Introduction to Applied Statistics A Modelling Approach,9780198528951

Introduction to Applied Statistics A Modelling Approach

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Edition: 2nd
ISBN13: 9780198528951
ISBN10: 0198528957
Format: Paperback
Pub. Date: 2004-01-29
Publisher(s): Oxford University Press
List Price: $89.02

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Summary

This title includes the following features: Interdisciplinary acrossmedicine, the biological sciences, and the social sciences; Web-based data setsand R codes available; Web-based instructor's manual with teaching hints andsolutions; Many examples and exercises relating statistical principles toresearch; More than 70 illustrations

Table of Contents

1 Basic concepts 1(46)
1.1 Variables
2(4)
1.1.1 Definition
2(2)
1.1.2 Characteristics of observations
4(2)
1.1.3 Several variables
6(1)
1.2 Summarising data
6(13)
1.2.1 Tables
7(4)
1.2.2 Measuring size and variability
11(2)
1.2.3 Graphics
13(6)
1.2.4 Detecting possible dependencies
19(1)
1.3 Probability
19(14)
1.3.1 Definition
20(4)
1.3.2 Probability laws
24(5)
1.3.3 Plotting probabilities
29(1)
1.3.4 Multinormal distribution
30(3)
1.4 Planning a study
33(10)
1.4.1 Protocols
33(1)
1.4.2 Observational surveys and experiments
34(6)
1.4.3 Study designs
40(3)
1.5 Exercises
43(4)
2 Categorical data 47(62)
2.1 Measures of dependence
47(13)
2.1.1 Estimation
48(3)
2.1.2 Independence
51(2)
2.1.3 Comparison of probabilities
53(4)
2.1.4 Characteristics of the odds ratio
57(2)
2.1.5 Simpson's paradox
59(1)
2.2 Models for binary response variables
60(25)
2.2.1 Models based on linear functions
61(4)
2.2.2 Logistic models
65(5)
2.2.3 One polytomous explanatory variable
70(2)
2.2.4 Several explanatory variables
72(7)
2.2.5 Logistic regression
79(6)
2.3 Polytomous response variables
85(18)
2.3.1 Polytomous logistic models
85(7)
2.3.2 Log linear models
92(4)
2.3.3 Log linear regression
96(3)
2.3.4 Ordinal response
99(4)
2.4 Exercises
103(6)
3 Inference 109(38)
3.1 Goals of inference
109(3)
3.1.1 Discovery and decisions
110(1)
3.1.2 Types of model selection
111(1)
3.2 Likelihood
112(12)
3.2.1 Likelihood function
113(2)
3.2.2 Maximum likelihood estimate
115(1)
3.2.3 Normed likelihood and deviance
116(5)
3.2.4 Standard errors
121(3)
3.3 Two special models
124(3)
3.3.1 Saturated models
124(1)
3.3.2 Null models
125(2)
3.4 Calibrating the likelihood
127(10)
3.4.1 Degrees of freedom
127(2)
3.4.2 Model selection criteria
129(1)
3.4.3 Significance tests
130(5)
3.4.4 Prior probability
135(2)
3.5 Goodness-of-fit
137(4)
3.5.1 Global fit
137(1)
3.5.2 Residuals and diagnostics
138(3)
3.6 Sample size calculation
141(4)
3.7 Exercises
145(2)
4 Probability distributions 147(86)
4.1 Constructing probability distributions
147(4)
4.1.1 Multinomial distribution
147(1)
4.1.2 Density functions
148(3)
4.2 Distributions for ordinal variables
151(6)
4.2.1 Uniform distribution
151(3)
4.2.2 Zeta distribution
154(3)
4.3 Distributions for counts
157(23)
4.3.1 Poisson distribution
157(7)
4.3.2 Geometric distribution
164(4)
4.3.3 Binomial distribution
168(5)
4.3.4 Negative binomial distribution
173(5)
4.3.5 Beta-binomial distribution
178(2)
4.4 Distributions for measurement errors
180(17)
4.4.1 Normal distribution
180(6)
4.4.2 Logistic distribution
186(3)
4.4.3 Laplace distribution
189(2)
4.4.4 Cauchy distribution
191(2)
4.4.5 Student t distribution
193(4)
4.5 Distributions for durations
197(16)
4.5.1 Intensity and survivor functions
198(1)
4.5.2 Exponential distribution
199(4)
4.5.3 Weibull distribution
203(3)
4.5.4 Gamma distribution
206(3)
4.5.5 Inverse Gauss distribution
209(4)
4.6 Transforming the response
213(9)
4.6.1 Log transformation
215(4)
4.6.2 Exponential transformation
219(1)
4.6.3 Power transformations
220(2)
4.7 Special families
222(3)
4.7.1 Location-scale family
222(1)
4.7.2 Exponential family
222(3)
4.8 Exercises
225(8)
5 Normal regression and ANOVA 233(34)
5.1 General regression models
233(3)
5.1.1 More assumptions or more data
233(2)
5.1.2 Generalised linear models
235(1)
5.1.3 Location regression models
236(1)
5.2 Linear regression
236(11)
5.2.1 One explanatory variable
237(7)
5.2.2 Multiple regression
244(3)
5.3 Analysis of variance
247(12)
5.3.1 One explanatory variable
248(3)
5.3.2 Two explanatory variables
251(3)
5.3.3 Matched pairs
254(1)
5.3.4 Analysis of covariance
255(4)
5.4 Correlation
259(2)
5.5 Sample size calculation
261(1)
5.6 Exercises
262(5)
6 Dependent responses 267(22)
6.1 Repeated measurements
267(2)
6.2 Time series
269(6)
6.2.1 Markov chains
269(4)
6.2.2 Autoregression
273(2)
6.3 Clustering
275(3)
6.4 Life tables
278(6)
6.4.1 One possible event
278(4)
6.4.2 Repeated events
282(2)
6.5 Exercises
284(5)
7 Where to now? 289(4)
A Tables 293(16)
A.1 P-values from the x2 distribution
293(2)
A.2 P-values from the Student t distribution
295(1)
A.3 P-values from the F distribution
296(8)
A.4 Area under the standard normal curve
304(2)
A.5 Values of the gamma function
306(1)
A.6 Estimating the Weibull power parameter
307(2)
Bibliography 309(4)
Author index 313(2)
Subject index 315

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