Stat-d.si Website Review

lecture: statistical topology and the random interstellar medium, November 8, 2018. Invitation to submit contributions for the 6th eu-microdata user conferences conference, until 29.9.2018. Invitation to submit a contribution for the Symbolic Data An*lysis Workshop, up to 22.7 .. lecture: predictive genetics: from attending to personalized medicine, 27. 3. 2018. lecture: use of design of experiments in the development of nasal spray, 19. 1. 2018. results of the tender new statistical approaches and original use of statistics. lecture: comparison of methods for clas*ification of scientific publications on the basis of references, 29.3.2017. lecture: control charts: statistical tool with a long tradition and ever-new usage, February 21st 017th lecture: statistics in the background of international knowledge research, 26/1/2017 round table: ethics in science with emphasis on the protection of personal data, 12 1. 2017. news: international fereration of the clas*ification society, October 2016. lecture: statistical inference problems based on the moments of distributions, 8. 12.2015. lecture: causal mediation an*lysis of observational, population-based cancer survival data, 10/13/2015. workshop research data management planning: problems and solutions. the adopted national strategy for open access to scientific publications and research data in Slovenia. lecture: use of different information sources in the as*essment of scientific excellence. lecture: ranking of sports teams using probable forecasts from betting odds, 11/18/2014. lecture: economic sustainability ratio and demographic dividend in Europe, 28.10.2014. 2014 is open for abstract submission and registration for applied statistics. call for invited papers for the second international conference on survey methods in multinational, multiregional and multicultural contexts. lecture: meta an*lysis of the results of clinical studies on the efficacy and safety of medicines. lecture: applications of as*essment of reliability and interpretation of regression forecasts in machine learning.