Cursus

# WIAS Course Statistics for the Life Sciences

 Organisator Wageningen Institute of Animal Sciences (WIAS) wo 1 juni 2016 tot wo 8 juni 2016 09:00

Program:

June 1      Introduction to data-analysis (with SAS)

- including a case study

June 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

June 6       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)

- deviance, likelihood ratio test and lack of fit test

June 7       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

June 8       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.

Teachers

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

T-tests:

- 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