Borrelios - fakta och fiktion?

Lyme-borrelios är ett aktuellt ämne som väcker mycket debatt bland såväl hälsovårdspersonal som lekmän. I antalet sjukdomsfall ses en jämnt stigande trend. Fästingar påträffas numera i hela Finland ända upp till södra Lappland. Risken för borrelios efter ett fästingbett är i genomsnitt 2 procent och därför är ett bett inte en indikation för antibiotikabehandling. Behandlingen av den typiska hudförändringen, erythema migrans, som uppstår på huden omkring bettet, består däremot av en antibiotikakur. Inga laboratorieprov behövs i detta skede. </p

New kind of operations model - meaningful learning through entrepreneurship education

Voimala Coaching Center for Entrepreneurship offers coaching for students and teachers at the upper secondary level. Voimala project started 1.5.2008 and operates under Tampere University of Applied Sciences Proacademy unit that specializes in entrepreneurship studies for BBA students. Voimala is financed by the European Social Fund, Pirkanmaa Centre for Economic Development, Transport, and the Environment, and Tampere University of Applied Sciences. Voimala project was set to have two major purposes. Firstly, to transform the entrepre- neurship culture of young people in Pirkanmaa region by creating positive entrepreneurship experiences and secondly, to offer tools for teachers to bring entrepreneurship education into practice at their work. To succeed in this mission coaches challenge and support the students and teachers in the learning process by utilizing Proacademy’s practical training methods. These methods are based on team-centred learning tools such as coaching in small teams, dialogue, knowledge-creation sessions, innovation, learning by doing, and real world working life customer projects

The Projected Covariance Measure for assumption-lean variable significance testing

Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test whether the regression coefficient for $X$ is non-zero. However, when the model is misspecified, the test may have poor power, for example when $X$ is involved in complex interactions, or lead to many false rejections. In this work we study the problem of testing the model-free null of conditional mean independence, i.e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$. We propose a simple and general framework that can leverage flexible nonparametric or machine learning methods, such as additive models or random forests, to yield both robust error control and high power. The procedure involves using these methods to perform regressions, first to estimate a form of projection of $Y$ on $X$ and $Z$ using one half of the data, and then to estimate the expected conditional covariance between this projection and $Y$ on the remaining half of the data. While the approach is general, we show that a version of our procedure using spline regression achieves what we show is the minimax optimal rate in this nonparametric testing problem. Numerical experiments demonstrate the effectiveness of our approach both in terms of maintaining Type I error control, and power, compared to several existing approaches.Comment: 89 pages, 5 figure