WIAS Course Statistics for the Life Sciences (May-June 2019)

Organised by Wageningen Institute of Animal Sciences (WIAS)

Mon 27 May 2019

Duration 27-29 May + 3-5 June 2019
Room Wageningen Campus

Provisional Program for PhD course Statistics for the Life Sciences

Day 1        Introduction to data-analysis (with SAS)

- including a case study

Day 2 & 3 Linear models

- regression analysis, analysis of variance, analysis of covariance

- 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 (start)

- the most important estimation principle in Statistics

Day 4       Maximum likelihood (continued)

- the most important estimation principle in Statistics

                  Generalized linear models

- 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

Approximately half of the time spent on lectures, half of the time computer practical using SAS.

Lectures / computer practical’s: 9.00 – 17.00 hours.

Please bring along your own data. We’ll try to integrate and discuss your own problems as much as possible into the course!


Henk Bovenhuis (Animal Breeding and Genetics Group)

Bas Engel  (Mathematical and Statistical Methods Group)

Gerrit Gort (Mathematical and Statistical Methods Group)

Prior knowledge for PhD Course  Statistics for the Life Sciences

Descriptive Statistics for univariate and bivariate cases:

  - Numerical summaries: mean, standard deviation / variance, correlation

  - Graphical summaries: boxplot, histogram, scatterplot

Basic probability theory, normal distribution, binomial distribution, Poisson distribution

Inferential Statistics:

  - Principles of estimation

  - Principles of hypothesis testing


  - Single random sample from normal distribution, s unknown

  - Paired samples

  - Two Independent samples

Simple and multiple linear regression, topics:

  - regression model, interpretation of parameters

  - least squares estimation of parameters

  - ANOVA table, estimate of variance

  - T-tests for parameter estimates and predictions

  - F-test for regression

One- and two-way ANOVA for balanced situation, topics:

  - ANOVA model, interpretation of parameters

  - ANOVA table, estimate of variance

  - F-tests for factors

  - additive model

  - interaction in two-way ANOVA

  - t-tests / F-test for linear combinations / contrasts

Analysis of counts:

  -  chi-square test for goodness of fit

  -  analysis of contingency tables: chi-square tests


[Note: this is approximately the end level of the MSc course MAT-20306, which is a second course in Statistics.]