- Browse
- » Common errors in statistics (and how to avoid them)
Common errors in statistics (and how to avoid them)
Author
Publisher
Varies, see individual formats and editions
Publication Date
Varies, see individual formats and editions
Language
English
Description
Loading Description...
Table of Contents
From the Book - 3rd ed.
Pt. I. Foundations
1. Sources of error
2. Hypotheses : the why of your research
3. Collecting data
Pt. II. Statistical analysis
4. Data quality assessment
5. Estimation
6. Testing hypotheses : choosing a test statistic
7. Miscellaneous statistical practices
Pt. III. Reports
8. Reporting your results
9. Interpreting reports
10. Graphics
Pt. IV. Building a model
11. Univariate regression
12. Alternate methods of regression
13. Multivariable regression
14. Modeling correlated data
15. Validation.
From the Book - 2nd ed.
Preface
pt. 1. Foundations
1. Sources of error
Prescription
Fundamental concepts
Ad hoc, post hoc hypotheses
2. Hypotheses : the why of your research
Prescription
What is a hypothesis?
How precise must a hypothesis be?
Found data
Null hypothesis
Neyman-Pearson theory
Deduction and induction
Losses
Decisions
To learn more
3. Collecting data
Preparation
Measuring devices
Determining sample size
Fundamental assumptions
Experimental design
Four guidelines
Are experiments really necessary?
To learn more
pt. 2. Hypothesis testing and estimation
4. Estimation
Prevention
Desirable and not-so-desirable estimators
Interval estimates
Improved results
Summary
To learn more
5. Testing hypotheses : choosing a test statistic
Comparing means of two populations
Comparing variances
Comparing the means of K samples
Higher-order experimental designs
Contingency tables
Inferior tests
Multiple tests
Before you draw conclusions
Summary
To learn more
6. Strengths and limitations of some miscellaneous statistical procedures
Bootstrap
Bayesian methodology
Meta-analysis
Permutation tests
To learn more
7. Reporting your results
Fundamentals
Tables
Standard error
p-values
Confidence intervals
Recognizing and reporting biases
Reporting power
Drawing conclusions
Summary
To learn more
8. Interpreting reports
With a grain of salt
Rates and percentages
Interpreting computer printouts
9. Graphics
The soccer data
Five rules for avoiding bad graphics
One rule for correct usage of three-dimensional graphics
One rule for the misunderstood pie chart
Two rules for effective display of subgroup information
Two rules for text elements in graphics
Multidimensional displays
Choosing effective display elements
Choosing graphical displays
Summary
To learn more
pt. 3. Building a model
10. Univariate regression
Model selection
Estimating coefficients
Further considerations
Summary
To learn more
11. Alternate methods of regression
Linear vs. Nonlinear regression
Least absolute deviation regression
Errors-in-variables regression
Quantile regression
The ecological fallacy
Nonsense regression
Summary
To learn more
12. Multivariable regression
Caveats
Factor analysis
Generalized linear models
Reporting your results
A conjecture
Building a successful model
To learn more
13. Validation
Methods of validation
Measures of predictive success
Long-term stability
To learn more
appendix A. A note on screening regression equations
appendix B. Cross-validation, the jackknife, and the bootstrap : excess error estimation in forward logistic regression
Glossary, grouped by related but distinct terms
Bibliography
Author index
Subject index.
From the Book
I: Foundations
1. Sources of error
2. Hypotheses: the why of your research
3. Collecting data
II: Hypothesis testing and estimation
4. Estimation
5. Testing hypotheses: choosing a test statistic
6. Strengths and limitations of some miscellaneous statistical procedures
7. Reporting your results
8. Graphics
III: Building a model
9. Univariate regression
10. Multivariable regression
11. Validation.
From the eBook - Fourth edition.
Common Errors in Statistics (and How to Avoid Them); Contents; Preface; Part I: FOUNDATIONS; Chapter 1: Sources of Error; PRESCRIPTION; FUNDAMENTAL CONCEPTS; SURVEYS AND LONG-TERM STUDIES; AD-HOC, POST-HOC HYPOTHESES; TO LEARN MORE; Chapter 2: 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; Chapter 3: Collecting Data; PREPARATION; RESPONSE VARIABLES; DETERMINING SAMPLE SIZE; FUNDAMENTAL ASSUMPTIONS.
Excerpt
Loading Excerpt...
Author Notes
Loading Author Notes...
More Details
Contributors
ISBN
9780471794318
9781118360095
9781118294390
9781118211274
9780471460688
9781118360132
9781280699399
9780470635438
9781118360125
9780470457986
9781118360118
9781118360095
9781118294390
9781118211274
9780471460688
9781118360132
9781280699399
9780470635438
9781118360125
9780470457986
9781118360118
UPC
Staff View
Loading Staff View.

