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| List of partial statistical tables | |
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| Preface | |
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| Introduction to statistics and simple descriptive statistics | |
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| Statistics and scientific enquiry | |
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| Basic definitions | |
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| Variables and constants | |
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| Scales of measurement | |
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| Accuracy and precision | |
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| Independent and dependent variables | |
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| Control and experimental groups | |
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| Samples and statistics, populations and parameters. Descriptive and inferential statistics. A few words about sampling | |
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| Statistical notation | |
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| Chapter 1 key concepts | |
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| Chapter 1 exercises | |
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| The first step in data analysis: summarizing and displaying data. Computing descriptive statistics | |
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| Frequency distributions | |
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| Frequency distributions of discontinuous numeric and qualitative variables | |
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| Frequency distributions of continuous numeric variables | |
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| Stem-and-leaf displays of data | |
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| Graphing data | |
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| Bar graphs and pie charts | |
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| Histograms | |
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| Polygons | |
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| Box plots | |
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| Descriptive statistics. Measures of central tendency and dispersion | |
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| Measures of central tendency | |
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| Measures of variation | |
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| Chapter 2 key concepts | |
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| Computer resources | |
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| Chapter 2 exercises | |
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| Probability and statistics | |
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| Random sampling and probability distributions | |
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| The probability distribution of qualitative and discontinuous numeric variables | |
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| The binomial distribution | |
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| The Poisson distribution | |
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| Bayes' theorem | |
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| The probability distribution of continuous variables | |
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| z scores and the standard normal distribution (SND) | |
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| Percentile ranks and percentiles | |
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| The probability distribution of sample means | |
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| Is my bell shape normal? | |
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| Chapter 3 key concepts | |
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| Computer resources | |
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| Chapter 3 exercises | |
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| Hypothesis testing and estimation | |
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| Different approaches to hypothesis testing and estimation | |
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| The classical significance testing approach | |
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| The maximum likelihood approach | |
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| The Bayesian approach | |
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| Estimation | |
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| Confidence limits and confidence interval | |
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| Point estimation | |
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| Hypothesis testing | |
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| The principles of hypothesis testing | |
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| Errors and power in hypothesis testing | |
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| Hypothesis tests using z scores | |
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| One-and two-tailed hypothesis tests | |
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| Assumptions of statistical tests | |
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| Hypothesis testing with the t distribution | |
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| Hypothesis tests using t scores | |
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| Reporting hypothesis tests | |
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| The classical significance testing approach. A conclusion | |
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| Chapter 4 key concepts | |
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| Chapter 4 exercises | |
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| The difference between two means | |
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| The un-paired t test | |
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| Assumptions of the un-paired t test | |
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| The comparison of a single observation with the mean of a sample | |
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| The paired t test | |
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| Assumptions of the paired t test | |
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| Chapter 5 key concepts | |
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| Computer resources | |
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| Chapter 5 exercises | |
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| The analysis of variance (ANOVA) | |
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| Model I and model II ANOVA | |
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| Model I, one-way ANOVA. Introduction and nomenclature | |
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| ANOVA assumptions | |
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| Post-hoc tests | |
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| The Scheff� test | |
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| Model I, two-way ANOVA | |
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| Other ANOVA designs | |
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| Chapter 6 key concepts | |
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| Computer resources | |
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| Chapter 6 exercises | |
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| Non-parametric tests for the comparison of samples | |
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| Ranking data | |
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| The Mann-Whitney U test for a two-sample un-matched design | |
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| The Kruskal-Wallis for a one-way, model I ANOVA design | |
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| The Wilcoxon signed-ranks test for a two-sample paired design | |
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| Chapter 7 key concepts | |
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| Computer resources | |
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| Chapter 7 exercises | |
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| The analysis of frequencies | |
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| The X<sup>2</sup> test for goodness-of-fit | |
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| The Kolmogorov-Smirnov one sample test | |
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| The X<sup>2</sup> test for independence of variables | |
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| Yates' correction for continuity | |
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| The likelihood ratio test (the G test) | |
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| Fisher's exact test | |
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| The McNemar test for a matched design | |
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| Tests of goodness-of-fit and independence of variables. Conclusion | |
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| The odds ratio (OR): measuring the degree of the association between two discrete variables | |
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| The relative risk (RR): measuring the degree of the association between two discrete variables | |
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| Chapter 8 key concepts | |
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| Computer resources | |
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| Chapter 8 exercises | |
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| Correlation analysis | |
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| The Pearson product-moment correlation | |
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| Non-parametric tests of correlation | |
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| The Spearman correlation coefficient r<sub>s</sub> | |
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| Kendall's coefficient of rank correlation-tau (�) | |
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| Chapter 9 key concepts | |
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| Chapter 9 exercises | |
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| Simple linear regression | |
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| An overview of regression analysis | |
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| Regression analysis step-by-step | |
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| The data are plotted and inspected to detect violations of the linearity and homoscedasticity assumptions | |
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| The relation between the X and the Y is described mathematically with an equation | |
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| The regression analysis is expressed as an analysis of the variance of Y | |
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| The null hypothesis that the parametric value of the slope is not statistically different from 0 is tested | |
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| The regression equation is used to predict values of Y | |
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| Lack of fit is assessed | |
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| The residuals are analyzed | |
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| Transformations in regression analysis | |
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| Chapter 10 key concepts | |
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| Computer resources | |
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| Chapter 10 exercises | |
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| Advanced topics in regression analysis | |
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| The multiple regression model | |
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| The problem of multicollinearity/collinearity | |
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| The algebraic computation of the multiple regression equation | |
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| An overview of multiple-regression-model building | |
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| Dummy independent variables | |
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| An overview of logistic regression | |
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| Writing up your results | |
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| Chapter 11 key concepts | |
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| Computer resources | |
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| Chapter 11 exercises | |
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| References | |
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| Index | |