Biomeasurement

• 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 reinforces 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.

Dawn Hawkins, Senior Lecturer, Department of Life Sciences, Anglia Ruskin University.

"This is a very user-friendly introduction to statistical methods for first-year undergraduate biology students." - F. James Rohlf, in Quarterly Review of Biology, September 2010

"Even from reading the first chapter only it is clear that the writer has put some thought into making the book entertaining to read. Oddly, it has worked! Her style of writing is casual, readable and humorous. This simple and 'easy to read' style makes the book inviting to use. The writer has achieved something I thought was impossible and made statistics interesting/appealing to study!" - Bethan Clifford, Student, University of Nottingham

"The presentation allows readers easily to access material which is relevant to their needs. Chapter 13, on presentation of data, provides an ideal conclusion to book." - The Society of General Microbiology

"Biomeasurement is a well researched and planned book. The audience of the book is clearly identified and targeted by the material." - The Society for General Microbiology

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