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Statistics for the Life Sciences / Edition 4

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Overview

Statistics for the Life Sciences presents the key concepts of statistics as applied to the life sciences, while incorporating tools and themes of modern data analysis. The book emphasizes interpretation of results using real data which facilitates an understanding of statistics and data through the use of graphical data and analysis. It also provides a summary of methods in Chapter 13: "What to do When." The second edition of Statistics for the Life Sciences has been revised to include a number of new sections covering boxplots, probability trees, and probability plots. In addition, chapter 8 has been expanded to include a more complete discussion of issues in data collection, including observational studies vs. experiments, the placebo effect, the use of historical controls vs. active controls, and randomized response sampling. It now includes a new Chapter 10, which includes all of the chi-square test material as well as new sections on Fisher's exact test, McNemar's test, and relative risk and odds ratios. An essential statistics reference for professionals and scientists in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.
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Editorial Reviews

Booknews
An introductory book/disk text in statistics, for undergraduate and graduate students majoring in the life sciences. Its aims are to show students how statistical reasoning is used in biological, medical, and agricultural research; to enable students to carry out simple statistical analyses and to interpret results; and to raise awareness of basic statistical issues. Chapter exercise are designed to reduce computational effort and focus attention on concepts, with only a few exercises requiring a computer. Assumes a prior course in algebra. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780321652805
  • Publisher: Pearson
  • Publication date: 1/10/2011
  • Edition description: New Edition
  • Edition number: 4
  • Pages: 672
  • Sales rank: 161,654
  • Product dimensions: 8.20 (w) x 10.10 (h) x 1.10 (d)

Meet the Author

Jeff Witmer is a PhD statistician and Professor at Oberlin College. He is a Fellow of the American Statistical Association, previous Chair of the Section on Statistical Education of ASA, previous editor of STATS magazine, and a member of the editorial board of the Consortium for the Advancement of Undergraduate Statistics Education. His work has been supported by grants from the National Science Foundation and the National Institutes of Health.

Andrew Schaffner is a PhD statistician and Professor at California Polytechnic State University—San Luis Obispo. Additionally, he serves as the staff research statistician for both the Cal Poly Environmental Biotechnology Institute and the Morro Bay National Estuary Program. Organizations that support his research include the National Institutes of Health, United States Department of Agriculture, and the US Environmental Protection Agency.

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Read an Excerpt

Statistics for the Life Sciences is an introductory text in statistics, specifically addressed to students specializing in the life sciences. Its primary aims are (1) to show students how statistical reasoning is used in biological, medical, and agricultural research; (2) to enable students confidently to carry out simple statistical analyses and to interpret the results; and (3) to raise students' awareness of basic statistical issues such as randomization, confounding, and the role of independent replication. Style and Approach

The style of Statistics for the Life Sciences is informal and uses only minimal mathematical notation. There are no prerequisites except elementary algebra; anyone who can read a biology or chemistry textbook can read this text. It is suitable for use by graduate or undergraduate students in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.

Use of Real Data. Real examples are more interesting and often more enlightening than artificial ones. Statistics for the Life Sciences includes hundreds of examples and exercises that use real data, representing a wide variety of research in the life sciences. Each example has been chosen to illustrate a particular statistical issue. The exercises have been designed to reduce computational effort and focus students' attention on concepts and interpretations.

Emphasis on Ideas. The text emphasizes statistical ideas rather than computations or mathematical formulations. Probability theory is included only to support statistics concepts. Throughout the discussion of descriptive andinferential statistics, interpretation is stressed. By means of salient examples, the student is shown why it is important that an analysis be appropriate for the research question to be answered, for the statistical design of the study, and for the nature of the underlying distributions. The student is warned against the common blunder of confusing statistical nonsignificance with practical insignificance, and is encouraged to use confidence intervals to assess the magnitude of an effect. The student is led to recognize the impact on real research of design concepts such as random sampling, randomization, efficiency, and the control of extraneous variation by blocking or adjustment. Numerous exercises amplify and reinforce the student's grasp of these ideas.

The Role of the Computer/ The analysis of research data is usually carried out with the aid of a computer. Computer-generated graphs and output, either from the statistical software DataDesk or MINITAB, are shown at several places in the text. MINITAB commands are given in a number of places (although MINITAB output can also be generated from menus while running the software). However, in studying statistics it is desirable for the student to gain experience working directly with data, using paper and pencil and a hand-held calculator, as well as a computer. This experience will help the student appreciate the nature and purpose of the statistical computations. The student is thus prepared to make intelligent use of the computer—to give it appropriate instructions and properly interpret the output. Accordingly, most of the exercises in this text are intended for hand calculation. Selected exercises, identified with the words "computer exercise" are intended to be completed with use of a computer. (Typically, the computer exercises require calculations that would be unduly burdensome if carried out by hand.) Organization

This text is organized to permit coverage in one semester of the maximum number of important statistical ideas, including power, multiple inference, and the basic principles of design. By including or excluding optional sections, the instructor can also use the text for a one-quarter course or a two-quarter course. It is suitable for a terminal course or for the first course of a sequence.

The following is a brief outline of the text:

Chapter 1: Introduction. The nature and impact of variability in biological data.

Chapter 2: Orientation. Frequency distributions, descriptive statistics, the concept of population versus sample.

Chapters 3, 4, and 5: Theoretical preparation. Probability, binomial and normal distributions, sampling distributions.

Chapter 6: Confidence interval for a mean or for a proportion.

Chapter 7: Comparison of two independent samples. The t-test and the Wilcoxon-Mann-Whitney test.

Chapter 8: Design. Randomization, blocking, hazards of observational studies.

Chapter 9: Inference for paired samples. Confidence interval, t-test, sign test, and Wilcoxon signed-rank test.

Chapter 10: Categorical data. Chi-square goodness-of-fit test, conditional probability, contingency tables. Optional sections cover Fisher's exact test, McNemar's test, and odds ratios.

Chapter 11: Analysis of variance: one-way layout. Multiple comparison procedures, two-way analysis of variance, contrasts, and interaction in two-factor designs are included in optional sections.

Chapter 12: Regression and correlation. Descriptive and inferential aspects of simple linear regression and correlation and the relationship between them.

Chapter 13: A summary of inference methods.

Statistical tables are provided at the back of the book. The tables of critical values are especially easy to use, because they follow mutually consistent layouts and so are used in essentially the same way.

Optional appendices at the back of the book give the interested student a deeper look into such matters as how the Wilcoxon-Mann-Whitney null distribution is calculated.

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Table of Contents

1. Introduction.
Statistics and the Life Sciences. Examples and Overview.

2. Description of Populations and Samples.
Introduction. Frequency Distributions: Techniques for Data. Frequency Distributions: Shapes and Examples. Descriptive Statistics: Measures of Center. Boxplots. Measures of Dispersion. Effect of Transformation of Variables (Optional). Samples and Populations: Statistical Inference. Perspective.

3. Random Sampling, Probability, and the Binomial Distribution.
Probability and the Life Sciences. Random Sampling. Introduction to Probability. Probability Trees. Density Curves. The Binomial Distribution. Fitting a Binomial Distribution to Data (Optional).

4. The Normal Distribution.
Introduction. The Normal Curves. Areas Under a Normal Curve. Assessing Normality. The Continuity Correction (Optional). Perspective.

5. Sampling Distributions.
Basic Ideas. Dichotomous Observations. Quantitative Observations. Illustration of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distribution (Optional). Perspective.

6. Confidence Intervals.
Statistical Estimation. Standard Error of the Mean. Confidence Interval for u. Planning a Study to Estimate u. Conditions for Validity of Estimation Methods. Confidence Interval for a Population Proportion. Perspective and Summary.

7. Comparison of TwoIndependent Samples.
Introduction. Standard Error of (y1 - y2). Confidence Interval for (u1 - u2). Hypothesis Testing: The t-test. Further Discussion of the t-test. One-Tailed Tests. More on Interpretation of Statistical Significance. Planning for Adequate Power (Optional). Student's t: Conditions and Summary. More on Principles of Testing Hypotheses. The Wilcoxon-Mann-Whitney Test. Perspective.

8. Statistical Principles of Design.
Introduction. Observational Studies. Experiments. Restricted Randomization: Blocking and Stratification. Levels of Replication. Sampling Concerns (Optional). Perspective.

9. Comparison of Two Paired Samples.
Introduction. The Paired-Sample t-Test and Confidence Interval. The Paired Design. The Sign Test. Further Considerations in Paired Experiments. Perspective.

10. Analysis of Categorical Data.
Inference for Proportions: The Chi-Square Goodness-of-Fit Test. The Chi-Square Test for the 2 X 2 Contingency Table. Independence and Association in a 2 X 2 Contingency Table. Fisher's Exact Test (Optional). The r x k Contingency Table. Applicability of Methods. Confidence Interval for a Difference Between Proportions. Paired Data and 2 X 2 Tables (Optional). Relative Risks and the Odds Ratio (Optional). Summary of Chi-Square Tests.

11. Comparing the Means of k Independent Samples.
Introduction. The Basic Analysis of Variance. The Analysis of Variance Model (Optional). The Global F Test. Applicability of Methods. Linear Combinations of Means (Optional). Multiple Comparisons (Optional). Perspective.

12. Linear Regression and Correlation.
Introduction. The Fitted Regression Line. Parametric Interpretation of Regression: The Linear Model. Statistical Inference Concerning B1. Guidelines for Interpreting Regression and Correlation. Perspective. Summary of Formulas.

13. A Summary of Inference Methods.
Introduction. Data Analysis Samples.

Appendices.
Chapter Notes.
Statistical Tables.
Answers to Selected Exercises.
Index.
Index of Examples.
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Preface

Statistics for the Life Sciences is an introductory text in statistics, specifically addressed to students specializing in the life sciences. Its primary aims are (1) to show students how statistical reasoning is used in biological, medical, and agricultural research; (2) to enable students confidently to carry out simple statistical analyses and to interpret the results; and (3) to raise students' awareness of basic statistical issues such as randomization, confounding, and the role of independent replication.

Style and Approach

The style of Statistics for the Life Sciences is informal and uses only minimal mathematical notation. There are no prerequisites except elementary algebra; anyone who can read a biology or chemistry textbook can read this text. It is suitable for use by graduate or undergraduate students in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.

Use of Real Data. Real examples are more interesting and often more enlightening than artificial ones. Statistics for the Life Sciences includes hundreds of examples and exercises that use real data, representing a wide variety of research in the life sciences. Each example has been chosen to illustrate a particular statistical issue. The exercises have been designed to reduce computational effort and focus students' attention on concepts and interpretations.

Emphasis on Ideas. The text emphasizes statistical ideas rather than computations or mathematical formulations. Probability theory is included only to support statistics concepts. Throughout the discussion of descriptive and inferential statistics, interpretation is stressed. By means of salient examples, the student is shown why it is important that an analysis be appropriate for the research question to be answered, for the statistical design of the study, and for the nature of the underlying distributions. The student is warned against the common blunder of confusing statistical nonsignificance with practical insignificance, and is encouraged to use confidence intervals to assess the magnitude of an effect. The student is led to recognize the impact on real research of design concepts such as random sampling, randomization, efficiency, and the control of extraneous variation by blocking or adjustment. Numerous exercises amplify and reinforce the student's grasp of these ideas.

The Role of the Computer/ The analysis of research data is usually carried out with the aid of a computer. Computer-generated graphs and output, either from the statistical software DataDesk or MINITAB, are shown at several places in the text. MINITAB commands are given in a number of places (although MINITAB output can also be generated from menus while running the software). However, in studying statistics it is desirable for the student to gain experience working directly with data, using paper and pencil and a hand-held calculator, as well as a computer. This experience will help the student appreciate the nature and purpose of the statistical computations. The student is thus prepared to make intelligent use of the computer—to give it appropriate instructions and properly interpret the output. Accordingly, most of the exercises in this text are intended for hand calculation. Selected exercises, identified with the words "computer exercise" are intended to be completed with use of a computer. (Typically, the computer exercises require calculations that would be unduly burdensome if carried out by hand.)

Organization

This text is organized to permit coverage in one semester of the maximum number of important statistical ideas, including power, multiple inference, and the basic principles of design. By including or excluding optional sections, the instructor can also use the text for a one-quarter course or a two-quarter course. It is suitable for a terminal course or for the first course of a sequence.

The following is a brief outline of the text:

Chapter 1: Introduction. The nature and impact of variability in biological data.

Chapter 2: Orientation. Frequency distributions, descriptive statistics, the concept of population versus sample.

Chapters 3, 4, and 5: Theoretical preparation. Probability, binomial and normal distributions, sampling distributions.

Chapter 6: Confidence interval for a mean or for a proportion.

Chapter 7: Comparison of two independent samples. The t-test and the Wilcoxon-Mann-Whitney test.

Chapter 8: Design. Randomization, blocking, hazards of observational studies.

Chapter 9: Inference for paired samples. Confidence interval, t-test, sign test, and Wilcoxon signed-rank test.

Chapter 10: Categorical data. Chi-square goodness-of-fit test, conditional probability, contingency tables. Optional sections cover Fisher's exact test, McNemar's test, and odds ratios.

Chapter 11: Analysis of variance: one-way layout. Multiple comparison procedures, two-way analysis of variance, contrasts, and interaction in two-factor designs are included in optional sections.

Chapter 12: Regression and correlation. Descriptive and inferential aspects of simple linear regression and correlation and the relationship between them.

Chapter 13: A summary of inference methods.

Statistical tables are provided at the back of the book. The tables of critical values are especially easy to use, because they follow mutually consistent layouts and so are used in essentially the same way.

Optional appendices at the back of the book give the interested student a deeper look into such matters as how the Wilcoxon-Mann-Whitney null distribution is calculated.

Read More Show Less

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