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Introduction to Statistics Through Resampling Methods and R/S-Plus

Phillip I. Good
Publisher: 
Jphn Wiley
Publication Date: 
2005
Number of Pages: 
229
Format: 
Paperback
Price: 
59.95
ISBN: 
0-471-71575-1
Category: 
Textbook
[Reviewed by
Ita Cirovic Donev
, on
01/3/2006
]

Introduction to Statistics through Resampling Methods and R/S-Plus is a little "To Go" book tightly packed to provide a reader with the basics of statistical methods via resampling methods. The first part of the book is an introduction to statistics describing basic data summary statistics, the concept of probability, and distributions. Estimation, testing statistical hypothesis and model building comprise the rest of the book. Statistical concepts (such as hypothesis testing, etc.) are not explained from theoretical point of view at all, but rather through examples and author's "dialogue" with the reader. Even the concept of resampling is not explained to some technical degree of "statistical significance". The book is written in a narrative style and is not technical at all. There are some equations but compared to most introductory statistics texts this can be classified almost as a general reading.

As far as prerequisites for reading the book, high school algebra is enough. Some knowledge of R would be an advantage, and of course an interest in statistics is crucial. An adequate introduction to R and S-Plus can be found in the programs' help files. These should be enough to get acquainted with the basics, which is enough to get started with this book. An S-Plus appendix in the book gives a very slim introduction to S-Plus, which I think is not enough to grasp even the main concepts of the software. To enable easier study, the complete R code presented in the book can be downloaded from the author's web site.

This book is very similar a "trial and error" type of task, where the reader is encouraged to try for oneself the examples in R. The reader is also encouraged to gather some samples on his own and try the methods presented. Exercises form a major part of the book; they are scattered throughout the text. Most of the exercises are computer based, i.e. they should be solved with the aid of R.

I would recommend this book to readers new to statistics, practitioners who lack the basics of statistical estimation and hypothesis testing, and students who need a side reference to help them in their more concrete study of statistical methods.


Ita Cirovic Donev is a PhD candidate at the University of Zagreb. She hold a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical mehods of credit and market risk. Apart from the academic work she does consulting work for financial institutions.

 

Preface.

1. Variation.

1.1 Variation.

1.2. Collecting Data.

1.3. Summarizing Your Data.

1.4. Types of Data.

1.5. Reporting Your Results.

1.6. Measures of Location.

1.7. Samples and Populations.

1.8. Variation— Within and Between.

1.9. Summary and Review.

2. Probability.

2.1. Probability.

2.2. Binomial.

2.3. Condition Probability.

2.4. Independence.

2.5. Applications to Genetics.

2.6. Summary and Review.

3. Distributions.

3.1. Distribution of Values.

3.2. Discrete Distributions.

3.3. Continuous Distributions.

3.4. Properties of Independence Observations.

3.5. Testing A Hypothesis.

3.6. Estimating Effect Size.

3.7 Summary and Review.

4. Testing Hypotheses.

4.1. One-Sample Problems.

4.2. Comparing Two Samples.

4.3. Which Test Should e Use?

4.4. Summary and Review.

5. Designing an Experiment or Survey.

5.1. The Hawthorne Effect.

5.2. Designing an Experiment or Survey.

5.3. How Large a Sample.

5.4. Meta-Analysis.

5.5. Summary and Review.

6. Analyzing Complex Experiments.

6.1. Changes Measured in Percentages.

6.2. Comparing More Than Two Samples.

6.3. Equalizing Variances.

6.4. Categorical Data.

6.5. Multivariate Analysis.

6.6. Summary and Review.

7. Developing Models.

7.1. Models.

7.2. Regression.

7.3. Fitting a Regression Equation.

7.4. Problems with Regression.

7.5 Quantile Regression.

7.6. Validation.

7.7 Classification and Regression Trees.

7.8 Summary and Review.

8. Reporting Your Findings.

8.1. What to Report.

8.2. Text, Tables, of Graph?

8.3. Summarizing Your Results.

8.4 Reporting Analysis Results.

8.5 Exceptions are the Real Story.

9. Problem Solving.

9.1. Real Life Problems.

9.2. Problem Sets.

9.3. Solutions.

Appendix: S-PLUS.

Answers to Selected Exercises.

Subject Index.

Index to R Functions.

Dummy View - NOT TO BE DELETED