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Statistics for Anthropology 2nd Revised edition [Pehme köide]

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(University of South Florida)
  • Formaat: Paperback / softback, 278 pages, kõrgus x laius x paksus: 247x174x14 mm, kaal: 560 g, Worked examples or Exercises; 30 Tables, black and white; 21 Halftones, unspecified; 41 Line drawings, unspecified
  • Ilmumisaeg: 01-Mar-2012
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521147085
  • ISBN-13: 9780521147088
Teised raamatud teemal:
Statistics for Anthropology 2nd Revised editionSuurem pilt
  • Formaat: Paperback / softback, 278 pages, kõrgus x laius x paksus: 247x174x14 mm, kaal: 560 g, Worked examples or Exercises; 30 Tables, black and white; 21 Halftones, unspecified; 41 Line drawings, unspecified
  • Ilmumisaeg: 01-Mar-2012
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521147085
  • ISBN-13: 9780521147088
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780521147088.html
Märksõnad:
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, 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. Many of the datasets are available for download at www.cambridge.org/9780521147088.

Muu info

A clear, step-by-step guide to statistical methods for anthropology students, providing a solid footing in basic statistical techniques.
List of partial statistical tables
xi
Preface xiii
1 Introduction to statistics and simple descriptive statistics
1(12)
1.1 Statistics and scientific enquiry
1(2)
1.2 Basic definitions
3(6)
1.2.1 Variables and constants
3(1)
1.2.2 Scales of measurement
4(2)
1.2.3 Accuracy and precision
6(1)
1.2.4 Independent and dependent variables
6(1)
1.2.5 Control and experimental groups
7(1)
1.2.6 Samples and statistics, populations and parameters. Descriptive and inferential statistics. A few words about sampling
8(1)
1.3 Statistical notation
9(3)
1.4
Chapter 1 key concepts
12(1)
1.5
Chapter 1 exercises
12(1)
2 The first step in data analysis: summarizing and displaying data. Computing descriptive statistics
13(29)
2.1 Frequency distributions
13(5)
2.1.1 Frequency distributions of discontinuous numeric and qualitative variables
13(2)
2.1.2 Frequency distributions of continuous numeric variables
15(2)
2.1.3 Stem-and-leaf displays of data
17(1)
2.2 Graphing data
18(7)
2.2.1 Bar graphs and pie charts
19(2)
2.2.2 Histograms
21(1)
2.2.3 Polygons
21(1)
2.2.4 Box plots
21(4)
2.3 Descriptive statistics. Measures of central tendency and dispersion
25(14)
2.3.1 Measures of central tendency
26(3)
2.3.2 Measures of variation
29(10)
2.4
Chapter 2 key concepts
39(1)
2.5 Computer resources
40(1)
2.6
Chapter 2 exercises
40(2)
3 Probability and statistics
42(41)
3.1 Random sampling and probability distributions
43(1)
3.2 The probability distribution of qualitative and discontinuous numeric variables
44(2)
3.3 The binomial distribution
46(2)
3.4 The Poisson distribution
48(5)
3.5 Bayes' theorem
53(4)
3.6 The probability distribution of continuous variables
57(21)
3.6.1 z scores and the standard normal distribution (SND)
63(8)
3.6.2 Percentile ranks and percentiles
71(2)
3.6.3 The probability distribution of sample means
73(4)
3.6.4 Is my bell shape normal?
77(1)
3.7
Chapter 3 key concepts
78(1)
3.8 Computer resources
79(1)
3.9
Chapter 3 exercises
80(3)
4 Hypothesis testing and estimation
83(25)
4.1 Different approaches to hypothesis testing and estimation
83(1)
4.1.1 The classical significance testing approach
83(1)
4.1.2 The maximum likelihood approach
84(1)
4.1.3 The Bayesian approach
84(1)
4.2 Estimation
84(6)
4.2.1 Confidence limits and confidence interval
85(4)
4.2.2 Point estimation
89(1)
4.3 Hypothesis testing
90(16)
4.3.1 The principles of hypothesis testing
90(3)
4.3.2 Errors and power in hypothesis testing
93(5)
4.3.3 Hypothesis tests using z scores
98(2)
4.3.4 One- and two-tailed hypothesis tests
100(1)
4.3.5 Assumptions of statistical tests
101(2)
4.3.6 Hypothesis testing with the t distribution
103(1)
4.3.7 Hypothesis tests using t scores
104(1)
4.3.8 Reporting hypothesis tests
105(1)
4.3.9 The classical significance testing approach. A conclusion
106(1)
4.4
Chapter 4 key concepts
106(1)
4.5
Chapter 4 exercises
107(1)
5 The difference between two means
108(14)
5.1 The un-paired t test
108(8)
5.1.1 Assumptions of the un-paired t test
112(4)
5.2 The comparison of a single observation with the mean of a sample
116(1)
5.3 The paired t test
117(3)
5.3.1 Assumptions of the paired t test
119(1)
5.4
Chapter 5 key concepts
120(1)
5.5 Computer resources
120(1)
5.6
Chapter 5 exercises
121(1)
6 The analysis of variance (ANOVA)
122(24)
6.1 Model I and model II ANOVA
122(1)
6.2 Model I, one-way ANOVA. Introduction and nomenclature
123(8)
6.3 ANOVA assumptions
131(1)
6.4 Post-hoc tests
132(3)
6.4.1 The Scheffe test
133(2)
6.5 Model I, two-way ANOVA
135(8)
6.6 Other ANOVA designs
143(1)
6.7
Chapter 6 key concepts
144(1)
6.8 Computer resources
145(1)
6.9
Chapter 6 exercises
145(1)
7 Non-parametric tests for the comparison of samples
146(20)
7.1 Ranking data
147(1)
7.2 The Mann-Whitney U test for a two-sample un-matched design
148(5)
7.3 The Kruskal-Wallis for a one-way, model I ANOVA design
153(6)
7.4 The Wilcoxon signed-ranks test for a two-sample paired design
159(5)
7.5
Chapter 7 key concepts
164(1)
7.6 Computer resources
164(1)
7.7
Chapter 7 exercises
164(2)
8 The analysis of frequencies
166(27)
8.1 The X2 test for goodness-of-fit
166(4)
8.2 The Kolmogorov-Smirnov one sample test
170(2)
8.3 The X2 test for independence of variables
172(3)
8.4 Yates' correction for continuity
175(1)
8.5 The likelihood ratio test (the G test)
176(2)
8.6 Fisher's exact test
178(5)
8.7 The McNemar test for a matched design
183(1)
8.8 Tests of goodness-of-fit and independence of variables. Conclusion
184(1)
8.9 The odds ratio (OR): measuring the degree of the association between two discrete variables
185(3)
8.10 The relative risk (RR): measuring the degree of the association between two discrete variables
188(2)
8.11
Chapter 8 key concepts
190(1)
8.12 Computer resources
190(1)
8.13
Chapter 8 exercises
191(2)
9 Correlation analysis
193(16)
9.1 The Pearson product-moment correlation
193(6)
9.2 Non-parametric tests of correlation
199(9)
9.2.1 The Spearman correlation coefficient rs
199(3)
9.2.2 Kendall's coefficient of rank correlation - tau (τ)
202(6)
9.3
Chapter 9 key concepts
208(1)
9.4
Chapter 9 exercises
208(1)
10 Simple linear regression
209(25)
10.1 An overview of regression analysis
210(4)
10.2 Regression analysis step-by-step
214(11)
10.2.1 The data are plotted and inspected to detect violations of the linearity and homoscedasticity assumptions
214(1)
10.2.2 The relation between the X and the Y is described mathematically with an equation
215(1)
10.2.3 The regression analysis is expressed as an analysis of the variance of Y
215(2)
10.2.4 The null hypothesis that the parametric value of the slope is not statistically different from 0 is tested
217(1)
10.2.5 The regression equation is used to predict values of Y
217(2)
10.2.6 Lack of fit is assessed
219(2)
10.2.7 The residuals are analyzed
221(4)
10.3 Transformations in regression analysis
225(7)
10.4
Chapter 10 key concepts
232(1)
10.5 Computer resources
232(1)
10.6
Chapter 10 exercises
232(2)
11 Advanced topics in regression analysis
234(23)
11.1 The multiple regression model
234(17)
11.1.1 The problem of multicollinearity/collinearity
235(1)
11.1.2 The algebraic computation of the multiple regression equation
236(4)
11.1.3 An overview of multiple-regression-model building
240(7)
11.1.4 Dummy independent variables
247(4)
11.2 An overview of logistic regression
251(4)
11.3 Writing up your results
255(1)
11.4
Chapter 11 key concepts
255(1)
11.5 Computer resources
256(1)
11.6
Chapter 11 exercises
256(1)
References 257(3)
Index 260
Lorena Madrigal is Professor of Anthropology at the University of South Florida, Tampa. A biological anthropologist, she is particularly interested in the evolution of Afro and Indo Costa Rican populations residing on the Atlantic coast of Costa Rica. She is currently President of the American Association of Physical Anthropologists.