Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts.
Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
M. D. Edge is a Postdoctoral Researcher in the Department of Evolution and Ecology at the University of California, Davis. Starting in 2020, he will be an Assistant Professor of Biological Sciences in the Quantitative and Computational Biology section at the University of Southern California.
Inhaltsangabe
Dedication Acknowledgments Prelude 1: Encountering Data 2: R and Exploratory Data Analysis 3: The Line of Best Fit 4: Probability and Random Variables 5: Properties of Random Variables Interlude 6: Properties of Point Estimators 7: Interval Estimation and Inference 8: Semiparametric Estimation and Inference 9: Parametric Estimation and Inference 10: Bayesian Estimation and Inference Postlude: Models and Data Appendix A: A Tour of Calculus Appendix B: More R Details Appendix C: Answers to Exercises Table of mathematical notation Glossary Bibliography Index
Dedication Acknowledgments Prelude 1: Encountering Data 2: R and Exploratory Data Analysis 3: The Line of Best Fit 4: Probability and Random Variables 5: Properties of Random Variables Interlude 6: Properties of Point Estimators 7: Interval Estimation and Inference 8: Semiparametric Estimation and Inference 9: Parametric Estimation and Inference 10: Bayesian Estimation and Inference Postlude: Models and Data Appendix A: A Tour of Calculus Appendix B: More R Details Appendix C: Answers to Exercises Table of mathematical notation Glossary Bibliography Index
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