Starting out in statistics an introduction for students of human health, disease and psychology.
To form a strong grounding in human-related sciences it is essential for students to grasp the fundamental concepts of statistical analysis, rather than simply learning to use statistical software. Although the software is useful, it does not arm a student with the skills necessary to formulate the...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Chichester
WILEY
2014
Wiley John Wiley John Wiley & Sons, Incorporated Wiley-Blackwell |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781118920381 1118384024 9781118384022 1118920384 1118384016 9781118384015 |
Cover
Table of Contents:
- Starting out in statistics : an introduction for students of human health, disease and psychology -- Contents -- Introduction - What's the Point of Statistics? -- Basic Maths for Stats Revision -- Statistical Software Packages -- About the Companion Website -- 1 Introducing Variables, Populations and Samples - 'Variability is the Law of Life' -- 2 Study Design and Sampling - 'Design is Everything. Everything!' -- 3 Probability - 'Probability...So True in General' -- 4 Summarising Data - 'Transforming Data into Information' -- 5 Statistical Power - '...Find out the Cause of this Effect' -- 6 Comparing Groups using t-Tests and ANOVA - 'To Compare is not to Prove' -- 7 Relationships between Variables: Regression and Correlation - 'In Relationships...Concentrate only on what is most Significant and Important' -- 8 Analysis of Categorical Data - 'If the Shoe Fits...' -- 9 Non-Parametric Tests - 'An Alternative to other Alternatives' -- 10 Resampling Statistics comes of Age - 'There's always a Third Way' -- Appendix A: Data Used for Statistical Analyses (Chapters 6,7 and 10) -- Appendix B: Statistical Software Outputs (Chapters 6-9) -- Index
- 4.6.1 The scatter diagram or plot -- 4.6.2 The line graph -- 4.7 Displaying complex (multidimensional) data -- 4.8 Displaying proportions or percentages -- 4.8.1 The pie chart -- 4.8.2 Tabulation -- 4.9 Summary -- References -- 5 Statistical Power - '. . . Find out the Cause of this Effect' -- 5.1 Aims -- 5.2 Power -- 5.3 From doormats to aortic valves -- 5.4 More on the normal distribution -- 5.4.1 The central limit theorem -- 5.5 How is power useful? -- 5.5.1 Calculating the power -- 5.5.2 Calculating the sample size -- 5.6 The problem with p values -- 5.7 Confidence intervals and power -- 5.8 When to stop collecting data -- 5.9 Likelihood versus null hypothesis testing -- 5.10 Summary -- References -- 6 Comparing Groups using t-Tests and ANOVA - 'To Compare is not to Prove' -- 6.1 Aims -- 6.2 Are men taller than women? -- 6.3 The central limit theorem revisited -- 6.4 Student's t-test -- 6.4.1 Calculation of the pooled standard deviation -- 6.4.2 Calculation of the t statistic -- 6.4.3 Tables and tails -- 6.5 Assumptions of the t-test -- 6.6 Dependent t-test -- 6.7 What type of data can be tested using t-tests? -- 6.8 Data transformations -- 6.9 Proof is not the answer -- 6.10 The problem of multiple testing -- 6.11 Comparing multiple means - the principles of analysis of variance -- 6.11.1 Tukey's honest significant difference test -- 6.11.2 Dunnett's test -- 6.11.3 Accounting for identifiable sources of error in one-way ANOVA: nested design -- 6.12 Two-way ANOVA -- 6.12.1 Accounting for identifiable sources of error using a two-way ANOVA: randomised complete block design -- 6.12.2 Repeated measures ANOVA -- 6.13 Summary -- References -- 7 Relationships between Variables: Regression and Correlation - 'In Relationships . . . Concentrate only on what is most Significant and Important' -- 7.1 Aims -- 7.2 Linear regression
- 7.2.1 Partitioning the variation -- 7.2.2 Calculating a linear regression -- 7.2.3 Can weight be predicted by height? -- 7.2.4 Ordinary least squares versus reduced major axis regression -- 7.3 Correlation -- 7.3.1 Correlation or linear regression? -- 7.3.2 Covariance, the heart of correlation analysis -- 7.3.3 Pearson's product-moment correlation coefficient -- 7.3.4 Calculating a correlation coefficient -- 7.3.5 Interpreting the results -- 7.3.6 Correlation between maternal BMI and infant birth weight -- 7.3.7 What does this correlation tell us and what does it not? -- 7.3.8 Pitfalls of Pearson's correlation -- 7.4 Multiple regression -- 7.5 Summary -- References -- 8 Analysis of Categorical Data - 'If the Shoe Fits . . . ' -- 8.1 Aims -- 8.2 One-way chi-squared -- 8.3 Two-way chi-squared -- 8.4 The odds ratio -- 8.5 Summary -- References -- 9 Non-Parametric Tests - 'An Alternative to other Alternatives' -- 9.1 Aims -- 9.2 Introduction -- 9.3 One sample sign test -- 9.4 Non-parametric equivalents to parametric tests -- 9.5 Two independent samples -- 9.6 Paired samples -- 9.7 Kruskal-Wallis one-way analysis of variance -- 9.8 Friedman test for correlated samples -- 9.9 Conclusion -- 9.10 Summary -- References -- 10 Resampling Statistics comes of Age - 'There's always a Third Way' -- 10.1 Aims -- 10.2 The age of information -- 10.3 Resampling -- 10.3.1 Randomisation tests -- 10.3.2 Bootstrapping -- 10.3.3 Comparing two groups -- 10.4 An introduction to controlling the false discovery rate -- 10.5 Summary -- References -- Appendix A: Data Used for Statistical Analyses (Chapters 6,7 and 10) -- Appendix B: Statistical Software Outputs (Chapters 6-9) -- Index -- EULA
- Intro -- Starting Out in Statistics -- Contents -- Introduction - What's the Point of Statistics? -- Reference -- Basic Maths for Stats Revision -- Statistical Software Packages -- About the Companion Website -- 1 Introducing Variables, Populations and Samples - 'Variability is the Law of Life' -- 1.1 Aims -- 1.2 Biological data vary -- 1.3 Variables -- 1.4 Types of qualitative variables -- 1.4.1 Nominal variables -- 1.4.2 Multiple response variables -- 1.4.3 Preference variables -- 1.5 Types of quantitative variables -- 1.5.1 Discrete variables -- 1.5.2 Continuous variables -- 1.5.3 Ordinal variables - a moot point -- 1.6 Samples and populations -- 1.7 Summary -- Reference -- 2 Study Design and Sampling - 'Design is Everything. Everything!' -- 2.1 Aims -- 2.2 Introduction -- 2.3 One sample -- 2.4 Related samples -- 2.5 Independent samples -- 2.6 Factorial designs -- 2.7 Observational study designs -- 2.7.1 Cross-sectional design -- 2.7.2 Case-control design -- 2.7.3 Longitudinal studies -- 2.7.4 Surveys -- 2.8 Sampling -- 2.9 Reliability and validity -- 2.10 Summary -- References -- 3 Probability - 'Probability ... So True in General' -- 3.1 Aims -- 3.2 What is probability? -- 3.3 Frequentist probability -- 3.4 Bayesian probability -- 3.5 The likelihood approach -- 3.6 Summary -- References -- 4 Summarising Data - 'Transforming Data into Information' -- 4.1 Aims -- 4.2 Why summarise? -- 4.3 Summarising data numerically - descriptive statistics -- 4.3.1 Measures of central location -- 4.3.2 Measures of dispersion -- 4.4 Summarising data graphically -- 4.5 Graphs for summarising group data -- 4.5.1 The bar graph -- 4.5.2 The error plot -- 4.5.3 The box-and-whisker plot -- 4.5.4 Comparison of graphs for group data -- 4.5.5 A little discussion on error bars -- 4.6 Graphs for displaying relationships between variables
- Chapter 8 -- SPSS for two-way chi-squared -- Chapter 9 -- Index -- End User License Agreement
- Intro -- Titlepage -- Copyright -- Dedication -- Introduction - What's the Point of Statistics? -- Reference -- Basic Maths for Stats Revision -- Arithmetic -- Algebra -- Simplifying numbers -- Centring and standardising data -- Numerical accuracy -- Statistical Software Packages -- About the Companion Website -- 1 Introducing Variables, Populations and Samples - 'Variability is the Law of Life' -- 1.1 Aims -- 1.2 Biological data vary -- 1.3 Variables -- 1.4 Types of qualitative variables -- 1.5 Types of quantitative variables -- 1.6 Samples and populations -- 1.7 Summary -- Reference -- 2 Study Design and Sampling - 'Design is Everything. Everything!' -- 2.1 Aims -- 2.2 Introduction -- 2.3 One sample -- 2.4 Related samples -- 2.5 Independent samples -- 2.6 Factorial designs -- 2.7 Observational study designs -- 2.8 Sampling -- 2.9 Reliability and validity -- 2.10 Summary -- Notes -- References -- 3 Probability - 'Probability ... So True in General' -- 3.1 Aims -- 3.2 What is probability? -- 3.3 Frequentist probability -- 3.4 Bayesian probability -- 3.5 The likelihood approach -- 3.6 Summary -- Notes -- References -- 4 Summarising Data - 'Transforming Data into Information' -- 4.1 Aims -- 4.2 Why summarise? -- 4.3 Summarising data numerically - descriptive statistics -- 4.4 Summarising data graphically -- 4.5 Graphs for summarising group data -- 4.6 Graphs for displaying relationships between variables -- 4.7 Displaying complex (multidimensional) data -- 4.8 Displaying proportions or percentages -- 4.9 Summary -- References -- 5 Statistical Power - '… Find out the Cause of this Effect' -- 5.1 Aims -- 5.2 Power -- 5.3 From doormats to aortic valves -- 5.4 More on the normal distribution -- 5.5 How is power useful? -- 5.6 The problem with p values -- 5.7 Confidence intervals and power -- 5.8 When to stop collecting data
- 5.9 Likelihood versus null hypothesis testing -- 5.10 Summary -- Notes -- References -- 6 Comparing Groups using t-Tests and ANOVA - 'To Compare is not to Prove' -- 6.1 Aims -- 6.2 Are men taller than women? -- 6.3 The central limit theorem revisited -- 6.4 Student's t-test -- 6.5 Assumptions of the t-test -- 6.6 Dependent t-test -- 6.7 What type of data can be tested using t-tests? -- 6.8 Data transformations -- 6.9 Proof is not the answer -- 6.10 The problem of multiple testing -- 6.11 Comparing multiple means - the principles of analysis of variance -- 6.12 Two-way ANOVA -- 6.13 Summary -- Notes -- References -- 7 Relationships between Variables: Regression and Correlation - 'In Relationships … Concentrate only on what is most Significant and Important' -- 7.1 Aims -- 7.2 Linear regression -- 7.3 Correlation -- 7.4 Multiple regression -- 7.5 Summary -- References -- 8 Analysis of Categorical Data - 'If the Shoe Fits …' -- 8.1 Aims -- 8.2 One-way chi-squared -- 8.3 Two-way chi-squared -- 8.4 The odds ratio -- 8.5 Summary -- Notes -- References -- 9 Non-Parametric Tests - 'An Alternative to other Alternatives' -- 9.1 Aims -- 9.2 Introduction -- 9.3 One sample sign test -- 9.4 Non-parametric equivalents to parametric tests -- 9.5 Two independent samples -- 9.6 Paired samples -- 9.7 Kruskal-Wallis one-way analysis of variance -- 9.8 Friedman test for correlated samples -- 9.9 Conclusion -- 9.10 Summary -- Notes -- References -- 10 Resampling Statistics comes of Age - 'There's always a Third Way' -- 10.1 Aims -- 10.2 The age of information -- 10.3 Resampling -- 10.4 An introduction to controlling the false discovery rate -- 10.5 Summary -- Notes -- References -- Appendix A: Data Used for Statistical Analyses (Chapters 6,7 and 10) -- Chapter 6 -- Chapter 7 -- Chapter 10 -- Appendix B: Statistical Software Outputs (Chapters 6-9) -- Chapter 6 -- Chapter 7