Essentials of Probability & Statistics for Engineers & Scientists: Pearson New International Edition


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Spécifications


Éditeur
Pearson Education
Auteur
Ronald Walpole, Raymond Myers, Sharon Myers, Keying Ye,
Langue
anglais
BISAC Subject Heading
NON000000 NON-CLASSIFIABLE
BIC subject category (UK)
WZ Miscellaneous items
Code publique Onix
05 Enseignement supérieur
Date de première publication du titre
01 novembre 2013
Subject Scheme Identifier Code
Classification thématique Thema: Papeterie et éléments divers

VitalSource eBook


Date de publication
01 novembre 2013
ISBN-13
9781292035734
Ampleur
Nombre de pages de contenu principal : 480
Code interne
1292035730
Protection technique e-livre
DRM

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Sommaire


1. Introduction to Statistics and Probability

1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability

1.2 Sampling Procedures; Collection of Data

1.3 Discrete and Continuous Data.

1.4 Probability: Sample Space and Events

   Exercises

1.5 Counting Sample Points

   Exercises

1.6 Probability of an Event

1.7 Additive Rules

   Exercises

1.8 Conditional Probability, Independence, and the Product Rule

   Exercises

1.9 Bayes' Rule

   Exercises

   Review Exercises

 

2. Random Variables, Distributions, and Expectations

2.1 Concept of a Random Variable

2.2 Discrete Probability Distributions

2.3 Continuous Probability Distributions

   Exercises

2.4 Joint Probability Distributions

   Exercises

2.5 Mean of a Random Variable

   Exercises

2.6 Variance and Covariance of Random Variables.

   Exercises

2.7 Means and Variances of Linear Combinations of Random Variables

   Exercises

   Review Exercises

2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

 

3. Some Probability Distributions

3.1 Introduction and Motivation

3.2 Binomial and Multinomial Distributions

   Exercises

3.3 Hypergeometric Distribution

   Exercises

3.4 Negative Binomial and Geometric Distributions

3.5 Poisson Distribution and the Poisson Process

   Exercises

3.6 Continuous Uniform Distribution

3.7 Normal Distribution

3.8 Areas under the Normal Curve

3.9 Applications of the Normal Distribution

   Exercises

3.10 Normal Approximation to the Binomial

   Exercises

3.11 Gamma and Exponential Distributions

3.12 Chi-Squared Distribution.

   Exercises

   Review Exercises

3.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

 

4. Sampling Distributions and Data Descriptions

4.1 Random Sampling

4.2 Some Important Statistics

   Exercises

4.3 Sampling Distributions

4.4 Sampling Distribution of Means and the Central Limit Theorem

   Exercises

4.5 Sampling Distribution of S2

4.6 t-Distribution

4.7 F-Distribution

4.8 Graphical Presentation

   Exercises

   Review Exercises

4.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

 

5. One- and Two-Sample Estimation Problems

5.1 Introduction

5.2 Statistical Inference

5.3 Classical Methods of Estimation.

5.4 Single Sample: Estimating the Mean

5.5 Standard Error of a Point Estimate

5.6 Prediction Intervals

5.7 Tolerance Limits

   Exercises

5.8 Two Samples: Estimating the Difference between Two Means

5.9 Paired Observations

   Exercises

5.10 Single Sample: Estimating a Proportion

5.11 Two Samples: Estimating the Difference between Two Proportions

   Exercises

5.12 Single Sample: Estimating the Variance

   Exercises

   Review Exercises

5.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

 

6. One- and Two-Sample Tests of Hypotheses.

6.1 Statistical Hypotheses: General Concepts

6.2 Testing a Statistical Hypothesis

6.3 The Use of P-Values for Decision Making in Testing Hypotheses

   Exercises

6.4 Single Sample: Tests Concerning a Single Mean

6.5 Two Samples: Tests on Two Means

6.6 Choice of Sample Size for Testing Means

6.7 Graphical Methods for Comparing Means

   Exercises

6.8 One Sample: Test on a Single Proportion.

6.9 Two Samples: Tests on Two Proportions

   Exercises

6.10 Goodness-of-Fit Test

6.11 Test for Independence (Categorical Data)

6.12 Test for Homogeneity

6.13 Two-Sample Case Study

   Exercises

   Review Exercises

6.14 Potential Misconceptions and Hazards;

Relationship to Material in Other Chapters

 

7. One-Factor Experiments: General

7.1 Analysis-of-Variance Technique and the Strategy of Experimental Design

7.2 One-Way Analysis of Variance (One-Way ANOVA): Completely Randomized Design

7.3 Tests for the Equality of Several Variances

   Exercises

7.4 Multiple Comparisons

   Exercises

7.5 Concept of Blocks and the Randomized Complete Block Design

   Exercises

7.6 Random Effects Models

7.7 Case Study for One-Way Experiment

   Exercises

   Review Exercises

7.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

 

8. Linear Regression

8.1 Introduction to Linear Regression

8.2 The Simple Linear Regression (SLR) Model and the Least Squares Method.

Exercises

8.3 Inferences Concerning the Regression Coefficients.

8.4 Prediction

  Exercises

8.5 Analysis-of-Variance Approach

8.6 Test for Linearity of Regression: Data with Repeated Observations

   Exercises

8.7 Diagnostic Plots of Residuals: Graphical Detection of Violation of Assumptions

8.8 Correlation

8.9 Simple Linear Regression Case Study.

   Exercises

8.10 Multiple Linear Regression and Estimation of the Coefficients

   Exercises

8.11 Inferences in Multiple Linear Regression

   Exercises

   Review Exercises

 

9. Factorial Experiments (Two or More Factors)

9.1 Introduction

9.2 Interaction in the Two-Factor Experiment

9.3 Two-Factor Analysis of Variance

   Exercises

9.4 Three-Factor Experiments.

   Exercises

   Review Exercises

9.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

 


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