Author Description

For Most Of The Twentieth Century Through To The Present Day, Statistics Has Been Neatly Divided Into Two Theoretical Frameworks: Classical/frequentist And Bayesian. Scientists Typically Choose The Statistical Theoretical Framework To Analyze Their Data Depending On The Nature And Complexity Of The Problem, And Based On Their Personal Views On Probability And Uncertainty. While Textbooks And Courses Should Reflect And Anticipate This Dual Reality, They Rarely Do So. Scientists Needing To Employ The Alternative Statistical Framework Almost Need To Relearn From Scratch. This Book Explains, Discusses, And Applies Both The Classical/frequentist And Bayesian Statistical Frameworks To Fit The Different Types Of Generalized Linear Mixed Models (glmm). The Glmms Allow An Analysis Of The Types Of Data Commonly Gathered By Researchers In The Life Sciences Scientists Incorporating The Experimental Or Survey Design, Or, More Generally, Features Of The Data Collection Process. It Presents Material In An Intuitive, Approachable, And Progressive Manner Suitable For Research Scientists And Graduate Students With Only A Very Basic Knowledge Of Calculus And Statistics. The Book Covers The Material In A Theoretically Rigorous Manner, Focusing On The Practical Applications Of All The Methods To Actual Research Data--