

Materials from the introductory R lectures.

Index of R commands used in exercises