Titre :	Introduction to probability and statistics : Principles and applications for engineering and the computing sciences
Type de document :	texte imprimé
Auteurs :	J. Susan Milton ; Jesse C. Arnold
Mention d'édition :	2e éd.
Editeur :	New York : McGraw-Hill
Année de publication :	1990
Collection :	McGraw-Hill series in probability and statistics
Importance :	1 vol (700 p.)
Présentation :	ill.
Format :	25 cm
ISBN/ISSN/EAN :	978-0-07-042353-4
Note générale :	Includes index.
Catégories :	Mathématique
Mots-clés :	Probabilité Statistiques
Note de contenu :	Sommaire 1 Introduction to Probability and Counting 1.1 Interpreting Probabilities 1.2 Sample Spaces and Events 1.3 Permutations and Combinations 2 Some Probability Laws 2.1 Axioms of Probability 2.2 Conditional Probability 2.3 Independence and the Multiplication Rule 2.4 Bayes' Theorem 3 Discrete Distributions 3.1 Random Variables 3.2 Discrete Probablility Densities 3.3 Expectation and Distribution Parameters 3.4 Geometric Distribution and the Moment Generating Function 3.5 Binomial Distribution 3.6 Negative Binomial Distribution 3.7 Hypergeometric Distribution 3.8 Poisson Distribution 4 Continuous Distributions 4.1 Continuous Densities 4.2 Expectation and Distribution Parameters 4.3 Gamma Distribution 4.4 Normal Distribution 4.5 Normal Probability Rule and Chebyshev's Inequality 4.6 Normal Approximation to the Binomial Distribution 4.7 Weibull Distribution and Reliability 4.8 Transformation of Variables 4.9 Simulating a Continuous Distribution 5 Joint Distributions 5.1 Joint Densities and Independence 5.2 Expectation and Covariance 5.3 Correlation 5.4 Conditional Densities and Regression 5.5 Transformation of Variables 6 Descriptive Statistics 6.1 Random Sampling 6.2 Picturing the Distribution 6.3 Sample Statistics 7 Estimation 7.1 Point Estimation 7.2 The Method of Moments and Maximum Likelihood 7.3 Functions of Random Variables--Distribution of X 7.4 Interval Estimation and the Central Limit Theorem 8 Inferences on the Mean and Variance of a Distribution 8.1 Interval Estimation of Variability 8.2 Estimating the Mean and the Student-t Distribution 8.3 Hypothesis Testing 8.4 Significance Testing 8.5 Hypothesis and Significance Tests on the Mean 8.6 Hypothesis Tests 8.7 Alternative Nonparametric Methods 9 Inferences on Proportions 9.1 Estimating Proportions 9.2 Testing Hypothesis on a Proportion 9.3 Comparing Two Proportions: Estimation 9.4 Coparing Two Proportions: Hypothesis Testing 10 Comparing Two Means and Two Variances 10.1 Point Estimation 10.2 Comparing Variances: The F Distribution 10.3 Comparing Means: Variances Equal (Pooled Test) 10.4 Comparing Means: Variances Unequal 10.5 Compairing Means: Paried Data 10.6 Alternative Nonparametric Methods 11 Sample Linear Regression and Correlation 11.1 Model and Parameter Estimation 11.2 Properties of Least-Squares Estimators 11.3 Confidence Interval Estimation and Hypothesis Testing 11.4 Repeated Measurements and Lack of Fit 11.5 Correlation 12 Multiple Linear Regression Models 12.1 Least-Squares Procedures for Model Fitting 12.2 A Matrix Approach to Least Squares 12.3 Properties of the Least-Squares Estimators 12.4 Interval Estimation 12.5 Testing Hypotheses about Model Parameters 12.6 Use of Indicator or "Dummy" Variables 12.7 Criteria for Variable Selection 12.8 Concluding comments 13 Analysis of Variance 13.1 One-Way Classification Fixed-Effects Model 13.2 Comparing Variances 13.3 Pairwise Comparison 13.4 Testing Contrasts 13.5 Randomized Complete Block Design 13.6 Latin Squares 13.7 Random-Effects Models 13.8 Design Models in Matrix Form 13.9 Alternative Nonparametric Methods 14 Factorial Experiments 14.1 Two-Factor Analysis of Variance 14.2 Extension to Three Factors 14.3 Random and Mixed Model Factorial Experiments 14.4 2^k Factorial Experiments 14.5 2^k Factorial Experiments in an Incomplete Block Design 14.6 Fractional Factorial Experiments 15 Categorical Data 15.1 Multinomial Distribution 15.2 Chi-Squared Goodness of Fit Tests 15.3 Testing for Independence 15.4 Comparing Proportions 16 Statistical Quality Control 16.1 X Charts and R Charts 16.2 Shewart P Charts and C Charts 16.4 Tolerance Limits 16.3 Acceptance Sampling 16.4 Extensions in Quality Control Appendix A Statistical Tables Appendix B Answers to Selected Problems index
Côte titre :	Fs/14349

Introduction to probability and statistics : Principles and applications for engineering and the computing sciences [texte imprimé] / J. Susan Milton ; Jesse C. Arnold  . -  2e éd. . - New York : McGraw-Hill, 1990 . - 1 vol (700 p.) : ill. ; 25 cm. - (McGraw-Hill series in probability and statistics) .
ISBN : 978-0-07-042353-4
Includes index.

Catégories :	Mathématique
Mots-clés :	Probabilité Statistiques
Note de contenu :	Sommaire 1 Introduction to Probability and Counting 1.1 Interpreting Probabilities 1.2 Sample Spaces and Events 1.3 Permutations and Combinations 2 Some Probability Laws 2.1 Axioms of Probability 2.2 Conditional Probability 2.3 Independence and the Multiplication Rule 2.4 Bayes' Theorem 3 Discrete Distributions 3.1 Random Variables 3.2 Discrete Probablility Densities 3.3 Expectation and Distribution Parameters 3.4 Geometric Distribution and the Moment Generating Function 3.5 Binomial Distribution 3.6 Negative Binomial Distribution 3.7 Hypergeometric Distribution 3.8 Poisson Distribution 4 Continuous Distributions 4.1 Continuous Densities 4.2 Expectation and Distribution Parameters 4.3 Gamma Distribution 4.4 Normal Distribution 4.5 Normal Probability Rule and Chebyshev's Inequality 4.6 Normal Approximation to the Binomial Distribution 4.7 Weibull Distribution and Reliability 4.8 Transformation of Variables 4.9 Simulating a Continuous Distribution 5 Joint Distributions 5.1 Joint Densities and Independence 5.2 Expectation and Covariance 5.3 Correlation 5.4 Conditional Densities and Regression 5.5 Transformation of Variables 6 Descriptive Statistics 6.1 Random Sampling 6.2 Picturing the Distribution 6.3 Sample Statistics 7 Estimation 7.1 Point Estimation 7.2 The Method of Moments and Maximum Likelihood 7.3 Functions of Random Variables--Distribution of X 7.4 Interval Estimation and the Central Limit Theorem 8 Inferences on the Mean and Variance of a Distribution 8.1 Interval Estimation of Variability 8.2 Estimating the Mean and the Student-t Distribution 8.3 Hypothesis Testing 8.4 Significance Testing 8.5 Hypothesis and Significance Tests on the Mean 8.6 Hypothesis Tests 8.7 Alternative Nonparametric Methods 9 Inferences on Proportions 9.1 Estimating Proportions 9.2 Testing Hypothesis on a Proportion 9.3 Comparing Two Proportions: Estimation 9.4 Coparing Two Proportions: Hypothesis Testing 10 Comparing Two Means and Two Variances 10.1 Point Estimation 10.2 Comparing Variances: The F Distribution 10.3 Comparing Means: Variances Equal (Pooled Test) 10.4 Comparing Means: Variances Unequal 10.5 Compairing Means: Paried Data 10.6 Alternative Nonparametric Methods 11 Sample Linear Regression and Correlation 11.1 Model and Parameter Estimation 11.2 Properties of Least-Squares Estimators 11.3 Confidence Interval Estimation and Hypothesis Testing 11.4 Repeated Measurements and Lack of Fit 11.5 Correlation 12 Multiple Linear Regression Models 12.1 Least-Squares Procedures for Model Fitting 12.2 A Matrix Approach to Least Squares 12.3 Properties of the Least-Squares Estimators 12.4 Interval Estimation 12.5 Testing Hypotheses about Model Parameters 12.6 Use of Indicator or "Dummy" Variables 12.7 Criteria for Variable Selection 12.8 Concluding comments 13 Analysis of Variance 13.1 One-Way Classification Fixed-Effects Model 13.2 Comparing Variances 13.3 Pairwise Comparison 13.4 Testing Contrasts 13.5 Randomized Complete Block Design 13.6 Latin Squares 13.7 Random-Effects Models 13.8 Design Models in Matrix Form 13.9 Alternative Nonparametric Methods 14 Factorial Experiments 14.1 Two-Factor Analysis of Variance 14.2 Extension to Three Factors 14.3 Random and Mixed Model Factorial Experiments 14.4 2^k Factorial Experiments 14.5 2^k Factorial Experiments in an Incomplete Block Design 14.6 Fractional Factorial Experiments 15 Categorical Data 15.1 Multinomial Distribution 15.2 Chi-Squared Goodness of Fit Tests 15.3 Testing for Independence 15.4 Comparing Proportions 16 Statistical Quality Control 16.1 X Charts and R Charts 16.2 Shewart P Charts and C Charts 16.4 Tolerance Limits 16.3 Acceptance Sampling 16.4 Extensions in Quality Control Appendix A Statistical Tables Appendix B Answers to Selected Problems index
Côte titre :	Fs/14349