Statistics for Research, 3rd Edition
"Statistics for Research has other fine qualities besides superior organization. The examples and the statistical methods are laid out with unusual clarity by the simple device of using special formats for each. The book was written with great care and is extremely user-friendly."—The UMAP Journal
Although the goals and procedures of statistical research have changed little since the Second Edition of Statistics for Research was published, the almost universal availability of personal computers and statistical computing application packages have made it possible for today's statisticians to do more in less time than ever before.
The Third Edition of this bestselling text reflects how the changes in the computing environment have transformed the way statistical analyses are performed today. Based on extensive input from university statistics departments throughout the country, the authors have made several important and timely revisions, including:
- Additional material on probability appears early in the text
- New sections on odds ratios, ratio and difference estimations, repeated measure analysis, and logistic regression
- New examples and exercises, many from the field of the health sciences
- Printouts of computer analyses on all complex procedures
- An accompanying Web site illustrating how to use SAS® and JMP® for all procedures
The text features the most commonly used statistical techniques for the analysis of research data. As in the earlier editions, emphasis is placed on how to select the proper statistical procedure and how to interpret results. Whenever possible, to avoid using the computer as a "black box" that performs a mysterious process on the data, actual computational procedures are also given.
A must for scientists who analyze data, professionals and researchers who need a self-teaching text, and graduate students in statistical methods, Statistics for Research, Third Edition brings the methodology up to date in a very practical and accessible way.
Preface to the Second Edition.
Preface to the First Edition.
1. The Role of Statistics.
1.1 The Basic Statistical Procedure.
1.2 The Scientific Method.
1.3 Experimental Data and Survey Data.
1.4 Computer Usage.
2. Populations, Samples, and Probability Distributions.
2.1 Populations and Samples.
2.2 Random Sampling.
2.3 Levels of Measurement.
2.4 Random Variables and Probability Distributions.
2.5 Expected Value and Variance of a Probability Distribution.
3. Binomial Distributions.
3.1 The Nature of Binomial Distributions.
3.2 Testing Hypotheses.
3.4 Nonparametric Statistics: Median Test.
4. Poisson Distributions.
4.1 The Nature of Poisson Distributions.
4.2 Testing Hypotheses.
4.4 Poisson Distributions and Binomial Distributions.
5. Chi-Square Distributions.
5.1 The Nature of Chi-Square Distributions.
5.2 Goodness-of-Fit Tests.
5.3 Contingency Table Analysis.
5.4 Relative Risks and Odds Ratios.
5.5 Nonparametric Statistics: Median Test for Several Samples.
6. Sampling Distribution of Averages.
6.1 Population Mean and Sample Average.
6.2 Population Variance and Sample Variance.
6.3 The Mean and Variance of the Sampling Distribution of Averages.
6.4 Sampling Without Replacement.
7. Normal Distributions.
7.1 The Standard Normal Distribution.
7.2 Inference From a Single Observation.
7.3 The Central Limit Theorem.
7.4 Inferences About a Population Mean and Variance.
7.5 Using a Normal Distribution to Approximate Other Distributions.
7.6 Nonparametric Statistics: A Test Based on Ranks.
8. Student’s t Distribution.
8.1 The Nature of t Distributions.
8.2 Inference About a Single Mean.
8.3 Inference About Two Means.
8.4 Inference About Two Variances.
8.5 Nonparametric Statistics: Matched-Pair and Two-Sample Rank Tests.
9. Distributions of Two Variables.
9.1 Simple Linear Regression.
9.2 Model Testing.
9.3 Inferences Related to Regression.
9.5 Nonparametric Statistics: Rank Correlation.
9.6 Computer Usage.
9.7 Estimating Only One Linear Trend Parameter.
10. Techniques for One-way Analysis of Variance.
10.1 The Additive Model.
10.2 One-Way Analysis-of-Variance Procedure.
10.3 Multiple-Comparison Procedures.
10.4 One-Degree-of-Freedom Comparisons.
10.6 Bonferroni Procedures.
10.7 Nonparametric Statistics: Kruskal–Wallis ANOVA for Ranks.
11. The Analysis-of-Variance Model.
11.1 Random Effects and Fixed Effects.
11.2 Testing the Assumptions for ANOVA.
12. Other Analysis-of-Variance Designs.
12.1 Nested Design.
12.2 Randomized Complete Block Design.
12.3 Latin Square Design.
12.4 a xb Factorial Design.
12.5 a xb xc Factorial Design.
12.6 Split-Plot Design.
12.7 Split Plot with Repeated Measures.
13. Analysis of Covariance.
13.1 Combining Regression with ANOVA.
13.2 One-Way Analysis of Covariance.
13.3 Testing the Assumptions for Analysis of Covariance.
13.4 Multiple-Comparison Procedures.
14. Multiple Regression and Correlation.
14.1 Matrix Procedures.
14.2 ANOVA Procedures for Multiple Regression and Correlation.
14.3 Inferences About Effects of Independent Variables.
14.4 Computer Usage.
14.5 Model Fitting.
14.6 Logarithmic Transformations.
14.7 Polynomial Regression.
14.8 Logistic Regression.
Appendix of Useful Tables.
Answers to Most Odd-Numbered Exercises and All Review Exercises.
STANLEY WEARDEN, PhD, is currently a professor in the Department of Statistics at West Virginia University in Morgantown, West Virginia, where he previously served for four years as Chairman of the Department of Statistics and Computer Science. He earned his PhD in population genetics from Cornell University and also held the position of Fulbright Professor of Statistics at the University of the West Indies.
DANIEL CHILKO, MS, is an Associate Professor of Statistics at West Virginia University and has contributed his expertise to several books in the field. He received his MS from Rutgers University.