Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 ‘Doing science’ – hypotheses, experiments, and disproof
- 3 Collecting and displaying data
- 4 Introductory concepts of experimental design
- 5 Probability helps you make a decision about your results
- 6 Working from samples – data, populations, and statistics
- 7 Normal distributions – tests for comparing the means of one and two samples
- 7 Type 1 and Type 2 errors, power, and sample size
- 9 Single factor analysis of variance
- 10 Multiple comparisons after ANOVA
- 11 Two factor analysis of variance
- 12 Important assumptions of analysis of variance: transformations and a test for equality of variances
- 13 Two factor analysis of variance without replication, and nested analysis of variance
- 14 Relationships between variables: linear correlation and linear regression
- 15 Simple linear regression
- 16 Non-parametric statistics
- 17 Non-parametric tests for nominal scale data
- 18 Non-parametric tests for ratio, interval, or ordinal scale data
- 19 Choosing a test
- 20 Doing science responsibly and ethically
- References
- Index
13 - Two factor analysis of variance without replication, and nested analysis of variance
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 ‘Doing science’ – hypotheses, experiments, and disproof
- 3 Collecting and displaying data
- 4 Introductory concepts of experimental design
- 5 Probability helps you make a decision about your results
- 6 Working from samples – data, populations, and statistics
- 7 Normal distributions – tests for comparing the means of one and two samples
- 7 Type 1 and Type 2 errors, power, and sample size
- 9 Single factor analysis of variance
- 10 Multiple comparisons after ANOVA
- 11 Two factor analysis of variance
- 12 Important assumptions of analysis of variance: transformations and a test for equality of variances
- 13 Two factor analysis of variance without replication, and nested analysis of variance
- 14 Relationships between variables: linear correlation and linear regression
- 15 Simple linear regression
- 16 Non-parametric statistics
- 17 Non-parametric tests for nominal scale data
- 18 Non-parametric tests for ratio, interval, or ordinal scale data
- 19 Choosing a test
- 20 Doing science responsibly and ethically
- References
- Index
Summary
Introduction
This chapter describes two slightly more complex ANOVA models often used by life scientists, but an understanding of these is not essential if you are reading this book as an introduction to biostatistics. If, however, you need to use more complex models, the explanations given here for two factor ANOVA without replication and nested ANOVA are straightforward extensions of the pictorial descriptions in Chapters 9 and 11 and will help with many of the ANOVA models used to analyse more complex designs.
Two factor ANOVA without replication
This is a special case of the two factor ANOVA described in Chapter 11. Sometimes an orthogonal experiment with two independent factors has to be done without replication, because there is a shortage of experimental subjects or the treatments are very expensive to administer. The simplest case of ANOVA without replication is a two factor design. You cannot do a one factor ANOVA without replication.
The data in Table 13.1 are for a preliminary trial of two experimental drugs ‘Proshib’ and ‘Testoblock’, which were being evaluated, together with a control treatment, for their effect on the growth of solid tumours of the prostate, in combination with three levels of radiation therapy (high, medium, and low). The researcher had only nine consenting volunteers with advanced prostate cancer, so an orthogonal design was only possible without replication.
- Type
- Chapter
- Information
- Statistics ExplainedAn Introductory Guide for Life Scientists, pp. 162 - 175Publisher: Cambridge University PressPrint publication year: 2005