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1.

図書

図書
J.K. Wani
出版情報: New York : Appleton-Century-Crofts, 1971  xiv, 315 p. ; 24 cm
シリーズ名: The Appleton-Century statistics series
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2.

図書

図書
delivered by J. Neyman, at the Graduate School of the United States Department of Agriculture, in April 1937 ; rev. and supplemented by the author with the editorial assistance of W. Edwards Deming
出版情報: [S.l.] : [s.n.], [1938]  160 p. ; 22 cm
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3.

図書

図書
Jay L. Devore, Nicholas R. Farnum
出版情報: Pacific Grove, Calif. : Duxbury Press, c1999  xiv, 577 p. ; 24 cm.
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4.

図書

図書
Michael E. Tarter
出版情報: Natick, Mass. : A K Peters, c2000  xiii, 386 p. ; 24 cm
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目次情報: 続きを見る
Preface
Introduction / 1:
Background / 1.1:
A fictional example / 1.2:
Curves and statistical history / 1.3:
Model and Distribution Terminology / 2:
Modeling background / 2.1:
Representative number / 2.2:
Curve types / 2.3:
Distribution and data terminology / 2.4:
Parameter validity and property existence / 2.5:
Estimator terminology / 2.6:
Degenerate curves / 2.7:
Variability and Related Curve Properties / 3:
Uncertainty and variability / 3.1:
The absolute deviation curve property / 3.2:
The general AD and the ADM curve properties / 3.3:
Curve property selection / 3.4:
The history of variability appreciation / 3.5:
Simplistic approaches and the history of probability / 3.6:
Moments and Curve Uncertainty / 4:
E and Var Geometry / 4.1:
Higher order moments and the indicator function / 4.2:
Early statistical models / 4.3:
Early statistical models and higher order moments / 4.4:
Curve sub-types and model choice / 4.5:
Goodness of fit / 5:
Neyman's and alternative criteria / 5.1:
Criteria, metrics and estimators / 5.2:
The Kolmogoroff-Smirnoff criteria / 5.3:
Bernoulli variation and the Cauchy density / 5.4:
Comparative goodness of fit / 5.5:
Variates, Variables and Regression / 6:
Variates and variables / 6.1:
Variates and subjects / 6.2:
Expressions, algorithms and life tables / 6.3:
Distinctions between curve types / 6.4:
Curve properties and symbols / 6.5:
Variates, variables, and E[subscript f](Y|x) regression / 6.6:
[mu](x), E[subscript f] (Y|x) and regression alternatives / 6.7:
Mixing Parameters and Data-generation models / 7:
An introduction to data-generation models / 7.1:
Error, regression, and probit, models / 7.2:
Regression and data-generation models / 7.3:
Probability, proportion, and data-generation models / 7.4:
The generation of contagious model and mixture model data / 7.5:
The Association Parameter [rho] / 8:
Response, key, and nuisance, variates / 8.1:
The association parameter [rho] / 8.2:
Conditional, joint and marginal, notation / 8.3:
The sample and the population correlation coefficient / 8.4:
Correlation geometry / 8.5:
Regression and Association Parameters / 9:
The curse of dimensionality / 9.1:
Multiple variable interdependence / 9.2:
Logit and linear models / 9.3:
Dual regression functions / 9.4:
Parameters, Confounding, and Least Squares / 10:
Ideal objects / 10.1:
Linear data-generation models and mixture models / 10.2:
Parameter distinctiveness / 10.3:
Representational uniqueness and model fitting / 10.4:
Model-fitting considerations / 10.5:
The variance curve property and bathtub functions / 10.6:
Regression and least squares / 10.7:
Nonparametric Adjustment / 11:
Age-adjustment and logistic regression / 11.1:
Crude and specific rates / 11.2:
Age-adjustment; marginal, joint, and conditional curves / 11.3:
Age-adjustment and partial correlation / 11.4:
Direct and indirect adjustment / 11.5:
The computation of adjusted rates / 11.6:
Continuous Variate Adjustment / 12:
Observed and expected rates / 12.1:
Trivariate data-generation and additive regression models / 12.2:
Regression and data generation / 12.3:
Correlation, regression, and nuisance variables / 12.4:
Trivariate Normality graphics / 12.5:
Procedural Road Maps / 13:
The organization of statistical data and statistical methods / 13.1:
Log and log(-log) transformations / 13.2:
Methodological alternatives / 13.3:
Conditional and joint density models / 13.4:
Model-based and Generalized Representation / 14:
Multiple properties and parameters / 14.1:
Specification and generalized representation / 14.2:
Identifiability of generalized versus extended model representation / 14.3:
The E(X) curve property's relationship to location and scale / 14.4:
Parameters, Transformations, and Quantiles / 15:
Location and scale parameter representation of continuous variates / 15.1:
[rho]-focused transformations and [sigma]-focused transformations / 15.2:
Quantiles, quartiles, and box-and-whisker plots / 15.3:
Normal ranges and box sizes / 15.4:
Confidence bands and prediction bands / 15.5:
Notches, stems, and leaves / 15.6:
The log transformation and skewness / 15.7:
Noncentrality Parameters and Degress of Freedom / 16:
The (C[subscript 1]|A[subscript 2]) case and variate-variable relationships / 16.1:
Invariance and confounding / 16.2:
ANOVA tables and confounding / 16.3:
Contingency tables and the parameter v / 16.4:
Student-t and Cauchy densities / 16.5:
Parameter-Based Estimation / 17:
Likelihood and BLU estimation / 17.1:
Censoring and incompleteness / 17.2:
Outliers and errors / 17.3:
Ordered variates and subscripts / 17.4:
BLU estimators / 17.5:
BLU estimation and censoring / 17.6:
BLU estimators and alternatives / 17.7:
Inference and Composite Variates / 18:
Curves and composite variates / 18.1:
Specific sampling distributions / 18.2:
The mean's variance formula and mixtures / 18.3:
Inference and a two-valued metric / 18.4:
The one tail z-test / 18.5:
Parameters and Test Statistics / 19:
The parameter [Delta] / 19.1:
Power and efficiency / 19.2:
Power and test considerations / 19.3:
The sample mean and the sample median / 19.4:
Tables and the details of test construction / 19.5:
Power, efficiency and BLU estimators / 19.6:
Curve Truncation and the Curve e(x) / 20:
Expectation as a limit and the effects of truncation / 20.1:
Truncation symmetry / 20.2:
Truncation and bias / 20.3:
Truncation and the curve e(x) / 20.4:
When are curve properties relevant and when are model parameters relevant / 20.5:
Models and Notation / I:
Notation historical background / I.1:
Specific models, the Normal / I.2:
Specific models, lognormals and related curves / I.3:
Model families / I.4:
Mixtures and Bayesian statistics / I.5:
Notational conventions about moments and variates / I.6:
Variate Independence and Curve Identity / II:
Independence and identical distribution / II.1:
Regression notation / II.2:
General Statistical and Mathematical Notation / III:
References
Index
Preface
Introduction / 1:
Background / 1.1:
5.

図書

図書
[by] H. G. Cuming [and] C. J. Anson
出版情報: London : Heywood Books, 1966  490 p. ; 26 cm
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6.

図書

図書
William L. Carlson, Betty Thorne
出版情報: Upper Saddle River, N.J. : Prentice Hall, c1997  xxiii, 1021 p. ; 24 cm
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目次情報: 続きを見る
Introduction / 1:
Describing the Data / 2:
Descriptive Relationships / 3:
Introduction to Probability / 4:
Discrete Random Variables and Probability Distribution Functions / 5:
Continuous Random Variables and Probability Density Functions / 6:
Two Random Variables / 7:
Sampling and Data Collection / 8:
Distribution of Sample Statistics / 9:
Estimation / 10:
Hypothesis Testing / 11:
Chi Square Tests / 12:
Analysis of Variance / 13:
Simple Least-Squares Regression / 14:
Multiple Regression / 15:
Multiple Regression Extensions / 16:
Time Series and Forecasting / 17:
Quality Assurance / 18:
Probability Tables / Appendix A:
Data Files for Problems and Examples / Appendix B:
Index
Introduction / 1:
Describing the Data / 2:
Descriptive Relationships / 3:
7.

図書

図書
Achintya Haldar, Sankaran Mahadevan
出版情報: New York ; Chichester : Wiley, c2000  xvi, 304 p. ; 25 cm
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Basic Concept of Reliability
Mathematics of Probability
Modeling of Uncertainty
Commonly Used Probability Distributions
Determination of Distributions and Parameters from Observed Data
Randomness in Response Variables
Fundamentals of Reliability Analysis
Advanced Topics on Reliability Analysis
Simulation Techniques
Appendices
Conversion Factors
References
Index
Basic Concept of Reliability
Mathematics of Probability
Modeling of Uncertainty
8.

図書

図書
[by] G. Barrie Wetherill
出版情報: London : Methuen, 1967  329 p. ; 24 cm
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9.

図書

図書
Alan Grafen, Rosie Hails
出版情報: Oxford : Oxford University Press, 2002  xv, 351 p. ; 25 cm
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Why use this book
How to use this book
How to teach this text
An introduction to analysis of variance / 1:
Model formulae and geometrical pictures / 1.1:
General Linear Models / 1.2:
The basic principles of ANOVA / 1.3:
An example of ANOVA / 1.4:
The geometrical approach for an ANOVA / 1.5:
Regression / 2:
What kind of data are suitable for regression? / 2.1:
How is the best fit line chosen? / 2.2:
The geometrical view of regression / 2.3:
Regression--an example / 2.4:
Confidence and prediction intervals / 2.5:
Conclusions from a regression analysis / 2.6:
Unusual observations / 2.7:
The role of X and Y--does it matter which is which? / 2.8:
Models, parameters and GLMs / 3:
Populations and parameters / 3.1:
Expressing all models as linear equations / 3.2:
Turning the tables and creating datasets / 3.3:
Using more than one explanatory variable / 4:
Why use more than one explanatory variable? / 4.1:
Elimination by considering residuals / 4.2:
Two types of sum of squares / 4.3:
Urban Foxes--an example of statistical elimination / 4.4:
Statistical elimination by geometrical analogy / 4.5:
Designing experiments--keeping it simple / 5:
Three fundamental principles of experimental design / 5.1:
The geometrical analogy for blocking / 5.2:
The concept of orthogonality / 5.3:
Combining continuous and categorical variables / 6:
Reprise of models fitted so far / 6.1:
Orthogonality in the context of continuous and categorical variables / 6.2:
Treating variables as continuous or categorical / 6.4:
The general nature of General Linear Models / 6.5:
Interactions--getting more complex / 7:
The factorial principle / 7.1:
Analysis of factorial experiments / 7.2:
What do we mean by an interaction? / 7.3:
Presenting the results / 7.4:
Extending the concept of interactions to continuous variables / 7.5:
Uses of interactions / 7.6:
Checking the models I: independence / 8:
Heterogeneous data / 8.1:
Repeated measures / 8.2:
Nested data / 8.3:
Detecting non-independence / 8.4:
Checking the models II: the other three asumptions / 9:
Homogeneity of variance / 9.1:
Normality of error / 9.2:
Linearity/additivity / 9.3:
Model criticism and solutions / 9.4:
Predicting the volume of merchantable wood: an example of model criticism / 9.5:
Selecting a transformation / 9.6:
Model selection I: principles of model choice and designed experiments / 10:
The problem of model choice / 10.1:
Three principles of model choice / 10.2:
Four different types of model choice problem / 10.3:
Orthogonal and near orthogonal designed experiments / 10.4:
Looking for trends across levels of a categorical variable / 10.5:
Model selection II: datasets with several explanatory variables / 11:
Economy of variables in the context of multiple regression / 11.1:
Multiplicity of p-values in the context of multiple regression / 11.2:
Automated model selection procedures / 11.3:
Whale Watching: using the GLM approach / 11.4:
Random effects / 12:
What are random effects? / 12.1:
Four new concepts to deal with random effects / 12.2:
A one-way ANOVA with a random factor / 12.3:
A two-level nested ANOVA / 12.4:
Mixing random and fixed effects / 12.5:
Using mock analyses to plan an experiment / 12.6:
Categorical data / 13:
Categorical data: the basics / 13.1:
The Poisson distribution / 13.2:
The chi-squared test in contingency tables / 13.3:
General linear models and categorical data / 13.4:
What lies beyond? / 14:
Generalised Linear Models / 14.1:
Multiple y variables, repeated measures and within-subject factors / 14.2:
Conclusion / 14.3:
Answers to exercises / 15:
Revision section: The basics / Chapter 1:
Populations and samples / R1.1:
Three types of variability: of the sample, the population and the estimate / R1.2:
Confidence intervals: a way of precisely representing uncertainty / R1.3:
The null hypothesis--taking the conservative approach / R1.4:
Comparing two means / R1.5:
The meaning of p-values and confidence intervals / R1.6:
What is a p-value?
What is a confidence interval?
Analytical results about variances of sample means / Appendix 2:
Introducing the basic notation
Using the notation to define the variance of a sample
Using the notation to define the mean of a sample
Defining the variance of the sample mean
To illustrate why the sample variance must be calculated with n - 1 in its denominator (rather than n) to be an unbiased estimate of the population variance
Probability distributions / Appendix 3:
Some gentle theory
Confirming simulations
Bibliography
Index
Why use this book
How to use this book
How to teach this text
10.

図書

図書
N.I. Fisher, T. Lewis, B.J.J. Embleton
出版情報: Cambridge [Cambridgeshire] ; New York : Cambridge University Press, 1987  xiv, 329 p. ; 24 cm
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目次情報: 続きを見る
Preface
Introduction / 1:
Terminology and spherical coordinate systems / 2:
Descriptive and ancillary methods, and sampling problems / 3:
Models / 4:
Analysis of a single sample of unit vectors / 5:
Analysis of a single sample of undirected lines / 6:
Analysis of two or more samples of vectorial or axial data / 7:
Correlation, regression and temporal/spatial analysis / 8:
Appendces
References
Index
Preface
Introduction / 1:
Terminology and spherical coordinate systems / 2:
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