Modern statistics in natural sciences, 5 credits

RECOMMENDED PREREQUISITES

Basic statistical knowledge.

LEARNING OUTCOMES

To give an introduction to the most commonly applied modern statistical techniques and tools used in a wide range of natural sciences. In addition to providing an overview of the statistical “tool-box”, the course generates an understanding of the philosophy and reasoning behind statistical design, modelling and inference. Practical elements (exercises) and group discussions gives the students hands-on experience, deeper insights and confidence. This is a general course in applied statistics that attracts PhD students from biology and other life sciences, geosciences, chemistry, information technology, experimental physics and related fields.

LEARNING OUTCOMES FOR DOCTORAL DEGREE 

The course develops and discusses a number of fundamental and general goals in postgraduate education, such as scientific inferences, limitations of dualism, hypothesis testing, scientific transparency, good statistical practice, experimental design and numerical analysis. More specific goals include insights into choice, and execution, of statistical models and interpretation of statistical analysis, which is key in many fields.

COURSE CONTENTS

The course is focused on analyses of experimental data, but observational data analysis is also covered briefly. The course focusses on linear models and includes: experimental designs leading to ANOVA or ANCOVA, mixed models, blocked experiments, repeated measurement designs, nested and factorial designs, multiple regression including strategies for selecting variables and evaluating models, generalized linear models (GLIM) including logistic and Poisson regression, contingency table tests, power analysis, multivariate analysis and ordination techniques, resampling and permutation statistics, Bayesian model fitting, MCMC techniques, geometric morphometrics and a few other topics.

INSTRUCTION

The core of the course is built around a series of 13 half-day and interactive lectures. In addition, the students then work off-schedule with a series of common practical elements/problems that are then discussed during a series of tutored group discussions. Hands-on advice and individual tutoring of the use of statistical software (R) is also offered at several occasions during the course.

ASSESSMENT

Attendance at all lectures and approved individual practical reports that students hand in.

COURSE EXAMINER

Göran Arnqvist, Goran.Arnqvist@ebc.uu.se

DEPARTMENT WITH MAIN RESPONSIBILITY

Department of Ecology and Genetics

CONTACT PERSON/S 

Göran Arnqvist, Goran.Arnqvist@ebc.uu.se (teacher)
Peter Eklöv, Peter.Eklov@ebc.uu.se (director of PhD studies)

APPLICATION

Submit the application for admission to: https://forms.gle/FtLWrww9R3h4QA589
Submit the application not later than: December 31, 2022