Assuming no previous statistics education, this practical reference provides a comprehensive introduction and tutorial on the main statistical analysis topics, demonstrating their solution with the most commonly used software packages. Intended for anyone needing to apply statistical analysis to a large variety of science and enigineering problems, the book explains and shows how to use SPSS, MATLAB and STATISTICA for analyses such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. It concisely explains key concepts and methods, illustrating them by practical examples using real data, and includes a CD-ROM with software tools and data sets used in the examples and exercises. Readers learn which software tools to apply and also gain insights into the comparative capabilities of the primary software packages.
Inhalt
1 Introduction.- 2 Presenting and Summarising the Data.- 3 Estimating Data Parameters.- 4 Parametric Tests of Hypotheses.- 5 Non-Parametric Tests of Hypotheses.- 6 Statistical Classification.- 7 Data Regression.- 8 Data Structure Analysis.- 9 Survival Analysis.- 10 Directional Data.- Appendix A - Short Survey on Probability Theory.- A.1 Basic Notions.- A.1.1 Events and Frequencies.- A.1.2 Probability Axioms.- A.2 Conditional Probability and Independence.- A.2.1 Conditional Probability and Intersection Rule.- A.2.2 Independent Events.- A.3 Compound Experiments.- A.4 Bayes' Theorem.- A.5 Random Variables and Distributions.- A.5.1 Definition of Random Variable.- A.5.2 Distribution and Density Functions.- A.5.3 Transformation of a Random Variable.- A.6 Expectation, Variance and Moments.- A.6.1 Definitions and Properties.- A.6.2 Moment-Generating Function.- A.6.3 Chebyshev Theorem.- A.7 The Binomial and Normal Distributions.- A.7.1 The Binomial Distribution.- A.7.2 The Laws of Large Numbers.- A.7.3 The Normal Distribution.- A.8 Multivariate Distributions.- A.8.1 Definitions.- A.8.2 Moments.- A.8.3 Conditional Densities and Independence.- A.8.4 Sums of Random Variables.- A.8.5 Central Limit Theorem.- Appendix B - Distributions.- B.1 Discrete Distributions.- B.1.1 Bernoulli Distribution.- B.1.2 Uniform Distribution.- B.1.3 Geometric Distribution.- B.1.4 Hypergeometric Distribution.- B.1.5 Binomial Distribution.- B.1.6 Multinomial Distribution.- B.1.7 Poisson Distribution.- B.2 Continuous Distributions.- B.2.1 Uniform Distribution.- B.2.2 Normal Distribution.- B.2.3 Exponential Distribution.- B.2.4 Weibull Distribution.- 6.2.5 Gamma Distribution.- B.2.6 Beta Distribution.- B.2.7 Chi-Square Distribution.- B.2.8 Student's t Distribution.- B.2.9 F Distribution.- B.2.10 Von Mises Distributions.- Appendix C - Point Estimation.- C.1 Definitions.- C.2 Estimation of Mean and Variance.- Appendix D - Tables.- D.1 Binomial Distribution.- D.2 Normal Distribution.- D.4 Chi-Square Distribution.- Appendix E - Datasets.- E.1 Breast Tissue.- E.2 Car Sale.- E.3 Cells.- E.4 Clays.- E.5 Cork Stoppers.- E.6 CTG.- E.7 Culture.- E.8 Fatigue.- E.9 FHR.- E.10 FHR-Apgar.- E.11 Firms.- E.12 Flow Rate.- E.13 Foetal Weight.- E.14 Forest Fires.- E.15 Freshmen.- E.16 Heart Valve.- E.17 Infarct.- E.18 Joints.- E.19 Metal Firms.- E.20 Meteo.- E.21 Moulds.- E.22 Neonatal.- E.23 Programming.- E.24 Rocks.- E.25 Signal & Noise.- E.26 Soil Pollution.- E.27 Stars.- E.28 Stock Exchange.- E.29 VCG.- E.30 Wave.- E.31 Weather.- E.32 Wines.- Appendix F - Tools.- F.1 MATLAB Functions.- F.2 Tools EXCEL File.- F.3 SCSIZE Program.- References.