We would like to announce the following online stats course:
Course: Time series analysis using regression techniques
Format: Online course with on-demand video and live Zoom sessions
When: Live summary sessions using Zoom will run in October 2022.
Price: 500 GBP (50% reduction for developing countries).
Included: A 1-hour face-to-face video chat with one or both instructors.
Flyer: http://highstat.com/.../2022/Flyer2022_04_ZIMF_Online.pdf
Website: http://highstat.com/index.php/courses-upcoming
A detailed outline of the course is provided below. All exercises
contain a video discussing the R solution code. Revision material on
data exploration and multiple linear regression is provided. All theory
material is also presented in videos.
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 Zuur
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