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A Computational Approach to Statistical Arguments in Ecology and Evolution.
Teaches powerful methods to test hypotheses using statistical arguments without the constraints and sophisticated mathematics of classical statistics.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge :
Cambridge University Press,
2011.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title; Copyright; Contents; Acknowledgments; 1 Introduction; 1.1 About the book; Purpose; Intended readers; Why use computation; Prerequisites; How to use this book; Brief overview; 1.2 Basic principles; Applicability; Argument style; Structure and variation; Example of a probability distribution; Other argument styles; 1.3 Scientific argument; Ingredients of statistical argument; Intellectual foundation; Structure; Test statistic; What is a statistical hypothesis?; Summary; 2 Programming and statistical concepts; 2.1 Computer programming; History; The two parts of a computer program.
- PlacesService berry example; Instructions; Leading spaces; Spreadsheet I/O; Procedures; Errors; 2.2 You start programming; Experienced programmers; Getting started with EXCEL macro programming; How to read and write a spreadsheet from your macro; 2.3 Completing the service berry example; Fruit-ripening phenology; Mechanisms of variation in fruit-ripening date; The data; Hypothesis and statistic; A macro to calculate the predicted probability distribution; Calculate the test statistic; Remember the four ingredients; Name vs content; 2.4 Sub CARPEL; 2.5 You practice.
- More about the EXCEL macro editorA real exercise problem; How to solve it; Remember lawyers; 3 Choosing a test statistic; 3.1 Significance of what; Data from fossil marine organisms; The controversy; Relevance of precision; Two irrelevant statistics; Relevant statistics; Freedom to choose any statistic; 3.2 Implement the program; Hypotheses of non-periodicity; Computational overview; Sample the chosen hypothesis with computation; Calculate a relevant statistic; Discover inter-peak intervals; Testing the macro; Estimate realized significance; Using significance to argue; 3.3 Sub PERIOD.
- 4 Random variables and distributions4.1 Random variables; At random; Random process; Continuous distributions; Random variable; 4.2 Distributions; Computation eliminates calculus; Bar graph; Practice writing a macro; Interpret the bar graph; Randomize; Accuracy vs precision; Pseudo-random; 4.3 Arithmetic with random variables; Hypotheses make statistics into random variables; Arithmetic with a random variable and numbers; A macro to convert u to another continuous uniform distribution; Sum of independent samples of the same binary random variable; Pascal's triangle; A macro to estimate s3.
- Macros to estimate other density distributions4.4 Expected value and variance; The middle of a distribution; Theoretical properties of expected value; Variance; Variance of the sum, f + g; The variance of u; 5 More programming and statistical concepts; 5.1 Re-sampling data; A question; Choose a test statistic; Design the macro; Not different mean same random process; Re-sampling data; Overview; Style; Efron; 5.2 Procedures; Why write procedures?; How to write a procedure; Access to places; Sub SORT; BIGDIF3; 5.3 Testing procedures; Testing SORT; Test data; Infinite loop; The watch window.