Requiring only introductory statistics and basic mathematics, this textbook avoids jargon and provides worked examples, data sets and R code, and review exercises. Designed for advanced undergraduates and postgraduates studying biostatistics and experiment design in biology-related fields, it applies statistical concepts to biological scenarios.
Requiring only introductory statistics and basic mathematics, this textbook avoids jargon and provides worked examples, data sets and R code, and review exercises. Designed for advanced undergraduates and postgraduates studying biostatistics and experiment design in biology-related fields, it applies statistical concepts to biological scenarios.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Gerry Quinn is an Honorary Professor in the School of Life and Environmental Sciences at Deakin University, having served as Chair in Marine Biology and Head of Warrnambool Campus during his academic career. He has extensive experience in teaching biostatistics at Deakin University and the University of Gothenburg.
Inhaltsangabe
Contents: List of Acronyms Preface 1. Introduction 2. Things to Know Before Proceeding 3. Sampling and Experimental Design 4. Introduction to Linear Models 5. Exploratory Data Analysis 6. Simple Linear Models with One Predictor 7. Linear Models for Crossed (Factorial) Designs 8. Multiple Regression Models 9. Predictor Importance and Model Selection in Multiple Regression Models 10. Random Factors in Factorial and Nested Designs 11. Split-plot (Split-unit) Designs: Partly Nested Models 12. Repeated Measures Designs 13. Generalized Linear Models for Categorical Responses 14. Introduction to Multivariate Analyses 15. Multivariate Analyses Based on Eigenanalyses 16. Multivariate Analyses Based on (dis)similarities or Distances 17. Telling Stories with Data References Glossary Index.
Contents: List of Acronyms Preface 1. Introduction 2. Things to Know Before Proceeding 3. Sampling and Experimental Design 4. Introduction to Linear Models 5. Exploratory Data Analysis 6. Simple Linear Models with One Predictor 7. Linear Models for Crossed (Factorial) Designs 8. Multiple Regression Models 9. Predictor Importance and Model Selection in Multiple Regression Models 10. Random Factors in Factorial and Nested Designs 11. Split-plot (Split-unit) Designs: Partly Nested Models 12. Repeated Measures Designs 13. Generalized Linear Models for Categorical Responses 14. Introduction to Multivariate Analyses 15. Multivariate Analyses Based on Eigenanalyses 16. Multivariate Analyses Based on (dis)similarities or Distances 17. Telling Stories with Data References Glossary Index.
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