Janez Staregraduated from the Faculty of Mathematics, University of Ljubljana, then gained a Master's Degree and PhD in Biostatistics from the University of Ljubljana's Faculty of Medicine.
He is currently full Professor of Biostatistics and Head of the Institute of Biostatistics and Medical Informatics, Faculty of Medicine, Ljubljana, and Head of the Doctoral Programme in Statistics at University of Ljubljana.
His research interests are explained variation in survival analysis, predictive ability of regression models in survival analysis, frailties, random effects in survival models, relative survival, goodness of fit of regression models, and scientometrics.
Janez Stare graduated from the Faculty of Mathematics, University of Ljubljana, then held a Master Degree and Ph.D. in Biostatistics from the Faculty of Medicine, University of Ljubljana. He is currently full Professor of Biostatistics at the Faculty of Medicine, Ljubljana, and Head of the Institute of Biostatistics and Medical Informatics, Faculty of Medicine, Ljubljana. His research interests are, or were, explained variation in survival analysis, predictive ability of regression models in survival analysis, frailties, random effects in survival models, relative survival, bibliometry.
Home page: http://ibmi.mf.uni-lj.si/janez/index.php?lang=en-GB
This is a refresher course in basic concepts of inferential statistics. In principle no statistical knowledge is pre-required, although I assume that all participants were exposed to some sort of statistical courses in the past. The same goes for mathematics. Although mathematics will be minimized, at least one course in college level algebra is assumed. This includes understanding the notion of a function. Some knowledge of calculus (maximization of a function, integration) is welcome, but not necessary.
Short course outline
1. Basic notions of probability: events, random variables, probability distribution, cumulative distribution function.
2. Some standard distributions: binomial, Poisson, normal, t.
3. Expected value and variance.
4. Sampling distributions of the mean, variance, and the difference of two means.
5. Point estimation, standard errors, confidence intervals.
6. Hypotheses testing, Type I and Type II errors.
7. Some basic tests: one sample and two sample t-tests, analysis of proportions, test of association between two categorical variables, introduction to bivariate linear regression.
The course will consist of 9 hours of lectures, and 6 hours of practical work. SPSS will be used, but no experience with the program is assumed.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.