Wilcox R.R. Applying Contemporary Statistical Techniques

Academic Press, 2003. — 608 p. — ISBN: 0127515410, 978-0127515410.Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible.Assumes no previous training in statistics.
Explains how and why modern statistical methods provide more accurate results than conventional methods.
Covers the latest developments on multiple comparisons.
Includes recent advances in risk-based methods.
Features many illustrations and examples using data from real studies.
Describes and illustrates easy-to-use S-plus functions for applying cutting-edge techniques.
Covers many contemporary ANOVA (analysis of variance) and regression methods not found in other books.Software.
R and S-PLUS Functions Written for This Book.
Probability and Related Concepts.
Basic Probability.
Expected Values.
Conditional Probability and Independence.
Population Variance.
The Binomial Probability Function.
Continuous Variables and the Normal Curve.
Understanding the Effects of Nonnormality.
Pearson's Correlation.
Some Rules About Expected Values.
Chi-Squared Distributions.
Summarizing Data.
Basic Summation Notation.
Measures of Location.
Measures of Variation or Scale.
Detecting Outliers.
Computing an M-Estimator of Location.
Histograms.
Kernel Density Estimators.
Stem-and-Leaf Displays.
Sampling Distributions and Confidence Intervals.
Basics.
Random Sampling.
Approximating the Sampling Distribution of The Sample Mean.
The Sample Mean versus MOM, the Median, Trimmed Mean, and M-Estimator.
A Confidence Interval for the Population Mean.
An Approach to Nonnormality: The Central Limit Theorem.
Confidence Intervals when a Is Unknown.
Student's T and Nonnormality.
Confidence Intervals for the Trimmed Mean.
Transforming Data.
Confidence Interval for the Population Median.
A Remark About MOM and M-Estimators.
Confidence Intervals for the Probability of Success.
Hypothesis Testing.
The Basics of Hypothesis Testing.
Power and Type II Errors.
Testing Hypotheses About the Mean When σ Is Not Known.
Controlling Power and Determining n.
Practical Problems with Student's T.
Hypothesis Testing Based on a Trimmed Mean.
Least Squares Regression and Pearson's Correlation.
Fitting a Straight Line to Data: The Least Squares Principle.
The Standard Least Squares Model.
Hypothesis Testing and Confidence Intervals.
Pearson's Correlation.
Testing H₀: ρ = 0.
Concluding Remarks.
Basic Bootstrap Methods.
The Percentile Method.
The Bootstrap-t Interval.
A Modified Percentile Method for Least Squares Regression and Pearson's Correlation.
More About the Population Mean.
Inferences About a Trimmed Mean.
Estimating Power When Testing Hypotheses About a Trimmed Mean.
Inferences Based on MOM and M-Estimators.
Detecting Nonlinear Associations.
Comparing Two Independent Croups.
Student's T.
Relative Merits of Student's T.
Welch's Heteroscedastic Method for Means.
Comparing Groups with Individual Confidence Intervals: An Example of What Not to Do.
A Bootstrap Method for Comparing Means.
A Permutation Test Based on Means.
Yuen's Method for Comparing Trimmed Means.
Bootstrap Methods for Comparing Trimmed Means.
Comparing MOM-Estimators, M-Estimators, and Other Measures of Location.
Comparing Variances or Other Measures of Scale.
Measuring Effect Size.
Comparing Correlations and Regression Slopes.
Comparing Two Binomials.
One-Way ANOVA.
Analysis of Variance (ANOVA) for Independent Groups.
Dealing with Unequal Variances.
Judging Sample Sizes and Controlling Power When Comparing Means.
Trimmed Means.
Bootstrap Methods.
Random Effects Model.
Two-Way ANOVA.
The Basics of a Two-Way ANOVA Design.
Testing Hypotheses About Main Effects and Interactions.
Heteroscedastic Methods for Trimmed Means.
Bootstrap Methods.
Testing Hypotheses Based on Medians.
Comparing Dependent Croups.
The Paired T-Test for Means.
Comparing Trimmed Means.
Bootstrap Methods.
Measuring Effect Size.
Comparing Variances.
Comparing More Than Two Groups.
Percentile Bootstrap Methods for Other Robust Measures of Location.
Comments on Which Method to Use.
Between-by-Within, or Split-Plot, Designs.
Multiple Comparisons.
Homoscedastic Methods for the Means of Independent Groups.
ANOVA F Versus Multiple Comparisons.
Heteroscedastic Methods for the Means of Independent Groups.
Linear Contrasts.
Judging Sample Sizes.
Methods for Trimmed Means.
Bootstrap Methods.
Methods for Dependent Groups.
Analyzing Between-by-Within Designs.
Robust and Exploratory Regression.
Detecting Outliers in Multivariate Data.
Some Robust Regression Methods.
More Regression Estimators.
Comments on Choosing a Regression Estimator.
Hypothesis Testing and Confidence Intervals.
Robust Measures of Correlation.
More Regression Methods.
Smoothers.
Smooths Based on Robust Measures of Location.
Comparing the Slopes of Two Independent Groups.
Tests for Linearity.
Inferential Methods with Multiple Predictors.
Identifying the Best Predictors.
Detecting Interactions.
ANCOVA.
Rank-Based and Nonparametric Methods.
Comparing Two Independent Groups.
Comparing More Than Two Groups.
Multiple Comparisons Among Independent Groups.
Two-Way Designs.
Multiple Comparisons in a Two-Way Design.
Comparing Two Dependent Groups.
Comparing Multiple Dependent Groups.
One-Way Multivariate Methods.
Between-by-Within Designs.
Rank-Based Correlations.
Comparing Rank-Based Correlations.
Rank-Based Regression.
The Rank-Transform Method.
Appendix A. Solutions to Selected Exercises.
Appendix V. Tables.
Appendix S. Basic Matrix Algebra.