We can entertain you for about 50 days with statistics courses. Obviously, no one wants to do so much statistics at once. We provide 10 statistics courses, see this pdf file for a detailed description. The figure below gives a schematic overview. #### Popular courses

The two most popular courses are:

• Course 1: Data exploration, regression, GLM and GAM. With an introduction to R.
• Course 2: Introduction to mixed modelling and GLMM. We provide three versions of this course; a frequentist version, a Bayesian version with JAGS and a Bayesian version with INLA. The frequentist course is easier, but the Bayesian courses are more versatile.

#### Keywords per course

Course 1: Data exploration, regression, GLM and GAM: with introduction to R

• Introduction to R. Outliers. Transformations. Collinearity. Multiple linear regression. Model selection. Visualising results. Poisson, negative binomial and binomial GLM and GAM. Overdispersion. ggplot2, mgcv.

Course 2: Introduction to mixed effects models and GLMM

• Introduction to linear mixed effects models. Introduction to GLMM. Dealing with pseudoreplication. Nested data. Longitudinal data. This course can be taught using frequentist tools (nlme, lme4 and glmmTMB) or Bayesian tools (either JAGS or INLA).

Course 3: Introduction to zero-inflated models

• Zero inflated models for count data and continuous data. ZIP and ZAP models. Zero-inflated GLMMs for nested data. Analysis of zero-inflated proportional and binomial data.
This course can be taught using frequentist tools (pscl and glmmTMB) or Bayesian tools (either JAGS or INLA).

Course 4: Introduction to GAM and GAMM

• Introduction to GAM. Poisson, negative binomial and binomial GAMs. Revision of mixed effects models. GAMM for nested data and non-linear relationships.
This course can be taught using frequentist tools (mgcv and gamm4) or Bayesian tools (either JAGS or INLA).

Course 5: Introduction to spatial and spatial-temporal models with R-INLA

• Adding spatial and spatial-temporal correlation to regression models, GLMs and GLMMs using R-INLA. Introduction to Bayesian analysis.

Course 6: Time series analysis using R-INLA

• We utilise R-INLA for the analysis of (multivariate) time series within the context of GLMs, GLMMs, GAMs and GAMMs.

Course 7: Workshop and combi-course

• Combine the appropriate modules and use your own data sets during the course.

We also run the following courses: Introduction to R, data visualisation with R, and multivariate analysis with R.

#### Recommended order of courses

If you do not have spatial or temporal data, then we recommend to attend the following courses within a time span of 3 years.

• Introduction to data exploration, regression, GLM and GAM. With introduction to R.
• Introduction to mixed effects models and GLMM (frequentist version).
• Depending on whether you have zero-inflation and/or non-linear relationships you can then attend the ‘Introduction to zero-inflated models’ and/or ‘Introduction to GAM and GAMM’ courses (frequentist version).

If you have spatial or temporal data, then we recommend the following courses.

• Introduction to data exploration, regression, GLM and GAM. With introduction to R.
• Introduction to mixed effects models and GLMM (INLA version).
• Introduction to spatial and spatial-temporal models with R-INLA.
• Depending on whether you have zero-inflation, non-linear relationships and/or time series data, you can then attend the ‘Introduction to zero-inflated models’, ‘Introduction to GAM and GAMM’ and/or the ‘Time series analysis using R-INLA’ courses (INLA version).

It is possible to skip the ‘Introduction to mixed effects models and GLMM’ course before taking the INLA courses, but this requires some preparation.
For more specialised GLMs and GLMMs (without spatial or temporal correlation) we recommend the JAGS version instead of INLA.

Our courses are non-technical and are less suited for people interested in the underlying mathematics.

#### Course material

Our course material is based on the following 10 textbooks.

• Analyzing Ecological Data (2017). Zuur et al.
• Mixed Effects Models and Extensions in Ecology with R (2009). Zuur et al.
• A Beginner's Guide to R (2009). Zuur et al.
• Zero Inflated Models and Generalized Linear Mixed Models with R (2012). Zuur et al.
• A Beginner's Guide to GAM with R (2012). Zuur
• A Beginner's Guide to GLM and GLMM with R (2013). Zuur et al.
• A Beginner's Guide to GAMM with R (2014). Zuur et al.
• A Beginner's Guide to Data Exploration and Visualisation (2015). Ieno and Zuur
• Beginner's Guide to Spatial, temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Volumes I and II. (2017; 2018). Zuur et al.