图书简介

Chapter 1. Introduction. Chapter 2. Fundamentals of Statistics. 2.1 Statistical Thinking. 2.2 Data Format. 2.3 Descriptive Statistics. 2.4 Data Visualization. 2.5 Probability and Probability Distributions. 2.6 Rules of Two and Three Sigma. 2.7 Sampling Distributions and the Laws of Large Numbers. 2.8 Skewness and Kurtosis. Chapter 3. Statistical Inference. 3.1 Introduction. 3.2 Point Estimation of Parameters. 3.3 Interval Estimation. 3.4 Hypothesis Testing. 3.5 Samples from Two Populations. 3.6 Probability Plots and Testing for Population Distributions. 3.7 Outlier Detection. 3.8 Monte-Carlo Simulations. 3.9 Bootstrap. Chapter 4. Statistical Models. 4.1 Introduction. 4.2 Regression Models. 4.3 Experimental Design and Analysis. Chapter 5. Fundamentals of Multivariate Statistics. 5.1 Introduction. 5.2 The Multivariate Random Sample. 5.3 Multivariate Data Visualization. 5.4 The Geometry of the Sample. 5.5 The Generalized Variance. 5.6 Distances in the P-Dimensional Space. 5.7 The Multivariate Normal (Gaussian) Distribution. Chapter 6. Multivariate Statistical Inference. 6.1 Introduction. 6.2 Inferences about a Mean Vector. 6.3 Comparing Mean Vectors from Two Populations. 6.4 Inferences about a Covariance Matrix. 6.5 How to Check Multivariate Normality. Chapter 7. Principal Components Analysis. 7.1 Introduction. 7.2 Definition and Properties of Principal Components. 7.3 Stopping Rules for Principal Components Analysis. 7.4 Principal Component Scores. 7.5 Residual Analysis. 7.6 Statistical Inference in Principal Components Analysis. 7.7 Further Reading. Chapter 8. Canonical Correlation Analysis. 8.1 Introduction. 8.2 Mathematical Formulation. 8.3 Practical Application. 8.4 Calculating Variability Explained by Canonical Variables. 8.5 Canonical Correlation Regression. 8.6 Further Reading. Chapter 9. Discrimination and Classification Supervised Learning. 9.1 Introduction. 9.2 Classification for Two Populations. 9.3 Classification for Several Populations. 9.4 Spatial Smoothing for Classification. 9.5 Further Reading. Chapter 10. Clustering Unsupervised Learning. 10.1 Introduction. 10.2 Similarity and Dissimilarity Measures. 10.3 Hierarchical Clustering Methods. 10.4 Non-Hierarchical Clustering Methods. 10.5 Clustering Variables. 10.6 Further Reading. Appendix A-1. Appendix A-2 Appendix A-3. 

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