Hello,
We would like to announce the following online stats course:*Time Series Analysis using regression techniques.* This course also served as a good introduction to GAM and GAMM using mgcv. Format: Online course with on-demand video and live Zoom sessions When: Live summary sessions using Zoom will run on 17, 19, 24, 26, 28 October 2022. Price: 500 GBP (50% reduction for developing countries). Included: A 1-hour face-to-face video chat with one or both instructors. Access to course material: 12 months Flyer:http://highstat.com/Courses/Flyers/2022/Flyer2022_10_TimeSeries_Online.pdf Website:http://highstat.com/index.php/courses-upcoming *Detailed outline* A detailed outline of the course is provided below. All exercises consist of a data set, a video describing the data and the questions, R solution code, and a video discussing the R solution file. Preparation material on data exploration and two exercises are provided. Module 1 Revision exercise on multiple linear regression. Short theory presentation on matrix notation. Theory presentation 'Introduction to GAM'. Three exercises to get familiar with GAM. Module 2 Theory presentation: How to include auto-regressive correlation in a regression model. Exercise showing how to fit a GLM with AR1 correlation in glmmTMB. Exercise on GAM with auto-regressive correlation applied to a regular spaced time-series data set. Exercise on GAM with auto-regressive correlation applied to an irregular spaced time-series data set. Exercise on detecting important changes in trends. Module 3 Theory presentation on linear mixed-effects models. Exercise on linear mixed-effects models. Three exercises on the application of GAMM on time-series data sets. Module 4 Theory presentation on distributions. Theory presentation: Revision of Poisson and negative binomial GLM. Revision exercise on Poisson and negative binomial GLM. Exercise on Poisson and negative binomial GLMM with auto-regressive correlation applied to a time-series data set. Exercise on Poisson and negative binomial GAM applied to a time-series data set. Module 5 Exercise on Bernoulli GAMM applied to time-series data set. Exercise on beta GAMM applied to a time-series data set. Exercise on binomial GAM(M) applied to a time-series data set. Exercise on gamma GAM(M) applied to a time-series data set. Exercise on Tweedie GAM(M) applied to a time-series data set. Kind regards, Alain -- Dr. Alain F. Zuur Highland Statistics Ltd. 9 St Clair Wynd AB41 6DZ Newburgh, UK Email:highs...@highstat.com URL:www.highstat.com [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
