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2001 | Buch | 2. Auflage

Descriptive Statistics Kapitel lesen Erstes Kapitel lesen

Resampling Methods

A Practical Guide to Data Analysis

verfasst von: Phillip I. Good

Verlag: Birkhäuser Boston

Enthalten in: Professional Book Archive

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Über dieses Buch

"Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. Good attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well."

— Ron C. Fryxell, Albion College

"...The wealth of the bibliography covers a wide range of disciplines."

---Dr. Dimitris Karlis, Athens University of Economics

This thoroughly revised second edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and research professionals in science, engineering, and technology.

Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.

Topics and Features:

* Offers more practical examples plus an additional chapter dedicated to regression and data mining techniques and their limitations

* Uses resampling approach to introduction statistics

* A practical presentation that covers all three sampling methods: bootstrap, density-estimation, and permutations

* Includes systematic guide to help one select the correct procedure for a particular application

* Detailed coverage of all three statistical methodologies: classification, estimation, and hypothesis testing

* Suitable for classroom use and individual, self-study purposes

* Numerous practical examples using popular computer programs such as SAS®, Stata®, and StatXact®

* Useful appendixes with computer programs and code to develop individualized methods

* Downloadable freeware from author’s website: http://users.oco.net/drphilgood/resamp.htm

With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests. Students, professionals, and researchers will find it a prarticularly useful handbook for modern resampling methods and their applications.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Descriptive Statistics
Abstract
Statistics help you
  • decide what data and how much data to collect
  • analyze your data
  • determine the degree to which you may rely on your findings.
Phillip I. Good
Chapter 2. Testing a Hypothesis
Abstract
In Chapter 1, we learned how to describe a sample and how to use the sample to describe and estimate the parameters of the population from which it was drawn. In this chapter, we learn how to frame hypotheses and alternatives about the population and to test them using the relabeling method. We learn the fundamentals of probability and apply them in practical situations. And we learn how to draw random, representative samples that can be used for testing and estimation.
Phillip I. Good
Chapter 3. Hypothesis Testing
Abstract
In Chapter 2, we learned how to frame a hypothesis and an alternative and to use relabeling to generate a permutation distribution with which to discriminate between the two. In this chapter, you generate the permutation distributions for a variety of one- and two-sample hypotheses.
Phillip I. Good
Chapter 4. When the Distribution Is Known
Abstract
One of the strengths of the hypothesis-testing procedures described in the preceding chapter is that you need to know very little about the underlying population(s) to apply them. But suppose you have full knowledge of the processes that led to the observations in your sample(s), should you still use the same statistical tests? The answer is no, not always, particularly with very small or very large amounts of data. In this chapter, we consider several parameter-based distributions including the binomial (which you were introduced to in Chapter 2), the Poisson, and the normal or Gaussian, along with several other parametric distributions derived from them that are of value in testing location and dispersion.
Phillip I. Good
Chapter 5. Estimation
Abstract
In this chapter, you expand on the knowledge of estimation gained in Chapter 1 to make point and interval estimates of the effects you detected in Chapter 3.
Phillip I. Good
Chapter 6. Power of a Test
Abstract
In Chapter 3, you were introduced to some practical, easily computed tests of hypotheses. But are they the best tests one can use? And are they always appropriate? In this chapter, we consider the assumptions that underlie a statistical test and look at some of a test’s formal properties: its significance level, power, and robustness.
Phillip I. Good
Chapter 7. Categorical Data
Abstract
In many experiments and in almost all surveys, many if not all the results fall into categories rather than being measurable on a continuous or ordinal scale: male vs. female, black vs. Hispanic vs. oriental vs. white, in favor vs. against vs. undecided. The corresponding hypotheses concern proportions: “Blacks are as likely to be Democrats as they are to be Republicans.” Or, “the dominant genotype ‘spotted shell’ occurs with three times the frequency of the recessive.” In this chapter, you learn to test hypotheses like these that concern categorical and ordinal data.
Phillip I. Good
Chapter 8. Experimental Design and Analysis
Abstract
Failing to account for or balance extraneous factors can lead to major errors in interpretation. In this chapter, you learn to block or measure all factors that are under your control and to utilize random assignment to balance the effects of those you cannot. You learn to design experiments to investigate multiple factors simultaneously, thus obtaining the maximum amount of information while using the minimum number of samples.
Phillip I. Good
Chapter 9. Multiple Variables and Multiple Hypotheses
Abstract
The value of an analysis based on simultaneous observations on several variable such as height, weight, blood pressure, and cholesterol level is that it can be used to detect subtle changes that might not be detectable, except with very large, prohibitively expensive samples, were we to consider only one variable at a time.
Phillip I. Good
Chapter 10. Model Building
Abstract
In this chapter, you advance from qualitative hypotheses to quantitative models linking cause and effect. As always, you’ll begin with your reports, listing and, preferably, graphing your anticipated results. From these graphs you will derive formal models combining deterministic and stochastic (that is, random) elements, then use the methods developed in Chapter 4 to estimate the values of model parameters and test hypotheses about them.
Phillip I. Good
Chapter 11. Which Statistic Should I Use?
Abstract
This chapter provides you with an expert system for use in choosing an appropriate estimation or testing technique.
Phillip I. Good
Backmatter
Metadaten
Titel
Resampling Methods
verfasst von
Phillip I. Good
Copyright-Jahr
2001
Verlag
Birkhäuser Boston
Electronic ISBN
978-1-4757-3425-6
Print ISBN
978-1-4757-3427-0
DOI
https://doi.org/10.1007/978-1-4757-3425-6