PDF Ebook Statistics for the Life Sciences (3rd Edition), by Myra L. Samuels, Jeffrey A. Witmer
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Statistics for the Life Sciences (3rd Edition), by Myra L. Samuels, Jeffrey A. Witmer
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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 Wilcoxon 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.
- Sales Rank: #621971 in Books
- Published on: 2002-12-13
- Original language: English
- Number of items: 1
- Dimensions: 10.29" h x 1.17" w x 8.27" l, 3.45 pounds
- Binding: Hardcover
- 736 pages
From the Back Cover
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 Wilcoxon 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.
Excerpt. � Reprinted by permission. All rights reserved.
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 ApproachThe 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.)
OrganizationThis 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.
Most helpful customer reviews
9 of 11 people found the following review helpful.
definitely oversimplified.
By J. Chang
I took an AP statistics course in high school that went into much better detail than this book ever did. With this book I could not have made it out with the understanding I currently have of statistics without having taken that class years ago. Our instructor was pretty much incompetent, so it was a true test of how well the textbook got the job done. I watched my classmates really struggle with this book. The problem is that the concepts of different test statistics and what they actually mean are too oversimplified. The book does a great job of teaching how through an extensive amount of examples, but not why, and not enough instances or explanations of what should be done when problems deviate from the given example. It is really important in statistics to know how to handle different kinds of data and which tests to apply to them, but the book in my opinion did a poor job of distinguishing and explaining the different types of tests, as well as how the results should be analyzed. I would definitely recommend looking elsewhere for a good statistics textbook. When it comes to mathematics, theory (knowing why a problem is solved the way it is and why it works) is just as important as being able to correctly solve the problem.
2 of 2 people found the following review helpful.
Book better than class.
By Christopher Wegener
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!
3 of 4 people found the following review helpful.
Oversimplified
By M. Gao
The book is reasonably good at explaining simple concepts.
However, the lack of detail does not allow for real life application in a biological field: the concepts are oversimplified and the examples given do not reflect the difficulty of actual basic statistics work in the life sciences.
If you want to buy this book, the questions you should ask yourself are:
1. Are you buying this book just to pass a basic statistics course?
2. Are you buying this book to expand your point of view and understanding of real life applications?
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