Applied Statistics : Using SPSS, STATISTICA, and MATLAB,9783540011569

Applied Statistics : Using SPSS, STATISTICA, and MATLAB

by ; ;
ISBN13: 9783540011569
ISBN10: 3540011560
Format: Hardcover
Pub. Date: 2003-08-01
Publisher(s): Springer Verlag
List Price: $73.45

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Summary

Aimed at students, professionals and research workers who need to apply statistical analysis to a large variety of practical problems using SPSS, MATLAB and STATISTICA, this text provides a comprehensive coverage of the main statistical analysis topics important for practical applications such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. The relevant notions and methods are explained concisely, illustrated with practical examples using real data, presented with the distinct intention of clarifying sensible practical issues. The solutions presented in the examples are obtained with one of the software packages in a pedagogical way. It provides guidance on how to use SPSS, MATALB and STATISTICA in statistical analysis applications without having to delve in the manuals. The accompanying CD-Rom includes several specific software tools for the topics described in the book, including a set of MATLAB functions for directional statistics as well as the data sets used in the examples and exercises covering a broad spectrum of areas from engineering, medicine, biology, psychology, economy, geology, and astronomy.

Table of Contents

Preface vii
Symbols and Abbreviations xvii
1 Introduction 1(20)
1.1 Deterministic Data and Random Data
1(4)
1.2 Population, Sample and Statistics
5(3)
1.3 Random Variables
8(2)
1.4 Probabilities and Distributions
10(3)
1.4.1 Discrete Variables
10(2)
1.4.2 Continuous Variables
12(1)
1.5 Beyond a Reasonable Doubt
13(4)
1.6 STATISTICA, SPSS and MATLAB
17(4)
2 Presenting and Summarising the Data 21(46)
2.1 Preliminaries
21(7)
2.1.1 Reading in the Data
21(4)
2.1.2 Operating with the Data
25(3)
2.2 Presenting the Data
28(17)
2.2.1 Counts and Bar Graphs
28(7)
2.2.2 Frequencies and Histograms
35(4)
2.2.3 Multivariate Tables, Scatter Plots and 3D Plots
39(4)
2.2.4 Categorised Plots
43(2)
2.3 Summarising the Data
45(18)
2.3.1 Measures of Location
45(3)
2.3.2 Measures of Spread
48(2)
2.3.3 Measures of Shape
50(3)
2.3.4 Measures of Association for Continuous Variables
53(2)
2.3.5 Measures of Association for Ordinal Variables
55(4)
2.3.6 Measures of Association for Nominal Variables
59(4)
Exercises
63(4)
3 Estimating Data Parameters 67(18)
3.1 Point Estimation and Interval Estimation
67(4)
3.2 Estimating a Mean
71(6)
3.3 Estimating a Proportion
77(3)
3.4 Estimating a Variance
80(1)
3.5 Estimating a Variance Ratio
81(2)
Exercises
83(2)
4 Parametric Tests of Hypotheses 85(56)
4.1 Hypothesis Test Procedure
85(4)
4.2 Test Errors and Test Power
89(6)
4.3 Inference on One Population
95(5)
4.3.1 Testing a Mean
95(4)
4.3.2 Testing a Variance
99(1)
4.4 Inference on Two Populations
100(13)
4.4.1 Testing a Correlation
100(2)
4.4.2 Comparing Two Variances
102(3)
4.4.3 Comparing Two Means
105(8)
4.5 Inference on More Than Two Populations
113(24)
4.5.1 Introduction to the Analysis of Variance
113(1)
4.5.2 One-Way ANOVA
114(13)
4.5.3 Two-Way ANOVA
127(10)
Exercises
137(4)
5 Non-Parametric Tests of Hypotheses 141(50)
5.1 Inference on One Population
142(16)
5.1.1 The Runs Test
142(2)
5.1.2 The Binomial Test
144(4)
5.1.3 The Chi-Square Goodness of Fit Test
148(4)
5.1.4 The Kolmogorov-Smirnov Goodness of Fit Test
152(4)
5.1.5 The Lilliefors Test for Normality
156(1)
5.1.6 The Shapiro-Wilk Test for Normality
156(2)
5.2 Contingency Tables
158(11)
5.2.1 The 2x2 Contingency Table
158(4)
5.2.2 The rxc Contingency Table
162(2)
5.2.3 The Chi-Square Test of Independence
164(2)
5.2.4 Measures of Association Revisited
166(3)
5.3 Inference on Two Populations
169(11)
5.3.1 Tests for Two Independent Samples
169(5)
5.3.2 Tests for Two Paired Samples
174(6)
5.4 Inference on More Than Two Populations
180(6)
5.4.1 The Kruskat-Wallis Test for Independent Samples
180(3)
5.4.2 The Friedmann Test for Paired Samples
183(2)
5.4.3 The Cochran Q test
185(1)
Exercises
186(5)
6 Statistical Classification 191(46)
6.1 Decision Regions and Functions
191(2)
6.2 Linear Discriminants
193(9)
6.2.1 Minimum Euclidian Distance Discriminant
193(3)
6.2.2 Minimum Mahalanobis Distance Discriminant
196(6)
6.3 Bayesian Classification
202(12)
6.3.1 Bayes Rule for Minimum Risk
202(6)
6.3.2 Normal Bayesian Classification
208(3)
6.3.3 Dimensionality Ratio and Error Estimation
211(3)
6.4 The ROC Curve
214(6)
6.5 Feature Selection
220(4)
6.6 Classifier Evaluation
224(3)
6.7 Tree Classifiers
227(6)
Exercises
233(4)
7 Data Regression 237(46)
7.1 Simple Linear Regression
238(14)
7.1.1 Simple Linear Regression Model
238(1)
7.1.2 Estimating the Regression Function
238(5)
7.1.3 Inferences in Regression Analysis
243(5)
7.1.4 ANOVA Tests
248(4)
7.2 Multiple Regression
252(11)
7.2.1 General Linear Regression Model
252(1)
7.2.2 General Linear Regression in Matrix Terms
253(3)
7.2.3 Inferences on Regression Parameters
256(1)
7.2.4 ANOVA and Extra Sums of Squares
257(4)
7.2.5 Polynomial Regression and Other Models
261(2)
7.3 Building and Evaluating the Regression Model
263(10)
7.3.1 Building the Model
263(2)
7.3.2 Evaluating the Model
265(2)
7.3.3 Case Study
267(6)
7.4 Regression Through the Origin
273(1)
7.5 Ridge Regression
274(2)
7.6 Logit and Probit Models
276(5)
Exercises
281(2)
8 Data Structure Analysis 283(22)
8.1 Principal Components
283(6)
8.2 Dimensional Reduction
289(3)
8.3 Principal Components of Correlation Matrices
292(7)
8.4 Factor Analysis
299(3)
Exercises
302(3)
9 Survival Analysis 305(22)
9.1 Survivor Function and Hazard Function
305(1)
9.2 Non-Parametric Analysis of Survival Data
306(9)
9.2.1 The Life Table Analysis
306(5)
9.2.2 The Kaplan-Meier Analysis
311(2)
9.2.3 Statistics for Non-Parametric Analysis
313(2)
9.3 Comparing Two Groups of Survival Data
315(3)
9.4 Models for Survival Data
318(6)
9.4.1 The Exponential Model
318(2)
9.4.2 The Weibull Model
320(2)
9.4.3 The Cox Regression Model
322(2)
Exercises
324(3)
10 Directional Data 327(26)
10.1 Representing Directional Data
327(4)
10.2 Descriptive Statistics
331(3)
10.3 The von Mises Distributions
334(4)
10.4 Assessing the Distribution of Directional Data
338(8)
10.4.1 Graphical Assessment of Uniformity
338(2)
10.4.2 The Rayleigh Test of Uniformity
340(2)
10.4.3 The Watson Goodness of Fit Test
342(2)
10.4.4 Assessing the von Misesness of Spherical Distributions
344(2)
10.5 Tests on von Mises Distributions
346(1)
10.5.1 One-Sample Mean Test
346(1)
10.5.2 Mean Test for Two Independent Samples
346(1)
10.6 Non-Parametric Tests
347(3)
10.6.1 The Uniform Scores Test for Circular Data
347(1)
10.6.2 The Watson Test for Spherical Data
348(2)
10.6.3 Testing Two Paired Samples
350(1)
Exercises
350(3)
Appendix A - Short Survey on Probability Theory 353(28)
A.1 Basic Notions
353(3)
A.1.1 Events and Frequencies
353(1)
A.1.2 Probability Axioms
354(2)
A.2 Conditional Probability and Independence
356(2)
A.2.1 Conditional Probability and Intersection Rule
356(1)
A.2.2 Independent Events
356(2)
A.3 Compound Experiments
358(1)
A.4 Bayes' Theorem
359(1)
A.5 Random Variables and Distributions
360(4)
A.5.1 Definition of Random Variable
360(1)
A.5.2 Distribution and Density Functions
361(2)
A.5.3 Transformation of a Random Variable
363(1)
A.6 Expectation, Variance and Moments
364(4)
A.6.1 Definitions and Properties
364(3)
A.6.2 Moment-Generating Function
367(1)
A.6.3 Chebyshev Theorem
368(1)
A.7 The Binomial and Normal Distributions
368(4)
A.7.1 The Binomial Distribution
368(1)
A.7.2 The Laws of Large Numbers
369(1)
A.7.3 The Normal Distribution
370(2)
A.8 Multivariate Distributions
372(9)
A.8.1 Definitions
372(2)
A.8.2 Moments
374(1)
A.8.3 Conditional Densities and Independence
375(2)
A.8.4 Sums of Random Variables
377(1)
A.8.5 Central Limit Theorem
378(3)
Appendix B - Distributions 381(24)
B.1 Discrete Distributions
381(8)
B.1.1 Bernoulli Distribution
381(1)
B.1.2 Uniform Distribution
382(1)
B.1.3 Geometric Distribution
383(1)
B.1.4 Hypergeometris Distribution
384(1)
B.1.5 Binomial Distribution
385(1)
B.1.6 Multinomial Distribution
386(2)
B.1.7 Poisson Distribution
388(1)
B.2 Continuous Distributions
389(16)
B.2.1 Uniform Distribution
389(2)
B.2.2 Normal Distribution
391(1)
B.2.3 Exponential Distribution
392(2)
B.2.4 Weibull Distribution
394(1)
B.2.5 Gamma Distribution
395(1)
B.2.6 Beta Distribution
396(2)
B.2.7 Chi-Square Distribution
398(1)
B.2.8 Student's t Distribution
399(2)
B.2.9 F Distribution
401(1)
B.2.10 Von Mises Distributions
402(3)
Appendix C - Point Estimation 405(4)
C.1 Definitions
405(1)
C.2 Estimation of Mean and Variance
406(3)
Appendix D - Tables 409(10)
D.1 Binomial Distribution
409(6)
D.2 Normal Distribution
415(1)
D.3 Student's t Distribution
416(1)
D.4 Chi-Square Distribution
417(1)
D.5 Critical Values for the F Distribution
418(1)
Appendix E - Datasets 419(18)
E.1 Breast Tissue
419(1)
E.2 arSale
419(1)
E.3 Cells
420(1)
E.4 Clays
420(1)
E.5 Cork Stoppers
421(1)
E.6 CTG
422(1)
E.7 Culture
423(1)
E.8 Fatigue
423(1)
E.9 FHR
424(1)
E.10 FHR-Apgar
424(1)
E.11 Firms
425(1)
E.12 Flow Rate
425(1)
E.13 Foetal Weight
425(1)
E.14 Forest Fires
426(1)
E.15 Freshmen
426(1)
E.16 Heart Valve
427(1)
E.17 Infarct
428(1)
E.18 Joints
428(1)
E.19 Metal Firms
429(1)
E.20 Meteo
429(1)
E.21 Moulds
429(1)
E.22 Neonatal
430(1)
E.23 Programming
430(1)
E.24 Rocks
431(1)
E.25 Signal & Noise
431(1)
E.26 Soil Pollution
432(1)
E.27 Stars
432(1)
E.28 Stock Exchange
433(1)
E.29 VCG
434(1)
E.30 Wave
434(1)
E.31 Weather
434(1)
E.32 Wines
435(2)
Appendix F - Tools 437(2)
F.1 MATLAB Functions
437(1)
F.2 Tools EXCEL File
438(1)
F.3 SCSIZE Program
438(1)
References 439

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