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Introduction to probability and statistics / J. Susan Milton
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
StatistiquesNote 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
StatistiquesNote 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
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