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Data Analysis by Resampling Concepts and Applications

ISBN-10: 0534221106

ISBN-13: 9780534221102

Edition: 2000

Authors: Clifford E. Lunneborg

List price: $199.95
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Description:

In DATA ANALYSIS BY RESAMPLING, Clifford Lunneborg argues that modern computing power has rendered the model-driven and assumption-plagued data analyses of the past unnecessary, obsolete, and inappropriate. This book introduces readers to modern, design-driven analyses that depend only on the observed data, on knowledge of how the data were collected, and on questions the data were intended to answer. Overall, Lunneborg provides a modern and timely approach to statistical inference.
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Book details

List price: $199.95
Copyright year: 2000
Publisher: Brooks/Cole
Publication date: 12/29/1999
Binding: Hardcover
Pages: 566
Size: 7.25" wide x 9.25" long x 1.25" tall
Weight: 2.288
Language: English

C.E. Lunneborg is Professor Emeritus of Psychology and Statistics at the University of Washington. During a career spanning 40 years he has published over 100 technical articles and three university-level texts. His current research interests are in resampling, experimental design, and web-based instruction.

Resampling Concepts
Introduction
Terms and Notation
Cases, Attributes, Scores, and Treatments
Experimental and Observational Studies
Data Sets, Samples, and Populations
Parameters, Statistics, and Distributions
Distribution Functions
Cases, Attributes, and Distributions
Attributes, Scores, Groups, and Treatments
Distributions of Scores and Statistics
Exercises
Populations and Random Samples
Varieties of Populations
Random Samples
Random Sampling
Simple Random Samples
Exercises
Statistics and Sampling Distributions
Statistics and Estimators
Accuracy of Estimation
The Sampling Distribution
Bias of an Estimator
Standard Error of a Statistic
RMS Error of an Estimator
Confidence Interval
Sampling Distribution Computations
Exercises
Testing Population Hypotheses
Population Statistical Hypotheses
Population Hypothesis Testing
Null Sampling Distribution p-Values
The p-Value of a Directional Test
The p-Value of a Nondirectional Test
Exercises
Parametrics, Pivotals, and Asymptotics
The Unrealizable Sampling Distribution
Sampling Distribution of a Sample Mean
Parametric Population Distributions
Pivotal Form Statistics
Asymptotic Sampling Distributions
Limitations of the Mathematical Approach
Cls for Normal Population [mu] and [sigma][superscript 2]
CI for a Normal Population Mean
CI for a Normal Population Variance
Nonparametric CI Estimation
Exercises
Limitations of Parametric Inference
Range and Precision of Scores
Size of Population
Size of Sample
Roughness of Population Distributions
Parameters and Statistics of Interest
Scarcity of Random Samples
Resampling Inference
Resampling Approaches to Inference
Exercises
The Real and Bootstrap Worlds
The Real World of Population Inference
The Bootstrap World of Population Inference
Real-World Population Distribution Estimates
Nonparametric Population Estimates
Sample Size and Distribution Estimates
Bootstrap Population Distributions
Nonparametric Population Estimates
Exercises
The Bootstrap Sampling Distribution
The Bootstrap Conjecture
Complete Bootstrap Sampling Distributions
Monte Carlo Bootstrap Distributions
The Bootstrap Estimate of Standard Error
The Bootstrap Estimate of Bias
Simple Bootstrap CI Estimates
Bootstrap SE, Bias, and CI Estimates
Example
Exercises
Better Bootstrap CIs: The Bootstrap-t
Pivotal Form Statistics
The Bootstrap-t Pivotal Transformation
Forming Bootstrap-t CIs
Estimating the Standard Error of an Estimate
Range of Application of the Bootstrap-t
Iterated Bootstrap CIs
SEs and CIs for Trimmed Means
Definition of the Trimmed Mean
Importance of the Trimmed Mean
A Note on Outliers
Determining the Trimming Fraction
Sampling Distribution of the Trimmed Mean
Applications
Exercise
Better Bootstrap CIs: BCA Intervals
Bias-Corrected and Accelerated CI Estimates
Applications of the BCA CI
Better Confidence Interval Estimates
Using CI Correction Factors
Requirements for a BCA CI
Implementations of the BCA Algorithm
Exercise
Bootstrap Hypothesis Testing
CIs, Null Hypothesis Tests, and p-Values
Bootstrap-t Hypothesis Testing
Bootstrap Hypothesis Testing Alternatives
CI Hypothesis Testing
Confidence Intervals or p-Values?
Bootstrap p-Values
Computing a Bootstrap-t p-Value
Fixed-[alpha] CIs and Hypotheis Testing
Computing a BCA CI p-Value
Exercise
Randomized Treatment Assignment
Two Functions of Randomization
Randomization of Sampled Cases
Randomization of Available Cases
Statistical Basis for Local Causal Inference
Population Hypotheses Revisited
Monte Carlo Reference Distributions
Serum Albumen in Diabetic Mice
Resampling Stats Analysis
SC Analysis
S-Plus Analysis
Exercises
Strategies for Randomizing Cases
Independent Randomization of Cases
Completely Randomized Designs
Randomized Blocks Designs
Restricted Randomizations
Constraints on Rerandomization
Implementing Case Rerandomization
Completely Randomized Designs
Randomized Blocks Designs
Independent Randomization of Cases
Restricted Randomization
Exercises
Random Treatment Sequences
Between- and Within-Cases Designs
Randomizing the Sequence of Treatments
Causal Inference for Within-Cases Designs
Sequence Randomization Strategies
Rerandomizing Treatment Sequences
Analysis of the AB-BA Design
Sequences of K ] 2 Treatments
Exercises
Between- and Within-Cases Designs
Between/Within Designs
Between/Within Resampling Strategies
Doubly Randomized Available Cases
Interactions and Simple Effects
Simple and Main Effects
Exercise
Subsamples: Stability of Description
Nonrandom Studies and Data Sets
Local Descriptive Inference
Description Stability and Case Homogeneity
Subsample Descriptions
Employing Subsample Descriptions
Subsamples and Randomized Studies
Structured and Unstructured Data
Half-Samples of Unstructured Data Sets
Subsamples of Source-Structured Cases
Exercises
Resampling Applications
Introduction
A Single Group of Cases
Random Sample or Set of Available Cases
Typical Size of Score Distribution
Variability of Attribute Scores
Association Between Two Attributes
Exercises
Two Independent Groups of Cases
Constitution of Independent Groups
Location Comparisons for Samples
Magnitude Differences, CR and RB Designs
Magnitude Differences, Nonrandom Designs
Study Size
Exercises
Multiple Independent Groups
Multiple Group Parametric Comparisons
Nonparametric K-Group Comparisons
Comparisons among Randomized Groups
Comparisons among Nonrandom Groups
Adjustment for Multiple Comparisons
Exercises
Multiple Factors and Covariates
Two Treatment Factors
Treatment and Blocking Factors
Covariate Adjustment of Treatment Scores
Exercises
Within-Cases Treatment Comparisons
Normal Models, Univariate and Multivariate
Bootstrap Treatment Comparisons
Randomized Sequence of Treatments
Nonrandom Repeated Measures
Exercises
Linear Models: Measured Response
The Parametric Linear Model
Nonparametric Linear Models
Prediction Accuracy
Linear Models for Randomized Cases
Linear Models for Nonrandom Studies
Exercises
Categorical Response Attributes
Cross-Classification of Cases
The 2 [times] 2 Table
Logistic Regression
Exercises
Postscript: Generality, Causality, and Stability
Study Design and Resampling
Resampling Tools
References
Index