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
Summary
Teaches powerful methods to test hypotheses using statistical arguments without the constraints and sophisticated mathematics of classical statistics
Notes
Testing PERMUTE
Bibliography
Includes bibliographical references (pages 253-255) and index
