An accessible textbook that explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists.
An accessible textbook that explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Pablo Inchausti is Professor of Ecology at the Universidad de la República, Centro Universitario Regional del Este, Uruguay. He is the co-editor of the influential and highly-cited book Biodiversity and Ecosystem Functioning: synthesis and perspectives (OUP, 2002) and has been successfully teaching statistics and mathematical modelling to students of the life and social sciences for over 15 years.
Inhaltsangabe
* Part 1: The Conceptual Basis For Fitting Statistical Models * 1: General introduction * 2: Statistical modeling: a short historical background * 3: Estimating parameters: the main purpose of statistical inference * Part II: Applying The Generalized Linear Model to Varied Data Types * 4: The General Linear Model I: numerical explanatory variables * 5: The General Linear Model II: categorical explanatory variables * 6: The General Linear Model III: interactions between explanatory variables * 7: Model selection: one, two, and more models fitted to the data * 8: The Generalized Linear Model * 9: When the response variable is binary * 10: When the response variables are counts, often with many zeros * 11: Further issues involved in the modeling of counts * 12: Models for positive real-valued response variables: proportions and others * Part III: Incorporating Experimental and Survey Design Using Mixed Models * 13: Accounting for structure in mixed/hierachical structures * 14: Experimental design in the life sciences - the basics * 15: Mixed-hierachical models and experimental design data * Afterword * R packages used in the book * Appendix 1: Using R and RStudio: the basics (only available online at www.oup.com/companion/InchaustiSMWR) * Appendix 2: Exploring and describing the evidence in graphics (only available online at www.oup.com/companion/InchaustiSMWR)
* Part 1: The Conceptual Basis For Fitting Statistical Models * 1: General introduction * 2: Statistical modeling: a short historical background * 3: Estimating parameters: the main purpose of statistical inference * Part II: Applying The Generalized Linear Model to Varied Data Types * 4: The General Linear Model I: numerical explanatory variables * 5: The General Linear Model II: categorical explanatory variables * 6: The General Linear Model III: interactions between explanatory variables * 7: Model selection: one, two, and more models fitted to the data * 8: The Generalized Linear Model * 9: When the response variable is binary * 10: When the response variables are counts, often with many zeros * 11: Further issues involved in the modeling of counts * 12: Models for positive real-valued response variables: proportions and others * Part III: Incorporating Experimental and Survey Design Using Mixed Models * 13: Accounting for structure in mixed/hierachical structures * 14: Experimental design in the life sciences - the basics * 15: Mixed-hierachical models and experimental design data * Afterword * R packages used in the book * Appendix 1: Using R and RStudio: the basics (only available online at www.oup.com/companion/InchaustiSMWR) * Appendix 2: Exploring and describing the evidence in graphics (only available online at www.oup.com/companion/InchaustiSMWR)
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