A course in probability and statistics
著者
書誌事項
A course in probability and statistics
Duxbury Press, c1996
大学図書館所蔵 全11件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes index
内容説明・目次
内容説明
This author's modern approach is intended primarily for honors undergraduates or undergraduates with a good math background taking a mathematical statistics or statistical inference course. The author takes a finite-dimensional functional modeling viewpoint (in contrast to the conventional parametric approach) to strengthen the connection between statistical theory and statistical methodology.
目次
1. RANDOM VARIABLES AND THEIR DISTRIBUTION
Introduction / Sample Distributions / Distributions / Random Variables / Probability Functions and Density Functions / Distribution Functions and Quantiles / Univariate Transformations / Independence
2. EXPECTATION
Introduction / Properties of Expectation / Variance / Weak Law of Large Numbers Simulation and the Monte Carlo Method
3. SPECIAL CONTINUOUS MODELS
Gamma and Beta Distributions / The Normal Distribution / Normal Approximation and the Central Limit Theorem
4. SPECIAL DISCRETE MODELS
Combinatorics / The Binomial Distribution / The Multinomial Distribution / The Poisson Distribution / The Poisson Process
5. DEPENDENCE
Covariance, Linear Prediction, and Correlation / Multivariate Expectation / Covariance and Variance - Covariance Matrices / Multiple Linear Prediction / Multivariate Density Function / Invertible Transformations / The Multivariate Normal Distribution
6. CONDITIONAL DISTRIBUTIONS
Sampling Without Replacement / Hypergeometric Distribution / Conditional Density Functions / Conditional Expectation / Prediction / Conditioning and the Multivariate Normal Distribution / Random Parameters
7. NORMAL MODELS
Introduction / Chi-Square, t, and F Distribution / Confidence Intervals / The t Test of an Inequality / The t Test of an Equality
8. THE F TEST
Introduction to Linear Regression / The Method of Least Squares / Factorial Experiments / Input-Response and Experimental Models
9. LINEAR ANALYSIS
Linear Spaces / Identifiability / Saturated Spaces / Inner Products / Orthogonal Projections / Normal Equations
10. LINEAR REGRESSION
Least-Square Estimation / Sums of Squares / Distribution Theory / sugar Beet Experiment / Lube Oil Experiment / The t Test / Submodels / The F Test
11. ORTHOGONAL ARRAYS
Main Effects / Interactions / Experiments with Factors Having Three Levels / Randomization, Blocking, and Covariates
12. BINOMIAL AND POISSON MODELS
Nominal Confidence Intervals and Tests / Exact P-values / One-Parameter Exponential Families
13. LOGISTIC REGRESSION AND POISSON REGRESSION
Input-Response and Experimental Models / Maximum-Likelihood Estimation / Existence and Uniqueness of the Maximum-Likelihood Estimate / Interactively Reweighted Least-Squares Method / Normal Approximation / The Likelihood-Ratio Test / APPENDICES: A. PROPERTIES OF VECTORS AND MATRICES / B. SUMMARY OF PROBABILITY / C. SUMMARY OF STATISTICS / D. HINTS AND ANSWERS / E. TABLES / INDEX
「Nielsen BookData」 より