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.

Course schedule

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