Common Errors in Statistics (and How to Avoid Them)

ISBN-10: 1118294394
ISBN-13: 9781118294390
Edition: 4th 2012
List price: $61.95
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Description: The Fourth Edition of this tried-and-true book elaborates on many key topics such as epidemiological studies, distribution of data; baseline data incorporation; case control studies; simulations; statistical theory publication; biplots; instrumental  More...

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Book details

List price: $61.95
Edition: 4th
Copyright year: 2012
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/23/2012
Binding: Paperback
Pages: 352
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.034
Language: English

The Fourth Edition of this tried-and-true book elaborates on many key topics such as epidemiological studies, distribution of data; baseline data incorporation; case control studies; simulations; statistical theory publication; biplots; instrumental variables; ecological regression; result reporting, survival analysis; etc. Including new modifications and figures, the book also covers such topics as research plan creation; data collection; hypothesis formulation and testing; coefficient estimates; sample size specifications; assumption checking; p-values interpretations and confidence intervals; counts and correlated data; model building and testing; Bayes' Theorem; bootstrap and permutation tests; and more.

Preface
Foundations
Sources of Error
Prescription
Fundamental Concepts
Surveys and Long-Term Studies
Ad-Hoc, Post-Hoc Hypotheses
To Learn More
Hypotheses: The Why of Your Research
Prescription
What Is a Hypothesis?
How Precise Must a Hypothesis Be?
Found Data
Null or Nil Hypothesis
Neyman-Pearson Theory
Deduction and Induction
Losses
Decisions
To Learn More
Collecting Data
Preparation
Response Variables
Determining Sample Size
Fundamental Assumptions
Experimental Design
Four Guidelines
Are Experiments Really Necessary?
To Learn More
Statistical Analysis
Data Quality Assessment
Objectives
Review the Sampling Design
Data Review
To Learn More
Estimation
Prevention
Desirable and Not-So-Desirable Estimators
Interval Estimates
Improved Results
Summary
To Learn More
Testing Hypotheses: Choosing a Test Statistic
First Steps
Test Assumptions
Binomial Trials
Categorical Data
Time-To-Event Data (Survival Analysis)
Comparing the Means of Two Sets of Measurements
Do Not Let Your Software Do Your Thinking For You
Comparing Variances
Comparing the Means of K Samples
Higher-Order Experimental Designs
Inferior Tests
Multiple Tests
Before You Draw Conclusions
Induction
Summary
To Learn More
Strengths and Limitations of Some Miscellaneous Statistical Procedures
Nonrandom Samples
Modem Statistical Methods
Bootstrap
Bayesian Methodology
Meta-Analysis
Permutation Tests
To Learn More
Reporting Your Results
Fundamentals
Descriptive Statistics
Ordinal Data
Tables
Standard Error
p-Values
Confidence Intervals
Recognizing and Reporting Biases
Reporting Power
Drawing Conclusions
Publishing Statistical Theory
A Slippery Slope
Summary
To Learn More
Interpreting Reports
With a Grain of Salt
The Authors
Cost-Benefit Analysis
The Samples
Aggregating Data
Experimental Design
Descriptive Statistics
The Analysis
Correlation and Regression
Graphics
Conclusions
Rates and Percentages
Interpreting Computer Printouts
Summary
To Learn More
Graphics
Is a Graph Really Necessary?
KISS
The Soccer Data
Five Rules for Avoiding Bad Graphics
One Rule for Correct Usage of Three-Dimensional Graphics
The Misunderstood and Maligned Pie Chart
Two Rules for Effective Display of Subgroup Information
Two Rules for Text Elements in Graphics
Multidimensional Displays
Choosing Effective Display Elements
Oral Presentations
Summary
To Learn More
Building a Model
Univariate Regression
Model Selection
Stratification
Further Considerations
Summary
To Learn More
Alternate Methods of Regression
Linear Versus Nonlinear Regression
Least-Absolute-Deviation Regression
Quantile Regression
Survival Analysis
The Ecological Fallacy
Nonsense Regression
Reporting the Results
Summary
To Learn More
Multivariable Regression
Caveats
Dynamic Models
Factor Analysis
Reporting Your Results
A Conjecture
Decision Trees
Building a Successful Model
To Learn More
Modeling Counts and Correlated Data
Counts
Binomial Outcomes
Common Sources of Error
Panel Data
Fixed- and Random-Effects Models
Population-Averaged Generalized Estimating Equation Models (GEEs)
Subject-Specific or Population-Averaged?
Variance Estimation
Quick Reference for Popular Panel Estimators
To Learn More
Validation
Objectives
Methods of Validation
Measures of Predictive Success
To Learn More
Glossary
Bibliography
Author Index
Subject Index

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