Foundational and applied statistics for biologists using R / Ken A. Aho

Incluye bibliografía: páginas 555-574 e índice: páginas 575-596

Foundations.. Philosophical and Historical Foundations.. Introduction.. Nature of Science.. Scientific Principles.. Scientific Method.. Scientific Hypotheses.. Logic.. Variability and Uncertainty in Investigations.. Science and Statistics.. Statistics and Biology.. Introduction to Probability.. Introduction: Models for Random Variables.. Classical Probability.. Conditional Probability.. Odds.. Combinatorial Analysis.. Bayes Rule.. Probability Density Functions.. Introduction.. Introductory Examples of pdfs.. Other Important Distributions.. Which pdf to Use?.. Reference Tables.. Parameters and Statistics.. Introduction.. Parameters.. Statistics.. OLS and ML Estimators.. Linear Transformations.. Bayesian Applications.. Interval Estimation: Sampling Distributions, Resampling Distributions, and Simulation Distributions.. Introduction.. Sampling Distributions.. Confidence Intervals.. Resampling Distributions.. Bayesian Applications: Simulation Distributions.. Hypothesis Testing.. Introduction.. Parametric Frequentist Null Hypothesis Testing.. Type I and Type II Errors.. Power.. Criticisms of Frequentist Null Hypothesis Testing.. Alternatives to Parametric Null Hypothesis Testing.. Alternatives to Null Hypothesis Testing.. Sampling Design and Experimental Design.. Introduction.. Some Terminology.. The Question Is: What Is the Question?.. Two Important Tenets: Randomization and Replication.. Sampling Design.. Experimental Design.. Applications.. Correlation.. Introduction.. Pearson's Correlation.. Robust Correlation.. Comparisons of Correlation Procedures.. Regression.. Introduction.. Linear Regression Model.. General Linear Models.. Simple Linear Regression.. Multiple Regression.. Fitted and Predicted Values.. Confidence and Prediction Intervals.. Coefficient of Determination and Important Variants.. Power, Sample Size, and Effect Size

Assumptions and Diagnostics for Linear Regression.. Transformation in the Context of Linear Models.. Fixing the Y-Intercept.. Weighted Least Squares.. Polynomial Regression.. Comparing Model Slopes.. Likelihood and General Linear Models.. Model Selection.. Robust Regression.. Model II Regression (X Not Fixed.. Generalized Linear Models.. Nonlinear Models.. Smoother Approaches to Association and Regression.. Bayesian Approaches to Regression.. Anova.. Introduction.. One-Way Anova.. Inferences for Factor Levels.. Anova as a General Linear Model.. Random Effects.. Power, Sample Size, and Effect Size.. Anova Diagnostics and Assumptions.. Two-Way Factorial Design.. Randomized Block Design.. Nested Design.. Split-Plot Design.. Repeated Measures Design.. Anova.. Unbalanced Designs.. Robust Anova.. Bayesian Approaches to Anova.. Tabular Analyses.. Introduction.. Probability Distributions for Tabular Analyses.. One-Way Formats.. Confidence Intervals for p.. Contingency Tables.. Two-Way Tables.. Ordinal Variables.. Power, Sample Size, and Effect Size.. Three-Way Tables.. Generalized Linear Models.. Appendix.. References.. Index

Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complicated procedures step by step and thus better understand the process of obtaining analysis results. The graphical capabilities of R are used to provide interactive demonstrations of simple to complex statistical concepts. Assuming only familiarity with algebra and general calculus, the text offers a flexible structure for both introductory and graduate-level biostatistics courses. The first seven chapters address fundamental topics in statistics, such as the philosophy of science, probability, estimation, hypothesis testing, sampling, and experimental design. The remaining four chapters focus on applications involving correlation, regression, ANOVA, and tabular analyses. Unlike classic biometric texts, this book provides students with an understanding of the underlying statistics involved in the analysis of biological applications. In particular, it shows how a solid statistical foundation leads to the correct application of procedures, a clear understanding of analyses, and valid inferences concerning biological phenomena. Web Resource An R package (asbio) developed by the author is available from CRAN. Accessible to those without prior command-line interface experience, this companion library contains hundreds of functions for statistical pedagogy and biological research. The author's website also includes an overview of R for novices. eng