# Statistics for the Life Sciences

• ISBN13:

• ISBN10:

## 013041316X

• Edition: 3rd
• Format: Hardcover w/CD
• Purchase Benefits
• Free Shipping On Orders Over \$35!
Your order must be \$35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
• Get Rewarded for Ordering Your Textbooks! Enroll Now
List Price: \$142.67

### Summary

For graduate or undergraduate courses in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences. With a strong emphasis on real data, exploratory data analysis, interpretation of results, and checking assumptions, this text clearly conveys the key concepts of statistics as applied to life sciences while incorporating tools and themes of modern data analysis. The authors' goal is to help students understand concepts not memorize formulas; they make liberal use of exercises, worked examples, and graphical methods to do so.

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.

The Third Edition has added many new sections to cover probability rules, random variables, the Wilcox on Signed-Rank Test, and two-way ANOVA and ANOVA for randomized blocks designs. In addition, there is expanded treatment of logistic regression in Chapter 12.This book is 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.

Preface vii
 Introduction
1(8)
 Statistics and the Life Sciences
1(1)
 Examples and Overview
1(8)
 Description of Populations and Samples
9(62)
 Introduction
9(3)
 Frequency Distributions: Techniques for Data
12(9)
 Frequency Distributions: Shapes and Examples
21(5)
 Descriptive Statistics: Measures of Center
26(6)
 Boxplots
32(8)
 Measures of Dispersion
40(10)
 Effect of Transformation of Variables (Optional)
50(7)
 Samples and Populations: Statistical Inference
57(6)
 Perspective
63(8)
 Random Sampling, Probability, and the Binomial Distribution
71(48)
 Probability and the Life Sciences
71(1)
 Random Sampling
71(7)
 Introduction to Probability
78(5)
 Probability Trees
83(5)
 Probability Rules (Optional)
88(5)
 Density Curves
93(3)
 Random Variables
96(6)
 The Binomial Distribution
102(10)
 Fitting a Binomial Distribution to Data (Optional)
112(7)
 The Normal Distribution
119(30)
 Introduction
119(3)
 The Normal Curves
122(1)
 Areas Under a Normal Curve
123(10)
 Assessing Normality
133(8)
 The Continuity Correction (Optional)
141(4)
 Perspective
145(4)
 Sampling Distributions
149(30)
 Basic Ideas
149(2)
 Dichotomous Observations
151(6)
 Quantitative Observations
157(10)
 Illustration of the Central Limit Theorem (Optional)
167(3)
 The Normal Approximation to the Binomial Distribution (Optional)
170(5)
 Perspective
175(4)
 Confidence Intervals
179(40)
 Statistical Estimation
179(1)
 Standard Error of the Mean
180(5)
 Confidence Interval for μ
185(12)
 Planning a Study to Estimate μ
197(2)
 Conditions for Validity of Estimation Methods
199(7)
 Confidence Interval for a Population Proportion
206(7)
 Perspective and Summary
213(6)
 Comparison of Two Independent Samples
219(90)
 Introduction
219(3)
 Standard Error of (y1 -- y2)
222(5)
 Confidence Interval for (μ1 -- μ2)
227(7)
 Hypothesis Testing: The t test
234(14)
 Further Discussion of the t test
248(8)
 One-Tailed t Tests
256(10)
 More on Interpretation of Statistical Significance
266(7)
273(7)
 Student's t: Conditions and Summary
280(4)
 More on Principles of Testing Hypotheses
284(4)
 The Wilcoxon-Mann-Whitney Test
288(10)
 Perspective
298(11)
 Statistical Principles of Design
309(38)
 Introduction
309(2)
 Observational Studies
311(6)
 Experiments
317(9)
 Restricted Randomization: Blocking and Stratification
326(8)
 Levels of Replication
334(4)
 Sampling Concerns (Optional)
338(3)
 Perspective
341(6)
 Comparison of Paired Samples
347(44)
 Introduction
347(1)
 The Paired-Sample t test and Confidence Interval
348(10)
 The Paired Design
358(6)
 The Sign Test
364(8)
 The Wilcoxon Signed-Rank Test
372(5)
 Further Considerations in Paired Experiments
377(4)
 Perspective
381(10)
 Analysis of Categorical Data
391(72)
 Inference for Proportions: The Chi-Square Goodness-of-Fit Test
391(11)
 The Chi-Square Test for the 2 x 2 Contingency Table
402(10)
 Indepence and Association in the 2 x 2 Contingency Table
412(10)
 Fisher's Exact Test (Optional)
422(6)
 The r x k Contingency Table
428(6)
 Applicability of Methods
434(5)
 Confidence Interval for Difference Between Probabilities
439(2)
 Paired Data and 2 x 2 Tables (Optional)
441(3)
 Relative Risk and the Odds Ratio (Optional)
444(10)
 Summary of Chi-Square Tests
454(9)
 Comparing the Means of Many Independent Samples
463(62)
 Introduction
463(4)
 The Basic Analysis of Variance
467(9)
 The Analysis of Variance Model (Optional)
476(2)
 The Global F Test
478(6)
 Applicability of Methods
484(3)
 Two-Way ANOVA (Optional)
487(11)
 Linear Combinations of Means (Optional)
498(9)
 Multiple Comparisons (Optional)
507(9)
 Perspective
516(9)
 Linear Regression and Correlation
525(70)
 Introduction
525(2)
 The Fitted Regression Line
527(14)
 Parametric Interpretation of Regression: The Linear Model
541(7)
 Statistical Inference Concerning β1
548(5)
 The Correlation Coefficient
553(12)
 Guidelines for Interpreting Regression and Correlation
565(11)
 Perspective
576(10)
 Summary of Formulas
586(9)
 A Summary of Inference Methods
595(16)
 Introduction
595(2)
 Data Analysis Examples
597(14)
Chapter Appendices 611(22)
Chapter Notes 633(36)
Statistical Tables 669(32)
 Random Digits
670(4)
 Binomial Coefficients nCj
674(1)
 Areas Under the Normal Curve
675(2)
 Critical Values of Student's t Distribution
677(1)
 Number of Observations for Independent-Samples t Test
678(2)
 Critical Values of U, the Wilcoxon-Mann-Whitney Statistic
680(4)
 Critical Values of B for the Sign Test
684(1)
 Critical Values of W for the Wilcoxon Signed-rank Test
685(1)
 Critical Values of the Chi-Square Distribution
686(1)
 Critical Values of the F Distribution
687(10)
 Critical Constants for the Newman-Keuls Procedure
697(2)
 Bonferroni Multipliers for 95% Confidence Intervals
699(2)
Index 715(8)
Index of Examples 723

### Excerpts

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 ofStatistics for the Life Sciencesis 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 Sciencesincludes 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 c

### Customer Reviews

Book better than class. August 2, 2011
This textbook was required for my statistics class at the University of Wisconsin. I found the book easier to follow with better examples than my lecture was. Often I cannot learn anything right from the book, but this book made it easy with complete examples and simple descriptions of variables and theory. The book was so good; in fact, I rarely made it to class because I learned it better from the book!
Flag Review
Please provide a brief explanation for why you are flagging this review:
Your submission has been received. We will inspect this review as soon as possible. Thank you for your input!
Statistics for the Life Sciences: 5 out of 5 stars based on 1 user reviews.