IMPRS Events 2015

IMPRS Events 2015

Speaker: Dr. Fränzi Korner-Nievergelt
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Statistics Module 2: Linear Models and Linear Mixed Models with R

Statistics Module 2: Linear Models and Linear Mixed Models with R
day 1: LM Linear Regression, multiple Regression ANOVA, ANCOVA (least-square method, parameterisation, interactions, tests (marginal and sequential), model selection, model assumptions, predictions, introduction to Bayesian data analysis); day 2: LME linear mixed models (maximum likelihood, restricted maximum likelihood, random and fixed effects, likelihood ratio test / bootstrap, random slopes-random intercept models, evt. further model types depending on the participants wishes); day 3: LME (model matrix, simulating posterior distributions of model parameters, predictions, posterior probabilities of hypotheses, preparing data for work on own data); day 4: work on own data and presentations. Prerequisite for participation: Basic knowledge in R programming is required. Particularly, it is assumed that you are familiar with working with the R Console and an editor, reading the data and producing the most common graphics (histogram, scatterplot, boxplot). [more]
2159 1437492503

Statistics Module 4: Own Data Workshop

Statistics Module 4: Own Data Workshop
day 1: 2-3 Short inputs depending on participants projects short presentation of participants projects day 2 and 3: guided work on own project day 3: presentations of projects prerequisite for participation basic knowledge in statistics, participation in at least 2 of the Modules 1 – 3. Participants bring their own data. They are requested to send a short description of their projects to the teachers at least one week before the start of the workshop. [more]
2146 1437492238

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

Statistics Module 2: Linear Models and Linear Mixed Models with R
day 1: LM Linear Regression, multiple Regression ANOVA, ANCOVA (least-square method, parameterisation, interactions, tests (marginal and sequential), model selection, model assumptions, predictions, introduction to Bayesian data analysis); day 2: LME linear mixed models (maximum likelihood, restricted maximum likelihood, random and fixed effects, likelihood ratio test / bootstrap, random slopes-random intercept models, evt. further model types depending on the participants wishes); day 3: LME (model matrix, simulating posterior distributions of model parameters, predictions, posterior probabilities of hypotheses, preparing data for work on own data); day 4: work on own data and presentations. Prerequisite for participation: Basic knowledge in R programming is required. Particularly, it is assumed that you are familiar with working with the R Console and an editor, reading the data and producing the most common graphics (histogram, scatterplot, boxplot). [more]
2147 1437492209

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

Statistics Module 3: Generalised linear models and generalised linear mixed models
Day 1: Binomial model - refreshing LM and LMM - introduction Bayesian data analysis - logistic regression, binomial model - model assumptions, overdispersion - tests, predictions Day 2: Poisson model - Poisson model - model assumptions, overdispersion - tests, predictions - depending on participants wishes: zero-inflation Day 3: GLMM - including random effects - glmer-function - depending on participants wishes: introduction to WinBUGS and more complex models Day 4: projects - work on own data and presentations Prerequisite for participation Modul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [more]
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