ISBN-10:
0321989589
ISBN-13:
9780321989581
Pub. Date:
01/07/2015
Publisher:
Pearson
Statistics for the Life Sciences / Edition 5

Statistics for the Life Sciences / Edition 5

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Product Details

ISBN-13: 9780321989581
Publisher: Pearson
Publication date: 01/07/2015
Edition description: New Edition
Pages: 648
Sales rank: 554,652
Product dimensions: 8.20(w) x 10.10(h) x 1.20(d)

About the Author

Myra L. Samuels (late) was an Associate Professor of Biostatistics and Epidemiology in Purdue's Department of Veterinary Pathobiology and Associate Director of Statistical Consulting in the Department of Statistics. She received her PhD in Statistics from the University of California–Berkeley, under Jerzy Neyman, and taught at Purdue for 24 years. Her research was oriented toward issues in biostatistics and included both conceptual issues in mathematical statistics and collaborations on applications. Myra was a member of the American Statistical Association, the Biometric Society, and the Society for Clinical Trials. Dr. Samuels passed away in 1992.

Jeff Witmer is Professor of Mathematics at Oberlin College. He received his PhD in Statistics from the University of Minnesota and taught at the University of Florida before coming to Oberlin. He is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute.

Andrew Schaffner is Professor of Statistics at California Polytechnic State University–San Luis Obispo and faculty statistician for the Environmental Biotechnology Institute. He received his PhD in Statistics from the University of Washington. His research involves statistical applications in environmental monitoring.


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.

Table of Contents

UNIT I: DATA AND DISTRIBUTIONS

1. Introduction

1.1 Statistics and the Life Sciences

1.2 Types of Evidence

1.3 Random Sampling

2. Description of Samples and Populations

2.1 Introduction

2.2 Frequency Distributions

2.3 Descriptive Statistics: Measures of Center

2.4 Boxplots

2.5 Relationships Between Variables

2.6 Measures of Dispersion

2.7 Effect of Transformation of Variables

2.8 Statistical Inference

2.9 Perspective

3. Probability and the Binomial Distribution

3.1 Probability and the Life Sciences

3.2 Introduction to Probability

3.3 Probability Rules (Optional)

3.4 Density Curves

3.5 Random Variables

3.6 The Binomial Distribution

3.7 Fitting a Binomial Distribution to Data (Optional)

4. The Normal Distribution

4.1 Introduction

4.2 The Normal Curves

4.3 Areas under a Normal Curve

4.4 Assessing Normality

4.5 Perspective

5. Sampling Distributions

5.1 Basic Ideas

5.2 The Sample Mean

5.3 Illustration of the Central Limit Theorem

5.4 The Normal Approximation to the Binomial Distribution

5.5 Perspective

Unit I Highlights and Study

UNIT II: INFERENCE FOR MEANS

6. Confidence Intervals

6.1 Statistical Estimation

6.2 Standard Error of the Mean

6.3 Confidence Interval for μ

6.4 Planning a Study to Estimate μ

6.5 Conditions for Validity of Estimation Methods

6.6 Comparing Two Means

6.7 Confidence Interval for (μ1 - μ2)

6.8 Perspective and Summary

7. Comparison of Two Independent Samples

7.1 Hypothesis Testing: The Randomization Test

7.2 Hypothesis Testing: The t Test

7.3 Further Discussion of the t Test

7.4 Association and Causation

7.5 One-Tailed t Tests

7.6 More on Interpretation of Statistical Significance

7.7 Planning for Adequate Power

7.8 Student’s t: Conditions and Summary

7.9 More on Principles of Testing Hypotheses

7.10 The Wilcoxon-Mann-Whitney Test

8. Comparison of Paired Samples

8.1 Introduction

8.2 The Paired-Sample t Test and Confidence Interval

8.3 The Paired Design

8.4 The Sign Test

8.5 The Wilcoxon Signed-Rank Test

8.6 Perspective

Unit II Highlights and Study

UNIT III: INFERENCE FOR CATEGORICAL DATA

9. Categorical Data: One-Sample Distributions

9.1 Dichotomous Observations

9.2 Confidence Interval for a Population Proportion

9.3 Other Confidence Levels (Optional)

9.4 Inference for Proportions: The Chi-Square Goodness-of-Fit Test

9.5 Perspective and Summary

10. Categorical Data: Relationships

10.1 Introduction

10.2 The Chi-Square Test for the 2 × 2 Contingency Table

10.3 Independence and Association in the 2 × 2 Contingency Table

10.4 Fisher’s Exact Test

10.5 The r × k Contingency Table

10.6 Applicability of Methods

10.7 Confidence Interval for Difference Between Probabilities

10.8 Paired Data and 2 × 2 Tables

10.9 Relative Risk and the Odds Ratio

10.10 Summary of Chi-Square Test

Unit III Highlights and Study

UNIT IV: MODELING RELATIONSHIPS

11. Comparing the Means of Many Independent Samples

11.1 Introduction

11.2 The Basic One-Way Analysis of Variance

11.3 The Analysis of Variance Model

11.4 The Global F Test

11.5 Applicability of Methods

11.6 One-Way Randomized Blocks Design

11.7 Two-Way ANOVA

11.8 Linear Combinations of Means

11.9 Multiple Comparisons

11.10 Perspective

12. Linear Regression and Correlation

12.1 Introduction

12.2 The Correlation Coefficient

12.3 The Fitted Regression Line

12.4 Parametric Interpretation of Regression: The Linear Model

12.5 Statistical Inference Concerning β1

12.6 Guidelines for Interpreting Regression and Correlation

12.7 Precision in Prediction

12.8 Perspective

12.9 Summary of Formulas

Unit IV Highlights and Study

13. A Summary of Inference Methods

13.1 Introduction

13.2 Data Analysis Examples

Chapter Appendices

Chapter Notes

Statistical Tables

Answers to Selected Exercises

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.

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