|
Also Recommended
|
|
|
Alan Grafen, Rosie Hails
£29.99
|
|
|
|
|
|
Debbie Holmes, Peter Moody...
£26.99
|
|
|
|
|
|
Graeme D. Ruxton, Nick Colegrave
£19.99
|
|
|
|
|
Biomeasurement
A student's guide to biological statistics
Second Edition
Dawn Hawkins
368 pages
|
100 Black and White illustrations
|
246x189mm
978-0-19-921999-5
|
Paperback
|
19 March 2009
|
|
|
|
|
- The perfect companion to an introductory biological statistics course.
- Avoids detailed treatment of the mathematical basis of statistics, but focuses on the application of statistics as a valuable research tool.
- Emphasis is placed on explantion so that students are guided through the key concepts and techniques in a steady, progressive way.
- The first entry-level biostatistics textbooks to introduce the generalized linear model.
- Use of real data sets enables students to build confidence in handling actual data.
- 'Literature Links' illustrate the use of the techniques presented in real world research work, highlighting the importance of statistics in 'real' science.
- Online Resource Centre features extensive additional resources for both lecturers and students, adding to the educational value of the text.
New to this edition - A new chapter on the generalized linear model introduces the reader to this powerful statistical tool, and explores alternative procedures to two-way ANOVA, multiple regression, and the the analysis of covariance.
- A new Appendix reinforcing the link between statistics and experimental design.
- Greater guidance on reporting of reults.
- Enhanced layout makes the key points and concepts easier to identify.
- Enhanced cross-referencing to the Online Resource Centre help readers use the print text and online support in an integrated way.
Statistical analysis allows us to attach meaning to data which we have collected; it helps us to understand what results really mean, and to assess whether we can trust what experiments seem to be telling us. Yet, despite being a collection of the most valuable and important tools available to bioscientists, statistics is the aspect of study which most students fear more than any other.
Biomeasurement offers a refreshing, student-focused introduction to the use of statistics in the study of the biosciences. With an emphasis on why statistical techniques are
essential tools for bioscientists, the book removes the stigma attached to statistics by giving students the confidence to use and further explore the key techniques for themselves.
The book starts by placing the role of data analysis in the context of wider scientific method, and introduces the student to the key terms and concepts which are common to all statistical tools. It then guides the student through descriptive statistics, and on to inferential statistics, explaining how and why each type of technique is used, and what each can tell us in order to better understand our data. The book goes on to present the key statistical tests, walking the student step-wise through the use of each, with carefully integrated examples, and plentiful opportunities for
hands-on practice. The book closes with an overview of choosing the right test to suit your data, and tools for presenting data and their statistical analyses.
Written by a talented educator, whose teaching has won praise from the UK's Quality and Assurance Agency for Higher Education, Biomeasurement is sure to engage even the most wary of students, demonstrating the power and importance of statistics throughout the study of bioscience.
Online Resource Centre
The Online Resource Centre to accompany Biomeasurement features
For lecturers:
Figures from the book in electronic format, ready to download.
For students:
Data set, for use in a variety of
statistical packages, so that students can practise carrying out statistical analysis.
Literature link articles: full-text versions of the Literature Link articles cited in the text.
Interactive calculation sheets to help students carry out key statistical tests quickly and easily, without needing other software.Readership: Any bioscience undergraduate studying statistics and statistical analysis as part of a bioscience degree. Also a useful primer for beginning graduate students.
|
|
|
Dawn Hawkins, Senior Lecturer, Department of Life Sciences, Anglia Ruskin University.
|
|
|
Review(s) from previous edition
"'Biomeasurement really is a 'top notch' text on statistics for Biologists. I wish it had been written before I planned my own course!' - Mark Winterbottom, Journal of Biological Education, Vol 40, No 2, Spring 2006
"'A 'must purchase' for biologists interested in statistics. Oh, that I had had the benefit of being taught by Dawn Hawkins!'" - Basil Jarvis, Biologist Vol 52 No 3
|
|
|
Chapter 1: Why am I reading this book?
1.1: My lecturer is a sadist!
1.2: Doing science: the big picture
1.3: The process in practice
1.4: Essential skills for doing science
1.5: Types of data analysis
Chapter 2: Getting to grips with the basics
2.1: Populations and samples
2.2: Variation and variables
2.3: Understanding data
2.4: Demystifying formulae
Chapter 3: Describing a single sample
3.1: The single sample
3.2: Descriptive statistics
3.3: Frequency distributions
3.4: Pies, boxes, and errors
3.5: Example data: ranger patrol tusk records
3.6: Worked example: using SPSS
Chapter 4: Inferring and estimating
4.1: Overview of inferential statistics
4.2: Inferring through estimation
4.3: Exampledata: ground squirrels
4.4: Worked example: using SPSS
Chapter 5: Overview of hypothesis testing
5.1: Four steps of (statistical) hypothesis testing
5.2: Error and power
5.3: Parametric and nonparametric
5.4: One-and two-tailed tests
Chapter 6: Tests on frequencies
6.1: Introduction to chi-square tests
6.2: Example data
6.3: One-way classification chi-square test
6.4: Two-way classification chi-square test
Chapter 7: Tests of difference: two unrelated samples
7.1: Introduction to the t-and Mann-Whitney U tests
7.2: Example data: dem bones
7.3: t-Test
7.4: Mann-Whitney U test
Chapter 8: Tests of difference: two related samples
8.1: Introduction to paired t- and Wilcoxon signed- rank tests
8.2: Example data: big horn ewes
8.3: Paired t-test
8.4: Wilcoxon signed-rank test
Chapter 9: Tests of difference: more than two samples
9.1: Introduction to one-way and Kruskal-Wallis Anov atests
9.2: Example data: nitrogen levels in reeds
9.3: One-way Anova test
9.4: Two-way Anova test
9.5: Kruskal-Wallis test
9.6: Model I and model II Anova
Chapter 10: Tests of relationship: regression
10.1: Introduction
10.2: Example data: species richness
10.3: Regression test
10.4: Logistic regression
10.5: Multiple regression
10.6: Model I and model II regression
Chapter 11: Tests of relationship: correlation
11.1: Introduction to the Pearson and Spearman correlation tests
11.2: Example data: eyeballs
11.3: Pearson correlation test
11.4: Spearman correlation test
11.5: Comparison of correlation and regression
Chapter 12: Introducing th General Linear Model
12.1: Introduction to General Linear Model
12.2: Example data: watered willow
12.3: Testing using the General Linear Model
12.4: Interaction
12.5: Random factors and mixed models
12.6: Types of sums of squares
12.7: Getting the most out of GLM: Multiple models and model choice
12.8: The general and generalized linear models compared
Chapter 13: Choosing the right test and graph
13.1: Introduction to choosing
13.2: Which test?
13.3: Which graph?
13.4: Worked examples: graphs using SPSS
13.5: How to report your results
|
|
|
|
|
|
|
|
|
The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
|
|
