# Past events 2018

Speaker: Dr. Fränzi Korner-Nievergelt

### Statistics Module 3: Generalised linear models and generalised linear mixed models

##### Statistics Module 3: Generalised linear models and generalised linear mixed models
Generalised linear models and generalised linear mixed models: Binomial model, Poission model, GLMM and work on own data [more]

### Statistics Module 4: Own Data Workshop

##### Statistics Module 4: Own Data Workshop
Guided work on own data. [more]

### Statistics Module 1: Introduction to basic statistics and R

##### Statistics Module 1: Introduction to basic statistics and R
Day 1: Introduction to R (working in the batch modus, programming language R, reading and displaying data, writing functions, simulating data) + Basic theory (Probability distributions, Central limit theorem, Bayes theorem, Bootstrapping, Inference from data using frequentist and Bayesian methods, classical frequentist tests (t-, F-, Chi-, Wilcoxon-test)) Day 2: Computation techniques (Monte Carlo simulation, Approximations), Application to own or simulated data: Comparison of two means using frequentist and Bayesian methods, Discussion [more]

### Statistics Module 2: Linear Models and Linear Mixed Models with R

##### Statistics Module 2: Linear Models and Linear Mixed Models with R
Linear models (LM) and linear mixed models (LME): Linear Regression, multiple Regression, ANOVA, ANCOVA, model selection (group work), linear mixed models, work on own data [more]

### Statistics Module 3: Generalised linear models and generalised linear mixed models

##### Statistics Module 3: Generalised linear models and generalised linear mixed models
Generalised linear models and generalised linear mixed models: Binomial model, Poission model, GLMM and work on own data [more]

### Statistics Module 4: Own Data Workshop

##### Statistics Module 4: Own Data Workshop
Guided work on own data. [more]

### Statistics Module 1: Introduction to basic statistics and R

##### Statistics Module 1: Introduction to basic statistics and R
Day 1: Introduction to R (working in the batch modus, programming language R, reading and displaying data, writing functions, simulating data) + Basic theory (Probability distributions, Central limit theorem, Bayes theorem, Bootstrapping, Inference from data using frequentist and Bayesian methods, classical frequentist tests (t-, F-, Chi-, Wilcoxon-test)) Day 2: Computation techniques (Monte Carlo simulation, Approximations), Application to own or simulated data: Comparison of two means using frequentist and Bayesian methods, Discussion [more]

### Statistics Module 2: Linear Models and Linear Mixed Models with R

##### Statistics Module 2: Linear Models and Linear Mixed Models with R
Linear models (LM) and linear mixed models (LME): Linear Regression, multiple Regression, ANOVA, ANCOVA, model selection (group work), linear mixed models, work on own data [more]

### Statistics Module 3: Generalised linear models and generalised linear mixed models

##### Statistics Module 3: Generalised linear models and generalised linear mixed models
Generalised linear models and generalised linear mixed models: Binomial model, Poission model, GLMM and work on own data [more]
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