$58.19
What is included with this book?
List of partial statistical tables | p. xi |
Preface | p. xiii |
Introduction to statistics and simple descriptive statistics | p. 1 |
Statistics and scientific enquiry | p. 1 |
Basic definitions | p. 3 |
Variables and constants | p. 3 |
Scales of measurement | p. 4 |
Accuracy and precision | p. 6 |
Independent and dependent variables | p. 6 |
Control and experimental groups | p. 7 |
Samples and statistics, populations and parameters. Descriptive and inferential statistics. A few words about sampling | p. 8 |
Statistical notation | p. 9 |
Chapter 1 key concepts | p. 12 |
Chapter 1 exercises | p. 12 |
The first step in data analysis: summarizing and displaying data. Computing descriptive statistics | p. 13 |
Frequency distributions | p. 13 |
Frequency distributions of discontinuous numeric and qualitative variables | p. 13 |
Frequency distributions of continuous numeric variables | p. 15 |
Stem-and-leaf displays of data | p. 17 |
Graphing data | p. 18 |
Bar graphs and pie charts | p. 19 |
Histograms | p. 21 |
Polygons | p. 21 |
Box plots | p. 21 |
Descriptive statistics. Measures of central tendency and dispersion | p. 25 |
Measures of central tendency | p. 26 |
Measures of variation | p. 29 |
Chapter 2 key concepts | p. 39 |
Computer resources | p. 40 |
Chapter 2 exercises | p. 40 |
Probability and statistics | p. 42 |
Random sampling and probability distributions | p. 43 |
The probability distribution of qualitative and discontinuous numeric variables | p. 44 |
The binomial distribution | p. 46 |
The Poisson distribution | p. 48 |
Bayes' theorem | p. 53 |
The probability distribution of continuous variables | p. 57 |
z scores and the standard normal distribution (SND) | p. 63 |
Percentile ranks and percentiles | p. 71 |
The probability distribution of sample means | p. 73 |
Is my bell shape normal? | p. 77 |
Chapter 3 key concepts | p. 78 |
Computer resources | p. 79 |
Chapter 3 exercises | p. 80 |
Hypothesis testing and estimation | p. 83 |
Different approaches to hypothesis testing and estimation | p. 83 |
The classical significance testing approach | p. 83 |
The maximum likelihood approach | p. 84 |
The Bayesian approach | p. 84 |
Estimation | p. 84 |
Confidence limits and confidence interval | p. 85 |
Point estimation | p. 89 |
Hypothesis testing | p. 90 |
The principles of hypothesis testing | p. 90 |
Errors and power in hypothesis testing | p. 93 |
Hypothesis tests using z scores | p. 98 |
One-and two-tailed hypothesis tests | p. 100 |
Assumptions of statistical tests | p. 101 |
Hypothesis testing with the t distribution | p. 103 |
Hypothesis tests using t scores | p. 104 |
Reporting hypothesis tests | p. 105 |
The classical significance testing approach. A conclusion | p. 106 |
Chapter 4 key concepts | p. 106 |
Chapter 4 exercises | p. 107 |
The difference between two means | p. 108 |
The un-paired t test | p. 108 |
Assumptions of the un-paired t test | p. 112 |
The comparison of a single observation with the mean of a sample | p. 116 |
The paired t test | p. 117 |
Assumptions of the paired t test | p. 119 |
Chapter 5 key concepts | p. 120 |
Computer resources | p. 120 |
Chapter 5 exercises | p. 121 |
The analysis of variance (ANOVA) | p. 122 |
Model I and model II ANOVA | p. 122 |
Model I, one-way ANOVA. Introduction and nomenclature | p. 123 |
ANOVA assumptions | p. 131 |
Post-hoc tests | p. 132 |
The Scheffé test | p. 133 |
Model I, two-way ANOVA | p. 135 |
Other ANOVA designs | p. 143 |
Chapter 6 key concepts | p. 144 |
Computer resources | p. 145 |
Chapter 6 exercises | p. 145 |
Non-parametric tests for the comparison of samples | p. 146 |
Ranking data | p. 147 |
The Mann-Whitney U test for a two-sample un-matched design | p. 148 |
The Kruskal-Wallis for a one-way, model I ANOVA design | p. 153 |
The Wilcoxon signed-ranks test for a two-sample paired design | p. 159 |
Chapter 7 key concepts | p. 164 |
Computer resources | p. 164 |
Chapter 7 exercises | p. 164 |
The analysis of frequencies | p. 166 |
The X^{2} test for goodness-of-fit | p. 166 |
The Kolmogorov-Smirnov one sample test | p. 170 |
The X^{2} test for independence of variables | p. 172 |
Yates' correction for continuity | p. 175 |
The likelihood ratio test (the G test) | p. 176 |
Fisher's exact test | p. 178 |
The McNemar test for a matched design | p. 183 |
Tests of goodness-of-fit and independence of variables. Conclusion | p. 184 |
The odds ratio (OR): measuring the degree of the association between two discrete variables | p. 185 |
The relative risk (RR): measuring the degree of the association between two discrete variables | p. 188 |
Chapter 8 key concepts | p. 190 |
Computer resources | p. 190 |
Chapter 8 exercises | p. 191 |
Correlation analysis | p. 193 |
The Pearson product-moment correlation | p. 193 |
Non-parametric tests of correlation | p. 199 |
The Spearman correlation coefficient r_{s} | p. 199 |
Kendall's coefficient of rank correlation-tau (¿) | p. 202 |
Chapter 9 key concepts | p. 208 |
Chapter 9 exercises | p. 208 |
Simple linear regression | p. 209 |
An overview of regression analysis | p. 210 |
Regression analysis step-by-step | p. 214 |
The data are plotted and inspected to detect violations of the linearity and homoscedasticity assumptions | p. 214 |
The relation between the X and the Y is described mathematically with an equation | p. 215 |
The regression analysis is expressed as an analysis of the variance of Y | p. 215 |
The null hypothesis that the parametric value of the slope is not statistically different from 0 is tested | p. 217 |
The regression equation is used to predict values of Y | p. 217 |
Lack of fit is assessed | p. 219 |
The residuals are analyzed | p. 221 |
Transformations in regression analysis | p. 225 |
Chapter 10 key concepts | p. 232 |
Computer resources | p. 232 |
Chapter 10 exercises | p. 232 |
Advanced topics in regression analysis | p. 234 |
The multiple regression model | p. 234 |
The problem of multicollinearity/collinearity | p. 235 |
The algebraic computation of the multiple regression equation | p. 236 |
An overview of multiple-regression-model building | p. 240 |
Dummy independent variables | p. 247 |
An overview of logistic regression | p. 251 |
Writing up your results | p. 255 |
Chapter 11 key concepts | p. 255 |
Computer resources | p. 256 |
Chapter 11 exercises | p. 256 |
References | p. 257 |
Index | p. 260 |
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