Lund University

This is an advanced course in linear and logistic regression, which expounds on the knowledge gained in introductory mathematical statistics courses. It covers matrix formulation of multivariate regression, methods for model validation, residuals, outliers, influential observations, construction and use of F- and t- tests, likelihood-ratio-test, confidence intervals and prediction, and applied implementation of various techniques in R software. Students also consider correlated errors, Poisson regression, multinominal and ordinal logistic regression. The first part of the course expands on previous study of linear regression to consider how to check if the model fits the data, what to do if it does not fit, how uncertain it is, and how to use it to draw conclusions about reality. The second part of the course explores logistic regression, which is used in surveys where the answers follow a categorical alternative pattern such as “yes/no,” “little/just fine/much,” or “car/bicycle/bus.” Students describe differences between continuous and discrete data, and the resulting consequences for the choice of statistical model. Students learn to give an account of the principles behind different estimation principles, and describe the statistical properties of such estimates as they appear in regression analysis. The interpretation of regression relations in terms of conditional distributions is studied. Odds and odds ration are presented, and students describe their relation to probabilities and to logistic regression. Students formulate both linear and logistic regression models for concrete problems, estimate and interpret the parameters, examine the validity of the model and make suitable modifications, use the model for prediction, utilize a statistical computer program for analysis, and present the analysis and conclusions of a practical problem in a written report and oral presentation. The course makes use of lectures, exercises, computer exercises, and project work.

This course is an advanced biology course providing an understanding of the fate, uptake, and effects of anthropogenic pollutants in the environment. Students study and apply ecotoxicological theories, models, and common methods and approaches concerning the fate of pollutants in the environment using fugacity-based multimedia partitioning models, uptake and accumulation using models of bioconcentration and biomagnification, and effects on different levels of biological organization (cellular, organism, population, and ecosystem) using ecotoxicological test methods with different endpoints.

This course provides an overview of Swedish and Nordic history from the Viking Age to the present. To allow a critical approach to an otherwise Swedish interpretation of history, the course is taught from a Nordic perspective. Selected issues from Swedish and Nordic history are discussed in order to orient students in relevant discussions and arguments in historical theory. Students are also trained in historical argumentation based on scholarly foundations.

The course addresses the development of communism after World War II, focusing on the situation in Central and Eastern Europe. The aim is to study the development of communism during the Cold War, from the Yalta conference in 1945 to the fall of the Soviet Union in 1991, and its consequences in the 1990s and early twenty-first century. The subject is addressed from both genetic and genealogical perspectives.

This course gives a general introduction to embedded system design which can be implemented using System-on-Chip technology. This kind of embedded systems contains both hardware and software components and therefore a hardware/software co-design is emphasized. The course gives a basic knowledge on specification methods, design representations (computational models) as well as related design methods. Special emphasis is placed on interface synthesis and low-power design methods.