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.
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