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| List of figures | |
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| Preface | |
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| Preface to the Second Edition | |
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| Introduction | |
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| Descriptive statistics | |
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| Measures of 'central tendency' | |
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| Measures of 'spread' | |
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| Describing a set of data: in conclusion | |
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| Comparing two sets of data with descriptive statistics | |
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| Some important information about numbers | |
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| Standard scores | |
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| Comparing scores from different distributions | |
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| The Normal Distribution | |
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| The Standard Normal Distribution | |
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| Introduction to hypothesis testing | |
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| Testing an hypothesis | |
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| The logic of hypothesis testing | |
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| One- and two-tailed predictions | |
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| Sampling | |
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| Populations and samples | |
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| Selecting a sample | |
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| Sample statistics and population parameters | |
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| Summary | |
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| Hypothesis testing with one sample | |
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| An example | |
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| When we do not have the known population standard deviation | |
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| Confidence intervals | |
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| Hypothesis testing with one sample: in conclusion | |
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| Selecting samples for comparison | |
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| Designing experiments to compare samples | |
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| The interpretation of sample differences | |
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| Hypothesis testing with two samples | |
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| The assumptions of the two sample t test | |
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| Related or independent samples | |
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| The related t test | |
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| The independent t test | |
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| Confidence intervals | |
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| Significance, error and power | |
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| Type I and Type II errors | |
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| Statistical power | |
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| The power of a test | |
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| The choice of [alpha] level | |
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| Effect size | |
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| Sample size | |
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| Conclusion | |
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| Introduction to the analysis of variance | |
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| Factors and conditions | |
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| The problem of many conditions and the t test | |
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| Why do scores vary in an experiment? | |
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| The process of analysing variability | |
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| The F distribution | |
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| Conclusion | |
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| One factor independent measures ANOVA | |
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| Analysing variability in the independent measures ANOVA | |
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| Rejecting the null hypothesis | |
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| Unequal sample sizes | |
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| The relationship of F to t | |
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| Multiple comparisons | |
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| The Tukey test (for all pairwise comparisons) | |
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| The Scheffe test (for complex comparisons) | |
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| One factor repeated measures ANOVA | |
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| Deriving the F value | |
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| Multiple comparisons | |
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| The interaction of factors in the analysis of variance | |
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| Interactions | |
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| Dividing up the between conditions sums of squares | |
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| Simple main effects | |
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| Conclusion | |
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| Calculating the two factor ANOVA | |
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| The two factor independent measures ANOVA | |
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| The two factor mixed design ANOVA | |
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| The two factor repeated measures ANOVA | |
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| A non-significant interaction | |
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| An introduction to nonparametric analysis | |
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| Calculating ranks | |
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| Two sample nonparametric analyses | |
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| The Mann-Whitney U test (for independent samples) | |
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| The Wilcoxon signed-ranks test (for related samples) | |
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| One factor ANOVA for ranked data | |
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| Kruskal-Wallis test (for independent measures) | |
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| The Friedman test (for related samples) | |
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| Analysing frequency data: chi-square | |
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| Nominal data, categories and frequency counts | |
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| Introduction to X[superscript 2] | |
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| Chi-square (X[superscript 2]) as a 'goodness of fit' test | |
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| Chi-square (X[superscript 2]) as a test of independence | |
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| The chi-square distribution | |
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| The assumptions of the X[superscript 2] test | |
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| Linear correlation and regression | |
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| Introduction | |
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| Pearson r correlation coefficient | |
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| Linear regression | |
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| The interpretation of correlation and regression | |
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| Problems with correlation and regression | |
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| The standard error of the estimate | |
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| The Spearman r[subscript S] correlation coefficient | |
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| Multiple correlation and regression | |
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| Introduction to multivariate analysis | |
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| Partial correlation | |
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| Multiple correlation | |
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| Multiple regression | |
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| Complex analyses and computers | |
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| Undertaking data analysis by computer | |
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| Complex analyses | |
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| Reliability | |
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| Factor analysis | |
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| Multivariate analysis of variance (MANOVA) | |
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| Discriminant function analysis | |
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| Conclusion | |
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| An introduction to the general linear model | |
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| Models | |
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| An example of a linear model | |
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| Modelling data | |
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| The model: the regression equation | |
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| Selecting a good model | |
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| Comparing samples (the analysis of variance once again) | |
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| Explaining variations in the data | |
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| The general linear model | |
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| Notes | |
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| Glossary | |
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| References | |
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| Acknowledgements and statistical tables | |
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| The standard normal distribution tables | |
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| Critical values of the t distribution | |
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| Critical values of the F distribution | |
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| Critical values of the Studentized range statistic, q | |
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| Critical values of the Mann-Whitney U statistic | |
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| Critical values of the Wilcoxon T statistic | |
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| Critical values of the chi-square (X[superscript 2]) distribution | |
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| Table of probabilities for X[superscript 2 subscript r] when k and n are small | |
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| Critical values of the Pearson r correlation coefficient | |
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| Critical values of the Spearman r[subscript S] ranked correlation coefficient | |
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| Index | |