Applied Nonparametric Statistical Methods, Fourth Edition

While preserving the clear, accessible style of previous editions, this volume reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.

While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some relevant general statistical concepts and an introduction to basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The text also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments, such as large sample methods and computer-intensive applications.Keeping mathematics to a minimum, this text introduces nonparametric methods to undergraduate students who are taking either mainstream statistics courses or statistics courses within other disciplines. By giving the proper attention to data collection and the interpretation of analyses, it provides a full introduction to nonparametric methods.

PREFACE SOME BASIC CONCEPTS Basic Statistics Populations and Samples Hypothesis Testing Estimation Ethical Issues FUNDAMENTALS OF NONPARAMETRIC METHODS A Permutation Test Binomial Tests Order Statistics and Ranks Exploring Data Efficiency of Nonparametric Procedures Computers and Nonparametric Methods Further Reading LOCATION INFERENCE FOR SINGLE SAMPLES Layout of Examples Continuous Data Samples Inferences about Medians Based on Ranks The Sign Test Use of Alternative Scores Comparing Tests and Robustness Fields of Application Summary OTHER SINGLE-SAMPLE INFERENCES Other Data Characteristics Matching Samples to Distributions Inferences for Dichotomous Data Tests Related to the Sign TestA Runs Test for Randomness Angular Data Fields of Application Summary METHODS FOR PAIRED SAMPLES Comparisons in Pairs A Less Obvious Use of the Sign Test Power and Sample Size Fields of Application Summary METHODS FOR TWO INDEPENDENT SAMPLES Centrality Tests and Estimates The Median Test Normal Scores Tests for Equality of Variance Tests for a Common Distribution Power and Sample Size Fields of Application Summary BASIC TESTS FOR THREE OR MORE SAMPLES Comparisons with Parametric Methods Centrality Tests for Independent Samples The Friedman Quade and Page Tests Binary Responses Tests for Heterogeneity of Variance Some Miscellaneous Considerations Fields of Application Summary ANALYSIS OF STRUCTURED DATA Factorial Treatment Structures Balanced 2 × 2 Factorial Structures The Nature of Interactions Alternative Approaches to Interactions Cross-Over Experiments Specific and Multiple Comparison TestsFields of ApplicationSummaryExercises ANALYSIS OF SURVIVAL DATA Special Features of Survival Data Modified Wilcoxon Tests Savage Scores and the Log-Rank Transformation Median Tests for Sequential Data Choice of Tests Fields of Application Summary CORRELATION AND CONCORDANCE Correlation in Bivariate Data Ranked Data for Several Variables Agreement Fields of Application Summary BIVARIATE LINEAR REGRESSION Fitting Straight Lines Fields of ApplicationSummary CATEGORICAL DATA Categories and Counts Nominal Attribute Categories Ordered Categorical Data Goodness-of-fit Tests for Discrete Data Extension of McNemar's Test Fields of application Summary ASSOCIATION IN CATEGORICAL DATA The Analysis of Association Some Models for Contingency Tables Combining and Partitioning of Tables A Legal Dilemma Power Fields of Application SummaryROBUST ESTIMATION When Assumptions Break Down Outliers and Influence The BootstrapM-estimators and Other Robust EstimatorsFields of Application Summary MODERN NONPARAMETRICS A Change in Emphasis Density Estimation Regression Logistic Regression Multivariate Data New Methods for Large Data Sets Correlations within ClustersSummary Exercises appear in each chapter.APPENDIX 1 APPENDIX 2 REFERENCES INDEX