The Statistical Sleuth: A Course in Methods of Data Analysis 3rd Edition by Fred Ramsey, ISBN-13: 978-1133490678

Description

The Statistical Sleuth: A Course in Methods of Data Analysis 3rd Edition by Fred Ramsey, ISBN-13: 978-1133490678

[PDF eBook eTextbook]

• Publisher: ‎ Cengage Learning; 3rd edition (May 2, 2012)
• Language: ‎ English
• 784 pages
• ISBN-10: ‎ 1133490670
• ISBN-13: ‎ 978-1133490678

THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANALYSIS, Third Edition offers an appealing treatment of general statistical methods that takes full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package, and prominently treats modeling and interpretation in a way that goes beyond routine patterns. The book focuses on a serious analysis of real case studies, strategies and tools of modern statistical data analysis, the interplay of statistics and scientific learning, and the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get readers excited about statistics.

Table of Contents:

Title

Statement

Copyright

Dedications

Contents

Preface

Ch 1: Drawing Statistical Conclusions

Introduction

1.1 Case Studies

1.2 Statistical Inference and Study Design

1.3 Measuring Uncertainty in Randomized Experiments

1.4 Measuring Uncertainty in Observational Studies

1.5 Related Issues

1.6 Summary

1.7 Exercises

Ch 2: Inference Using t-Distributions

Introduction

2.1 Case Studies

2.2 One-Sample t-Tools and the Paired t-Test

2.3 A t-Ratio for Two-Sample Inference

2.4 Inferences in a Two-Treatment Randomized Experiment

2.5 Related Issues

2.6 Summary

2.7 Exercises

Ch 3: A Closer Look at Assumptions

Introduction

3.1 Case Studies

3.2 Robustness of the Two-Sample t-Tools

3.3 Resistance of the Two-Sample t-Tools

3.4 Practical Strategies for the Two-Sample Problem

3.5 Transformations of the Data

3.6 Related Issues

3.7 Summary

3.8 Exercises

Ch 4: Alternatives to the t-Tools

Introduction

4.1 Case Studies

4.2 The Rank-Sum Test

4.3 Other Alternatives for Two Independent Samples

4.4 Alternatives for Paired Data

4.5 Related Issues

4.6 Summary

4.7 Exercises

Ch 5: Comparisons Among Several Samples

Introduction

5.1 Case Studies

5.2 Comparing Any Two of the Several Means

5.3 The One-Way Analysis of Variance F-Test

5.4 More Applications of the Extra-Sum-of-Squares F-Test

5.5 Robustness and Model Checking

5.6 Related Issues

5.7 Summary

5.8 Exercises

Ch 6: Linear Combinations and Multiple Comparisons of Means

Introduction

6.1 Case Studies

6.2 Inferences About Linear Combinations of Group Means

6.3 Simultaneous Inferences

6.4 Some Multiple Comparison Procedures

6.5 Related Issues

6.6 Summary

6.7 Exercises

Ch 7: Simple Linear Regression A Model for the Mean

Introduction

7.1 Case Studies

7.2 The Simple Linear Regression Model

7.3 Least Squares Regression Estimation

7.4 Inferential Tools

7.5 Related Issues

7.6 Summary

7.7 Exercises

Ch 8: A Closer Look at Assumptions for Simple Linear Regression

Introduction

8.1 Case Studies

8.2 Robustness of Least Squares Inferences

8.3 Graphical Tools for Model Assessment

8.4 Interpretation After Log Transformations

8.5 Assessment of Fit Using the Analysis of Variance

8.6 Related Issues

8.7 Summary

8.8 Exercises

Ch 9: Multiple Regression

Introduction

9.1 Case Studies

9.2 Regression Coefficients

9.3 Specially Constructed Explanatory Variables

9.4 A Strategy for Data Analysis

9.5 Graphical Methods for Data Exploration and Presentation

9.6 Related Issues

9.7 Summary

9.8 Exercises

Ch 10: Inferential Tools for Multiple Regression

Introduction

10.1 Case Studies

10.2 Inferences About Regression Coefficients

10.3 Extra-Sums-of-Squares F-Tests

10.4 Related Issues

10.5 Summary

10.6 Exercises

Ch 11: Model Checking and Refinement

Introduction

11.1 Case Studies

11.2 Residual Plots

11.3 A Strategy for Dealing with Influential Observations

11.4 Case-Influence Statistics

11.5 Refining the Model

11.6 Related Issues

11.7 Summary

11.8 Exercises

Ch 12: Strategies for Variable Selection

Introduction

12.1 Case Studies

12.2 Specific Issues Relating to Many Explanatory Variables

12.3 Sequential Variable-Selection Techniques

12.4 Model Selection Among All Subsets

12.5 Posterior Beliefs About Different Models

12.6 Analysis of the Sex Discrimination Data

12.7 Related Issues

12.8 Summary

12.9 Exercises

Ch 13: The Analysis of Variance for Two-Way Classifications

Introduction

13.1 Case Studies

13.2 Additive and Nonadditive Models for Two-Way Tables

13.3 Analysis of the Seaweed Grazer Data

13.4 Analysis of the Pygmalion Data

13.5 Related Issues

13.6 Summary

13.7 Exercises

Ch 14: Multifactor Studies Without Replication

Introduction

14.1 Case Studies

14.2 Strategies for Analyzing Tables with One Observation per Cell

14.3 Analysis of the Chimpanzee Learning Times Study

14.4 Analysis of the Soybean Data

14.5 Related Issues

14.6 Summary

14.7 Exercises

Ch 15: Adjustment for Serial Correlation

Introduction

15.1 Case Studies

15.2 Comparing the Means of Two Time Series

15.3 Regression After Transformation in the AR(1) Model

15.4 Determining if Serial Correlation is Present

15.5 Diagnostic Procedures for Judging the Adequacy of the AR(1) Model

15.6 Related Issues

15.7 Summary

15.8 Exercises

Ch 16: Repeated Measures and Other Multivariate Responses

Introduction

16.1 Case Studies

16.2 Tools and Strategies for Analyzing Repeated Measures

16.3 Comparing the Means of Bivariate Responses in Two Groups

16.4 One-Sample Analysis with Bivariate Responses

16.5 Related Issues

16.6 Summary

16.7 Exercises

Ch 17: Exploratory Tools for Summarizing Multivariate Responses

Introduction

17.1 Case Studies

17.2 Linear Combinations of Variables

17.3 Principal Components Analysis

17.4 Canonical Correlations Analysis

17.5 Introduction to Other Multivariate Tools

17.6 Summary

17.7 Exercises

Ch 18: Comparisons of Proportions or Odds

Introduction

18.1 Case Studies

18.2 Inferences for the Difference of Two Proportions

18.3 Inference About the Ratio of Two Odds

18.4 Inference from Retrospective Studies

18.5 Summary

18.6 Exercises

Ch 19: More Tools for Tables of Counts

Introduction

19.1 Case Studies

19.2 Population Models for 2 x 2 Tables of Counts

19.3 The Chi-Squared Test

19.4 Fisher’s Exact Test The Randomization (Permutation) Test for 2 x 2 Tables

19.5 Combining Results from Several Tables with Equal Odds Ratios

19.6 Related Issues

19.7 Summary

19.8 Exercises

Ch 20: Logistic Regression for Binary Response Variables

Introduction

20.1 Case Studies

20.2 The Logistic Regression Model

20.3 Estimation of Logistic Regression Coefficients

20.4 The Drop-in-Deviance Test

20.5 Strategies for Data Analysis Using Logistic Regression

20.6 Analyses of Case Studies

20.7 Related Issues

20.8 Summary

20.9 Exercises

Ch 21: Logistic Regression for Binomial Counts

Introduction

21.1 Case Studies

21.2 Logistic Regression for Binomial Responses

21.3 Model Assessment

21.4 Inferences About Logistic Regression Coefficients

21.5 Extra-Binomial Variation

21.6 Analysis of Moth Predation Data

21.7 Related Issues

21.8 Summary

21.9 Exercises

Ch 22: Log-Linear Regression for Poisson Counts

Introduction

22.1 Case Studies

22.2 Log-Linear Regression for Poisson Responses

22.3 Model Assessment

22.4 Inferences About Log-Linear Regression Coefficients

22.5 Extra-Poisson Variation and the Log-Linear Model

22.6 Related Issues

22.7 Summary

22.8 Exercises

Ch 23: Elements of Research Design

Introduction

23.1 Case Study

23.2 Considerations in Forming Research Objectives

23.3 Research Design Tool Kit

23.4 Design Choices That Affect Accuracy and Precision

23.5 Choosing a Sample Size

23.6 Steps in Designing a Study

23.7 Related Issue—A Factor of 4

23.8 Summary

23.9 Exercises

Ch 24: Factorial Treatment Arrangements and Blocking Designs

Introduction

24.1 Case Study

24.2 Treatments

24.3 Factorial Arrangement of Treatment Levels

24.4 Blocking

24.5 Summary

24.6 Exercises

Appendix Bibliography

Index

Fred Ramsey received his undergraduate degree from the University of Oregon (1961) and graduate degrees from Iowa State University (1963, 1964). He completed post-doctorate work at Johns Hopkins University. He has been on the faculty of the Department of Statistics at Oregon State University since 1966, with leaves for teaching and research positions at the University of Copenhagen, Denmark (1972-1973); Murdoch University, Perth, Western Australia (1997-1978); the University of Wollongong, NSW, Australia (1985-1986); and Oregon Health Sciences University in Portland, Oregon (1990-1991). His principal research interest is applications of statistics to wildlife problems.

Daniel Schafer holds an undergraduate degree in Mathematics from Pomona College (1978) and graduate degrees in Statistics from the University of Chicago (1981, 1982). He is currently a professor of statistics at Oregon State University. His hobby is wildlife photography.

What makes us different?

• Instant Download

• Always Competitive Pricing

• 100% Privacy

• FREE Sample Available

• 24-7 LIVE Customer Support