Primer of Applied Regression & Analysis of Variance

Book Description

Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA).

Customer Reviews:

Outstanding.......2006-01-31

I looked at several options for a regression textbook that would be both understandable and relatively complete for my introduction to the topic. This book won hands down. The authors keep it simple and use a wide variety of examples to get the point across. I felt that the sections on logistic and Cox regression could have been a bit better, but these subjects are best learned by dedicated textbooks such as Hosmer and Lemeshow and Collett.

I think that this may be the best introductory regression book out there.

The best second book of statistics for biologists........2000-11-13

Once you've learned the basic principles of statistics, how can a biologist learn more advanced techniques? Many books focus on math rather than on understanding concepts. Other books are too narrow -- discussing only a single method. And books that focus on multiple regression and ANOVA tend to have examples from psychology and social sciences. Glantz and Slinker do a great job of explaining the principles of multiple regression, analysis of variance, and analysis of covariance. The focus is not on mathematical proofs, but rather on making sense of the results in the context of biological and medical research.

This book also has excellent chapters on linear regression, nonlinear regression (curve fitting) and logistic and proportional hazards regression (regression when the outcome is an either-or binary variable).

New to the second edition are a chapter on analysis of covariance, more extensive discussions of multiple comparisons methods, and a discussion of Cox proportional hazards regression for analyses of survival data.

The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book for biologists to learn about various kinds of ANOVAS and regressions.

The best advanced statistics book for biologists.......1998-05-30

Like all advanced stats books, this one has mathematical rigor and plenty of examples. But unlike the others, this one is written from the point of view of a biologist. You won't just learn the math, you'll learn how to make sense of the results. The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book to learn about various kinds of ANOVAS and regressions.

Average customer rating:

Best stats book out there without the errors of the 6th edition
Statistics in plane english.
good material, too many errors
Non-mathematical introduction to biostatistics
Lots of mistakes in this book

Primer of Biostatistics
Stanton A. Glantz
Manufacturer: McGraw-Hill Medical
ProductGroup: Book
Binding: Paperback

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ASIN: 0071435093

Book Description

Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author’s unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.

Customer Reviews:

Best stats book out there without the errors of the 6th edition.......2007-05-07

The quickest, most accurate stats book out there. Warning: you must read it carefully, you can't really skim. That said, if you do read it carefully, even just the first few chapters, you will truly understand basic stats. Before reading this I didn't get significance or what a t-test really meant. Now I can speak the lingo and critically assess other authors work. Just a great book if you give it a few hours to sink in.
Get the 5th edition, not the 6th, for some reason the 6th is full of typos and errors in the examples.

Statistics in plane english........2006-07-05

If you can not explaine it in plane english you probably don't undestand it yourself. In this book the author present a clear explanation of this technical field in plane english.

good material, too many errors.......2006-05-02

It's a decent book overall. The material is not too hard to underestand, and there are a lot of medical examples. However, there's nearly a typo on every other page. I wouldn't mind if the mistakes were minor, but it's too much when a third of the solutions are wrong, and statistical tables are incorrect (e.g. 6-8 & 6-9).

Non-mathematical introduction to biostatistics.......2006-03-30

This is not a treatise in mathematics. The author uses easy to understand examples to explain concepts. Other reviewers have noted typos that altered their learning experience; I have not. Other reviewers report that the presentation of ANOVA is not easily understood; I disagree. Statisticians that are used to the Fisher and Scheffé methods of explaining ANOVA will clearly find this different. I actually found it useful to present ANOVA before the T-test and to use the examples he did. Glantz's text on regression and ANOVA is also very good.

Lots of mistakes in this book.......2003-11-13

The concept of this book is good-cut to the chase and give lots of relevent examples. However, the book has innumerable typos which can make it very dificult to follow the text. Some sections are just incomprehensible such as the section on power calculations for the chi square test. Most of the exercises at the end of the chapter are heavy on arithmetic and light on conceptual issues. Often the answers to the exercises are wrong. All these typos are really unacceptable for a fifth edition. I am at Amazon looking for another text to keep as a reference. By way of disclaimer, I am taking the author's course on introductory biostatistics and am doing average.

Basic Statistics: A Primer for the Biomedical Sciences

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Basic Statistics: A Primer for the Biomedical Sciences
Olive Jean Dunn , and Virginia A. Clark
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471354228

Book Description

An introduction to the use of statistics in biomedical research-now fully updated for the information age

Since the second edition of Basic Statistics was published more than two decades ago, there have been significant changes in the way statistics is applied in the biomedical fields. To provide relevant, up-to-date coverage of biostatistics for professionals in public health, medicine, and other biomedical fields, this popular primer is now thoroughly revised. The new edition emphasizes biomedical research methodology and clearly explains the design, graphical methods, and exploratory data analysis as used in contemporary studies. Through short, concise, nontechnical presentations (high-school algebra is the only requirement!), readers will learn to interpret and examine data from biomedical studies and apply such tools as sampling, random assignment, and biostatistical analyses in their own research. Basic Statistics, Third Edition:

* Describes major types of biomedical studies and how to design them

* Discusses the samples used in different types of biomedical studies

* Emphasizes the use of confidence intervals in data analysis

* Offers a new introduction to survival analysis, including clinical

life tables

* Substantiates with examples some of the major statistical theorems

* Adds new graphs, figures, problem sets, and bibliographic references

* Discusses using statistical computer programs in data analysis

Biostatistics and Epidemiology: A Primer for Health and Biomedical Professionals

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Biostatistics and Epidemiology: A Primer for Health and Biomedical Professionals
Sylvia Wassertheil-Smoller
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Accessories:

ASIN: 0387402926

Book Description

Contents
Preface To The Third Edition
Acknowledgments

Chapter 1. The Scientific Method
1.1 The Logic of Scientific Reasoning
1.2 Variability of Phenomena Requires Statistical Analysis
1.3 Inductive Inference: Statistics as the Technology of the Scientific Method
1.4 Design of Studies
1.5 How to Quantify Variables
1.6 The Null Hypothesis
1.7 Why Do We Test the Null Hypothesis?
1.8 Types of Errors
1.9 Significance Level and Types of Error
1.10 Consequences of Type I and Type II Errors

Chapter 2. A Little Bit Of Probability
2.1 What Is Probability?
2.2 Combining Probabilities
2.3 Conditional Probability
2.4 Bayesian Probability
2.5 Odds and Probability
2.6 Likelihood Ratio
2.7 Summary of Probability

Chapter 3. Mostly About Statistics
3.1 Chi-Square for 2 x 2 Tables
3.2 McNemar Test
3.3 Kappa
3.4 Description of a Population: Use of the Standard Deviation
3.5 Meaning of the Standard Deviation: The Normal Distribution
3.6 The Difference Between Standard Deviation and Standard Error
3.7 Standard Error of the Difference Between Two Means
3.8 Z Scores and the Standardized Normal Distribution
3.9 The t Statistic
3.10 Sample Values and Population Values Revisited
3.11 A Question of Confidence
3.12 Confidence Limits and Confidence Intervals
3.13 Degrees of Freedom
3.14 Confidence Intervals for Proportions
3.15 Confidence Intervals Around the Difference Between Two Means
3.16 Comparisons Between Two Groups
3.17 Z-Test for Comparing Two Proportions
3.18 t-Test for the Difference Between Means of Two Independent Groups: Principles
3.19 How to Do a t-Test: An Example
3.20 Matched Pair t-Test
3.21 When Not to Do a Lot of t-Tests: The Problem of Multiple Tests of Significance
3.22 Analysis of Variance: Comparison Among Several Groups
3.23 Principles
3.24 Bonferroni Procedure: An Approach to Making Multiple Comparisons
3.25 Analysis of Variance When There Are Two Independent Variables: The Two-Factor ANOVA
3.26 Interaction Between Two Independent Variables
3.27 Example of a Two-Way ANOVA
3.28 Kruskal-Wallis Test to Compare Several Groups
3.29 Association and Causation: The Correlation Coefficient
3.30 How High Is High?
3.31 Causal Pathways
3.32 Regression
3.33 The Connection Between Linear Regression and the Correlation Coefficient
3.34 Multiple Linear Regression
3.35 Summary So Far

Chapter 4. Mostly About Epidemiology
4.1 The Uses of Epidemiology
4.2 Some Epidemiologic Concepts: Mortality Rates
4.3 Age-Adjusted Rates
4.4 Incidence and Prevalence Rates
4.5 Standardized Mortality Ratio
4.6 Person-Years of Observation
4.7 Dependent and Independent Variables
4.8 Types of Studies
4.9 Cross-Sectional Versus Longitudinal Looks at Data
4.10 Measures of Relative Risk: Inferences From Prospective Studies: the Framingham Study
4.11 Calculation of Relative Risk from Prospective Studies
4.12 Odds Ratio: Estimate of Relative Risk from Case-Control Studies
4.13 Attributable Risk
4.14 Response Bias
4.15 Confounding Variables
4.16 Matching
4.17 Multiple Logistic Regression
4.18 Confounding By Indication
4.19 Survival Analysis: Life Table Methods
4.20 Cox Proportional Hazards Model
4.21 Selecting Variables For Multivariate Models
4.22 Interactions: Additive and Multiplicative Models
Summary:

Chapter 5. Mostly About Screening
5.1 Sensitivity, Specificity, and Related Concepts
5.2 Cutoff Point and Its Effects on Sensitivity and Specificity

Chapter 6. Mostly About Clinical Trials
6.1 Features of Randomized Clinical Trials
6.2 Purposes of Randomization
6.3 How to Perform Randomized Assignment
6.4 Two-Tailed Tests Versus One-Tailed Test
6.5 Clinical Trial as "Gold Standard"
6.6 Regression Toward the Mean
6.7 Intention-to-Treat Analysis
6.8 How Large Should the Clinical Trial Be?
6.9 What Is Involved in Sample Size Calculation?
6.10 How to Calculate Sample Size for the Difference Between Two Proportions
6.11 How to Calculate Sample Size for Testing the Difference Between Two Means

Chapter 7. Mostly About Quality Of Life
7.1 Scale Construction
7.2 Reliability
7.3 Validity
7.4 Responsiveness
7.5 Some Potential Pitfalls

Chapter 8. Mostly About Genetic Epidemiology
8.1 A New Scientific Era
8.2 Overview of Genetic Epidemiology
8.3 Twin Studies
8.4 Linkage and Association Studies
8.5 LOD Score: Linkage Statistic
8.6 Association Studies
8.7 Transmission Disequilibrium Tests (TDT)
8.8 Some Additional Concepts and Complexities of Genetic Studies

Chapter 9. Research Ethics And Statistics
9.1 What does statistics have to do with it?
9.2 Protection of Human Research Subjects
9.3 Informed Consent
9.4 Equipoise
9.5 Research Integrity
9.6 Authorship policies
9.7 Data and Safety Monitoring Boards
9.8 Summary

Postscript A Few Parting Comments On The Impact Of Epidemiology On Human Lives
Appendix A. Critical Values Of Chi-square, Z, And T
Appendix B. Fisher'S Exact Test
Appendix C. Kruskal-wallis Nonparametric Test To Compare Several Groups
Appendix D. How To Calculate A Correlation Coefficient
Appendix E. Age-adjustment
Appendix F. Confidence Limits On Odds Ratios
Appendix G. "J" Or "U" Shaped Relationship Between Two Variables
Appendix H. Determining Appropriateness Of Change Scores
Appendix I. Genetic Principles
References
Suggested Readings
Index

Statistical Ecology: A Primer in Methods and Computing

Average customer rating:

Statistical Ecology, Tremendous Text, Poor Software

Statistical Ecology: A Primer in Methods and Computing
John A. Ludwig , and James F. Reynolds
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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A Primer Of Ecological Statistics

ASIN: 0471832359

Book Description

An accessible introduction to quantitative methods in community ecology, with related software. Provides a thoughtful overview on the nature of data collection, followed by detailed explanations of the major statistical techniques applied to problems in population dynamics. Presents important conclusions about the use (and abuse) of specific techniques. Examples include communities of both flora and fauna, prey-predator relations, the effects of food supply on populations, and species abundance and overlap models. Software includes several BASIC microcomputer programs which can be used with the examples provided or with other data sets.

Customer Reviews:

Statistical Ecology, Tremendous Text, Poor Software.......1999-01-15

Statistical Ecology was easy to interpret. Examples made this book excellent.

The book comes with a disk containing BASIC programs. This is of little help for individuals having win 95/98. The BASIC interpreter, mentioned in the primer, was terminated when Windows 95 replaced MSDOS as the OS for PCs.

So if you have win95/98 you are BASICally hosed.

Book Description

Through careful, deliberate thought this book provides the nonmathematician with a foundation for understanding the underlying statistical reasoning process in clinical research. This volume recognizes the inevitable tension between the mathematics of hypothesis testing and the ethical requirements in medical research and concentrates on the resolution of these issues in P value interpretation. By presenting the material in a conversational format and consciously de-emphasizing computational devices and focusing instead on the features of experimental design that either clarify or blur P value interpretation statistical reasoning becomes extremely accessible to the uninitiated.

Average customer rating:

Proceed with Caution
Emphasis on PRIMER
A good introduction to random number simulation.
An excelent primer
Interesting, broad-gauge, practically oriented

A Primer for the Monte Carlo Method
Ilya M. Sobol
Manufacturer: CRC
ProductGroup: Book
Binding: Paperback

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ASIN: 084938673X

Book Description

The Monte Carlo method is a numerical method of solving mathematical problems through random sampling. As a universal numerical technique, the method became possible only with the advent of computers, and its application continues to expand with each new computer generation. A Primer for the Monte Carlo Method demonstrates how practical problems in science, industry, and trade can be solved using this method. The book features the main schemes of the Monte Carlo method and presents various examples of its application, including queueing, quality and reliability estimations, neutron transport, astrophysics, and numerical analysis. The only prerequisite to using the book is an understanding of elementary calculus.

Customer Reviews:

Proceed with Caution.......2001-06-30

This is a fine enough introduction to Monte Carlo scenarios, but only for the highly technically proficient reader. The mathematics is quite sophisticated and probably out of reach for those who want to understand Monte Carlo methods rather than apply them. It is also a very thin book for a very high price.

Emphasis on PRIMER.......1999-01-05

It is generally a very elementary book, and does not go into more advanced methods (for example, Sobol's low discrepancy series). It does give a number of very good examples of problems that can be tackled by Monte Carlo Methods. If you want a few pages on what Monte Carlo Methods can do,along with a little of bit decent theory, you may want to try this book. However, I am not sure that a person serious enough to spend the money on this book will be enlightened.

A good introduction to random number simulation........1998-12-23

I enjoyed (and enjoying) this book. However, I gave it a three due to it being $54.00 for 100 pages. A little overpriced.

An excelent primer.......1997-11-11

A easy to follow introduction to various concept related to (pseudo / quasi) random numbers, and their applications.

Interesting, broad-gauge, practically oriented.......1997-06-25

Prof. Sobol's neatly presented treatment of the principles underlying static, or Monte Carlo, simulations is an excellent introduction to the topic and will be appreciated by two distinct audiences. First, students early in their study of simulation can approach the topic aided by Sobol's clarity and frequent use of simple, concrete examples. Second, engineers (and other mathematically adept readers) who generally do not rely on probabilistic techniques for analysis can grasp the basics quickly and be reassured of the mathematical rigor which drives the technique. The most valuable part of the book is the concise development of the generation of pseudorandom numbers. Additionally, Sobol's little book gives some interesting insights into Russian practice, contributions and implementations of the Monte Carlo technique.

The Interpretation of Ecological Data: A Primer on Classification and Ordination

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The Interpretation of Ecological Data: A Primer on Classification and Ordination
E. C. Pielou
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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Multivariate Statistics for Wildlife and Ecology Research

ASIN: 0471889504

Book Description

A detailed introduction to the methods used by ecologists--classification and ordination--to clarify and interpret large, unwieldy masses of multivariate field data. Permits ecologists to understand, not just mechanically use, pre-packaged programs for multivariate analysis. Demonstrates these techniques using artificial data simple enough for every analytical step to be understood.

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Primer of Epidemiology
Gary D. Friedman
Manufacturer: McGraw-Hill Professional Publishing
ProductGroup: Book
Binding: Paperback

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ASIN: 0070224544

Book Description

Written for physicians, medical students, and other health care professionals, this concise textbook explains epidemiologic concepts clearly and simply. It bridges the gap between the clinician and the epidemiologist by providing a number of clinical examples, and explains the epidemiologic emphasis on the study of groups rather than individuals.

Book Description

Lowers the Learning Curve for Physicians and Researchers!

The successful Statistical Reasoning in Medicine: The Intuitive P-value Primer, with its novel emphasis on patient and community protection, illustrated the correct use of statistics in health care research for healthcare workers. Through clear explanations and examples, this book provided the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinical research, the core principles of research design, and the correct use of statistical inference and p-values.

The P-Value Primer 2nd Edition levels the learning curve of statistics for health care researchers by further de-emphasizing mathematical and computational devices, bringing the principles of statistical reasoning closer to the uninitiated. Adding to the updated discussions of research design, hypothesis testing, regression analysis, and Bayes procedures, are new discussions of absolute and relative risk, as well as a lucid description of the number needed to treat (NNT). The multiple analysis issue is clearly defined, and a new description of the correct use and interpretation of combined endpoints in health care research is offered in an easily digestible format.

The P-value Primer 2nd Edition demolishes other obstacles that have impeded a clear understanding of the application of statistics in medicine. The intertwined roles of epidemiology and biostatistics are depicted. In addition to a description of the non-technical history of statistics, a new discussion describes the active cultural forces that have historically argued against the use of probability and statistics, placing the current applications and controversies involving p-values in context. New illustrations of the difficulties physicians and health care providers face in research are offered, and the differences between research skills and statistical skills are distinguished. New discussion describing the process of scientific reasoning, p-values, and the law is included. All of this nonstandard content, so essential for a well rounded perspective on the modern use of statistics in medicine, makes this volume unique among introductory statistics books.

New figures, conversation, and illustrations fortify each chapter. In addition, three new appendices have been added on the normal distribution, sample size computations, and new requirements for the use of statistics in the courtroom.

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