WIAS Course Statistics for the Life Sciences

Course schedule

Dates Start time End time Location Coordinator registrations
27-29 May + 3-5 June 2019 Wageningen Campus, Room t.b.d. Marianne Bruining 13 / 24 Registration ended at 13/05/2019

Course description

Course description

This course focuses on modern statistical techniques and models to analyse data obtained in experimental and observational studies in the Life Sciences. Many examples will be discussed, and, if time allows, discussion of own data is possible to some extent. A broad view on statistical data-analysis is presented, followed by an in-depth discussion of linear models (regression, AN(C)OVA) for normally distributed responses, generalized linear models (including maximum likelihood) for non-normal responses, mixed models for dependent responses, and an introduction into Bayesian statistics, allowing prior information about parameters to be included in the statistical analysis. The main statistical software used during the course is SAS.

Learning goals

Do’s and don’ts in statistical data analysis.

Concepts and methods in:

  • linear models: regression analysis, ANOVA and ANCOVA
  • generalized linear models and maximum likelihood
  • mixed linear models: analysis of dependent data
  • Bayesian statistics

For a more elaborate description of topics, see below under programma per day.

Target group / group size

PhD candidates / 10 to 24 participants

Prior knowledge required

Prior knowledge is required. Some of the topics mentioned below will be refreshed during the course. Topics:

  • Descriptive statistics: mean, standard deviation, variance, correlation; boxplot, histogram, scatterplot
  • Basic probability theory: normal, binomial distribution
  • Inferential statistics: estimation, confidence interval, hypothesis testing
  • T-tests: one-sample t-test, 2 sample t-test, paired t-test
  • Regression: simple regression: model, least-squares, ANOVA table, t-tests for slopes, F-test for regression
  • ANOVA: oneway and twoway, parameters, ANOVA table, F-tests, interaction
  • Analysis of counts: chi-square test for goodness of fit and for analysis of contingency tables

Other course info

The course duration is 6 days, and the course will be given once a year in spring. Home work is not compulsory, but may be advised to keep on track. Credits: 1.5 ECTS

Course programme

Daily program: 9.00-17.00 h; breaks 10.45-11.00 h, 12.30-13.30 h and 15.15-15.30 h.


Day 1: Introduction to data-analysis

  • using SAS, and  including a case study

Day 2+3: Linear Models + Maximum Likelihood (ML)

  • general structure and assumptions

    Special attention to:

  • main effects and interaction in ANOVA
  • interpretation of covariates
  • testing using extra sum of squares principle and F-test
  • confidence intervals
  • residual analysis

    Maximum likelihood: introduction into the most important estimation principle in Statistics

Day 4: ML + Generalized Linear Models (GLM)

  • analysis of binary data and fractions (logistic regression)
  • analysis of counts (Poisson regression, log-linear models)
  • link function, variance function
  • deviance, likelihood ratio test and lack of fit test

Day 5:  Mixed Linear Models

  • analysis of dependent data
  • elements of the model: fixed effects, random effects, components of variance
  • hypothesis testing at different levels
  • restricted maximum likelihood
  • particular covariance structures
  • examples include:
  • subsampling
  • split-plot analysis
  • repeated measurements

Day 6: Bayesian statistics

  • combining prior information and data (likelihood) into posterior distribution
  • use of Winbugs