ISBN:
1139423320
,
1139022695
,
9781139423328
,
9781139022699
,
9781139417181
,
9781139421270
,
9781299006362
,
9781139415033
Language:
English
Pages:
1 Online-Ressource (xiv, 264 pages)
,
illustrations
Edition:
2nd ed
Parallel Title:
Erscheint auch als Madrigal, Lorena Statistics for anthropology
DDC:
301.072/7
Keywords:
Anthropology Statistical methods
;
SCIENCE ; Life Sciences ; Zoology ; Primatology
;
SOCIAL SCIENCE ; Anthropology ; General
;
SOCIAL SCIENCE ; Regional Studies
;
SOCIAL SCIENCE ; Sociology ; General
;
Anthropology ; Statistical methods
Abstract:
"Anthropology as a discipline is rapidly becoming more quantitative, and anthropology students are now required to develop sophisticated statistical skills. This book provides students of anthropology with a clear, step-by-step guide to univariate statistical methods, demystifying the aspects that are often seen as difficult or impenetrable. Explaining the central role of statistical methods in anthropology, and using only anthropological examples, the book provides a solid footing in statistical techniques. Beginning with basic descriptive statistics, this new edition also covers more advanced methods such as analyses of frequencies and variance, and simple and multiple regression analysis with dummy and continuous variables. It addresses commonly encountered problems such as small samples and non-normality. Each statistical technique is accompanied by clearly worked examples, and the chapters end with practice problem sets"--
Abstract:
3.1 Random sampling and probability distributions -- 3.2 The probability distribution of qualitative and discontinuous numeric variables -- 3.3 The binomial distribution -- 3.4 The Poisson distribution -- 3.5 Bayes' theorem -- 3.6 The probability distribution of continuous variables -- 3.6.1 z scores and the standard normal distribution (SND) -- 3.6.2 Percentile ranks and percentiles -- 3.6.3 The probability distribution of sample means -- 3.6.4 Is my bell shape normal-- 3.7 Chapter 3 key concepts -- 3.8 Computer resources -- 3.9 Chapter 3 exercises -- 4 Hypothesis testing and estimation -- 4.1 Different approaches to hypothesis testing and estimation -- 4.1.1 The classical significance testing approach -- 4.1.2 The maximum likelihood approach -- 4.1.3 The Bayesian approach -- 4.2 Estimation -- 4.2.1 Confidence limits and confidence interval -- 4.2.2 Point estimation -- 4.3 Hypothesis testing -- 4.3.1 The principles of hypothesis testing -- (1) The null (H0) and alternative (H1) hypotheses are stated -- (2) Establish the significance level -- (3) Data collection -- (4) Compare the sample with the null hypothesis, and reach a conclusion about the latter -- 4.3.2 Errors and power in hypothesis testing -- 4.3.2.1 Type I error (β) -- 4.3.2.2 Type II error (β) -- 4.3.2.3 Power of statistical tests (1 -- β) -- 4.3.3 Hypothesis tests using z scores -- 4.3.4 One- and two-tailed hypothesis tests -- 4.3.5 Assumptions of statistical tests -- 4.3.6 Hypothesis testing with the t distribution -- 4.3.7 Hypothesis tests using t scores -- 4.3.8 Reporting hypothesis tests -- 4.3.9 The classical significance testing approach. A conclusion -- 4.4 Chapter 4 key concepts -- 4.5 Chapter 4 exercises -- 5 The difference between two means -- 5.1 The un-paired t test -- 5.1.1 Assumptions of the un-paired t test -- 5.1.1.1 Random sampling -- 5.1.1.2 Independence of variates.
Abstract:
9.2.2 Kendalls coefficient of rank correlation -- tau (τ) -- 9.3 Chapter 9 key concepts -- 9.4 Chapter 9 exercises -- 10 Simple linear regression -- 10.1 An overview of regression analysis -- 10.2 Regression analysis step-by-step -- 10.2.1 The data are plotted and inspected to detect violations of the linearity and homoscedasticity assumptions -- 10.2.2 The relation between the X and the Y is described mathematically with an equation -- 10.2.3 The regression analysis is expressed as an analysis of the variance of Y -- 10.2.4 The null hypothesis that the parametric value of the slope is not statistically different from 0 is tested -- 10.2.5 The regression equation is used to predict values of Y -- 10.2.6 Lack of fit is assessed -- 10.2.7 The residuals are analyzed -- 10.3 Transformations in regression analysis -- 10.4 Chapter 10 key concepts -- 10.5 Computer resources -- 10.6 Chapter 10 exercises -- 11 Advanced topics in regression analysis -- 11.1 The multiple regression model -- 11.1.1 The problem of multicollinearity/collinearity -- 11.1.2 The algebraic computation of the multiple regression equation -- 11.1.3 An overview of multiple-regression-model building -- 11.1.4 Dummy independent variables -- 11.2 An overview of logistic regression -- 11.3 Writing up your results -- 11.4 Chapter 11 key concepts -- 11.5 Computer resources -- 11.6 Chapter 11 exercises -- References -- Index.
Abstract:
List of partial statistical tables -- Preface -- Introduction to statistics and simple descriptive statistics -- The first step in data analysis: summarizing and displaying data : computing descriptive statistics -- Probability and statistics -- Hypothesis testing and estimation -- The difference between two means -- The analysis of variance (ANOVA) -- Non-parametric tests for the comparison of samples -- The analysis of frequencies -- Correlation analysis -- Simple linear regression -- Advanced topics in regression analysis.
Note:
Includes bibliographical references and index
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