|Who Should Attend||Some familiarity with GenStat is necessary (for example from an Introductory GenStat Course). You should also be aware of the standard probability distributions (Normal, binomial and Poisson), but there will be no need for any complicated maths. On each day the lectures will be interspersed with practicals to introduce you to real-life data sets and illustrate the methods. The practicals also give you the opportunity to discuss your own problems and investigations with the presenters.|
|Overview||Regression is one of the most widely used methods in statistics, and one that is still producing new and exciting techniques. This 2-day course starts by explaining ordinary linear regression (with one or several variables), and then extends the ideas to nonlinear models and on to generalized linear models – so that you can analyse counts and proportions as well as the more usual numeric variables. The final session will introduce some of the most recent developments in generalized linear models, including Youngjo Lee and John Nelder’s hierarchical generalized linear models, to bring you fully up-to-date with the range of possibilities.|
|Learning Objectives||GenStat has a very powerful set of facilities for regression and generalized linear models that are nevertheless very straightforward and easy to use. The course is designed to familiarize you with these techniques, and give you the underlying knowledge and confidence to use them correctly and effectively. It shows how GenStat’s menus guide you from simple even to very complicated analyses, and also explains the regression commands to enable you to program any non-standard analyses that you need.|
|Methods of Evaluation||Subsequent to instructor facilitated sessions, participants will be expected to complete example analyses and exercises unaided.|
|Training Methods||Instructor facilitated, interactive computer sessions.|
|Contents||The Course is in 4 sessions:
For further information about this course or on-site training, please email the training team or call them on +44-(0)1442-450230.