<p>Practical Methods for Design and Analysis</p><p>和</p><p>Standard Probability and Statistic Tables and Formulae</p><p>两本书.</p><p></p><p></p><p> </p><p> </p><p><br/></p><p><br/>后面这本书的目录如下：</p><p>Contents<br/>1 Introduction<br/>1.1 Background<br/>1.2 Data sets<br/>1.3 References<br/>2 Summarizing Data<br/>2.1 Tabular and graphical procedures<br/>2.2 Numerical summary measures<br/>3 Probability<br/>3.1 Algebra of sets<br/>3.2 Combinatorial methods<br/>3.3 Probability<br/>3.4 Random variables<br/>3.5 Mathematical expectation<br/>3.6 Multivariate distributions<br/>3.7 Inequalities<br/>4 Functions of Random Variables<br/>4.1 Finding the probability distribution<br/>4.2 Sums of random variables<br/>4.3 Sampling distributions<br/>4.4 Finite population<br/>4.5 Theorems<br/>4.6 Order statistics<br/>4.7 Range and studentized range<br/>5 Discrete Probability Distributions<br/>5.1 Bernoulli distribution<br/>5.2 Beta binomial distribution<br/>5.3 Beta Pascal distribution<br/>5.4 Binomial distribution<br/>5.5 Geometric distribution<br/>5.6 Hypergeometric distribution<br/>5.7 Multinomial distribution<br/>5.8 Negative binomial distribution<br/>5.9 Poisson distribution<br/>5.10 Rectangular (discrete uniform) distribution<br/>6 Continuous Probability Distributions<br/>6.1 Arcsin distribution<br/>6.2 Beta distribution<br/>6.3 Cauchy distribution<br/>6.4 Chi–square distribution<br/>6.5 Erlang distribution<br/>6.6 Exponential distribution<br/>6.7 Extreme–value distribution<br/>6.8 F distribution<br/>6.9 Gamma distribution<br/>6.10 Half–normal distribution<br/>6.11 Inverse Gaussian (Wald) distribution<br/>6.12 Laplace distribution<br/>6.13 Logistic distribution<br/>6.14 Lognormal distribution<br/>6.15 Noncentral chi–square distribution<br/>6.16 Noncentral F distribution<br/>6.17 Noncentral t distribution<br/>6.18 Normal distribution<br/>6.19 Normal distribution: multivariate<br/>6.20 Pareto distribution<br/>6.21 Power function distribution<br/>6.22 Rayleigh distribution<br/>6.23 t distribution</p><p>6.24 Triangular distribution<br/>6.25 Uniformdistributio n<br/>6.26 Weibull distribution<br/>6.27 Relationships among distributions<br/>7 Standard Normal Distribution<br/>7.1 Density function and related functions<br/>7.2 Critical values<br/>7.3 Tolerance factors for normal distributions<br/>7.4 Operating characteristic curves<br/>7.5 Multivariate normal distribution<br/>7.6 Distribution of the correlation coefficient<br/>7.7 Circular normal probabilities<br/>7.8 Circular error probabilities<br/>8 Estimation<br/>8.1 Definitions<br/>8.2 Cram&acute;er–Rao inequality<br/>8.3 Theorems<br/>8.4 The method of moments<br/>8.5 The likelihood function<br/>8.6 The method of maximum likelihood<br/>8.7 Invariance property of MLEs<br/>8.8 Different estimators<br/>8.9 Estimators for small samples<br/>8.10 Estimators for large samples<br/>9 Confidence Intervals<br/>9.1 Definitions<br/>9.2 Common critical values<br/>9.3 Sample size calculations<br/>9.4 Summary of common confidence intervals<br/>9.5 Confidence intervals: one sample<br/>9.6 Confidence intervals: two samples<br/>9.7 Finite population correction factor<br/>10 Hypothesis Testing</p><p>10.1 Introduction<br/>10.2 The Neyman–Pearson lemma<br/>10.3 Likelihood ratio tests<br/>10.4 Goodness of fit test<br/>10.5 Contingency tables<br/>10.6 Bartlett’s test<br/>10.7 Cochran’s test<br/>10.8 Number of observations required<br/>10.9 Critical values for testing outliers<br/>10.10 Significance test in 2×2 contingency tables<br/>10.11 Determining values in Bernoulli trials<br/>11 Regression Analysis<br/>11.1 Simple linear regression<br/>11.2 Multiple linear regression<br/>11.3 Orthogonal polynomials<br/>12 Analysis of Variance<br/>12.1 One-way anova<br/>12.2 Two-way anova<br/>12.3 Three-factor experiments<br/>12.4 Manova<br/>12.5 Factor analysis<br/>12.6 Latin square design<br/>13 Experimental Design<br/>13.1 Latin squares<br/>13.2 Graeco–Latin squares<br/>13.3 Block designs<br/>13.4 Factorial experimentation: 2 factors<br/>13.5 2r Factorial experiments<br/>13.6 Confounding in 2n factorial experiments<br/>13.7 Tables for design of experiments<br/>13.8 References<br/>14 Nonparametric Statistics<br/>14.1 Friedman test for randomized block design</p><p>14.2 Kendall’s rank correlation coefficient<br/>14.3 Kolmogorov–Smirnoff tests<br/>14.4 Kruskal–Wallis test<br/>14.5 The runs test<br/>14.6 The sign test<br/>14.7 Spearman’s rank correlation coefficient<br/>14.8 Wilcoxon matched-pairs signed-ranks test<br/>14.9 Wilcoxon rank–sum(Mann–Whitney ) test<br/>14.10 Wilcoxon signed-rank test<br/>15 Quality Control and Risk Analysis<br/>15.1 Quality assurance<br/>15.2 Acceptance sampling<br/>15.3 Reliability<br/>15.4 Risk analysis and decision rules<br/>16 General Linear Models<br/>16.1 Notation<br/>16.2 The general linear model<br/>16.3 Summary of rules for matrix operations<br/>16.4 Quadratic forms<br/>16.5 General linear hypothesis of full rank<br/>16.6 General linear model of less than full rank<br/>17 Miscellaneous Topics<br/>17.1 Geometric probability<br/>17.2 Information and communication theory<br/>17.3 Kalman filtering<br/>17.4 Large deviations (theory of rare events)<br/>17.5 Markov chains<br/>17.6 Martingales<br/>17.7 Measure theoretical probability<br/>17.8 Monte Carlo integration techniques<br/>17.9 Queuing theory<br/>17.10 Random matrix eigenvalues<br/>17.11 Random number generation<br/>17.12 Resampling methods</p><p>17.13 Self-similar processes<br/>17.14 Signal processing<br/>17.15 Stochastic calculus<br/>17.16 Classic and interesting problems<br/>17.17 Electronic resources<br/>17.18 Tables<br/>18 Special Functions<br/>18.1 Bessel functions<br/>18.2 Beta function<br/>18.3 Ceiling and floor functions<br/>18.4 Delta function<br/>18.5 Error functions<br/>18.6 Exponential function<br/>18.7 Factorials and Pochhammer’s symbol<br/>18.8 Gamma function<br/>18.9 Hypergeometric functions<br/>18.10 Logarithmic functions<br/>18.11 Partitions<br/>18.12 Signumfunctio n<br/>18.13 Stirling numbers<br/>18.14 Sums of powers of integers<br/>18.15 Tables of orthogonal polynomials<br/>18.16 References</p>

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