Past events 2014

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 statistics [mehr]

This five-day course is aimed at Biologists (PhD students and Master students) who work with medium to large datasets. The course goal is to learn how to re-arrange and query the data and how to best manage data. This course will teach researchers how to use the Unix shell, Python programming language, what databases are for and how to use them, to become more efficient at the conduction of the common but often time-consuming scientific task to deal with data. We will spend two days learning different techniques, and then we will move on and deal with your own data sets for two days. We will develop solutions for individual problems in the group. If the time allows it, we will move on to relational databases on the last day. When signing up, please send an exemplary data file that you work with, and which you need to re-arrange or query on a regular basis, but that you find difficult or time-consuming to do in Excel. You do not need to send a complete dataset, what we need to know is the main structure of the dataset, and the task that needs doing. Incomplete or exemplary datasets are sufficient. This course will use the operating systems of OS X (on a Mac) or in a Linux environment. Windows users should be prepared to install Linux on a partition of their laptop, or to install a software that emulates Linux (both are free of charge). Requirements: None. This course aims at people who find using Excel for data management time-consuming, boring and inefficient, but do not know how to do better. No previous experience in scripting is required. After completing this course, you will be able to use the power of your computer to time-efficently handle your data, which will allow you to spend more time doing actual research and analyses. [mehr]

Research workers in many fields are realizing the substantial limitations of statistical tests, test statistics, arbitrary α-levels, P-values, and dichotomous rulings concerning “statistical significance.” These traditional approaches were developed at the beginning of the last century and are being replaced by modern methods that are much more useful. These methods rely on the concept of information loss and formal evidence. They provide easy-to-compute quantities such at the probability of each hypothesis/model and evidence ratios. Furthermore, simple methods allow formal inference (e.g. prediction/forecasting) from all the models in an a priori set (“multimodel inference”). This course on the Information-Theoretic approaches to statistical inference focuses on the practical application of these new methods and is based on Kullback-Leibler information and Akaike’s information criterion (AIC). The material follows the recent textbook: Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, NY. 184pp. A copy of this book, a reference sheet, and several handouts are included in the registration fee. These courses stress science and science philosophy as much as statistical methods. The focus is on quantification and qualification of formal evidence concerning alternative science hypotheses. The courses are informal and discussion and debate is encouraged.Registration deadline: September, 15. [mehr]

This five-day course is aimed at Biologists (PhD students and Master students) who work with medium to large datasets. The course goal is to learn how to re-arrange and query the data and how to best manage data. This course will teach researchers how to use the Unix shell, Python programming language, what databases are for and how to use them, to become more efficient at the conduction of the common but often time-consuming scientific task to deal with data. We will spend two days learning different techniques, and then we will move on and deal with your own data sets for two days. We will develop solutions for individual problems in the group. If the time allows it, we will move on to relational databases on the last day. When signing up, please send an exemplary data file that you work with, and which you need to re-arrange or query on a regular basis, but that you find difficult or time-consuming to do in Excel. You do not need to send a complete dataset, what we need to know is the main structure of the dataset, and the task that needs doing. Incomplete or exemplary datasets are sufficient. This course will use the operating systems of OS X (on a Mac) or in a Linux environment. Windows users should be prepared to install Linux on a partition of their laptop, or to install a software that emulates Linux (both are free of charge). Requirements: None. This course aims at people who find using Excel for data management time-consuming, boring and inefficient, but do not know how to do better. No previous experience in scripting is required. After completing this course, you will be able to use the power of your computer to time-efficently handle your data, which will allow you to spend more time doing actual research and analyses. [mehr]

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 presentationsPrerequisite for participationModul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [mehr]