Essentials of Probability & Statistics for Engineers & Scientists
International Edition
Published on 13. March 2012
Book
Paperback/Softback
480 pages
978-0-321-84584-9 (ISBN)
Description
This text covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. The authors assume one semester of differential and integral calculus as a prerequisite.
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Ronald Walpole | Ronald E. Walpole | Raymond H. Myers
Essentials of Probability & Statistics for Engineers & Scientists
Pearson New International Edition
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08/2013
1st Edition
Pearson Education Limited
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Content
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. Linear Regression
7.1 Introduction to Linear Regression
7.2 The Simple Linear Regression (SLR) Model and the Least Squares Method.
Exercises
7.3 Inferences Concerning the Regression Coefficients.
7.4 Prediction
Exercises
7.5 Analysis-of-Variance Approach
7.6 Test for Linearity of Regression: Data with Repeated Observations
Exercises
7.7 Diagnostic Plots of Residuals: Graphical Detection of Violation of Assumptions
7.8 Correlation
7.9 Simple Linear Regression Case Study.
Exercises
7.10 Multiple Linear Regression and Estimation of the Coefficients
Exercises
7.11 Inferences in Multiple Linear Regression
Exercises
Review Exercises
8. One-Factor Experiments: General
8.1 Analysis-of-Variance Technique and the Strategy of Experimental Design
8.2 One-Way Analysis of Variance (One-Way ANOVA): Completely Randomized Design
8.3 Tests for the Equality of Several Variances
Exercises
8.4 Multiple Comparisons
Exercises
8.5 Concept of Blocks and the Randomized Complete Block Design
Exercises
8.6 Random Effects Models
8.7 Case Study for One-Way Experiment
Exercises
Review Exercises
8.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
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
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. Linear Regression
7.1 Introduction to Linear Regression
7.2 The Simple Linear Regression (SLR) Model and the Least Squares Method.
Exercises
7.3 Inferences Concerning the Regression Coefficients.
7.4 Prediction
Exercises
7.5 Analysis-of-Variance Approach
7.6 Test for Linearity of Regression: Data with Repeated Observations
Exercises
7.7 Diagnostic Plots of Residuals: Graphical Detection of Violation of Assumptions
7.8 Correlation
7.9 Simple Linear Regression Case Study.
Exercises
7.10 Multiple Linear Regression and Estimation of the Coefficients
Exercises
7.11 Inferences in Multiple Linear Regression
Exercises
Review Exercises
8. One-Factor Experiments: General
8.1 Analysis-of-Variance Technique and the Strategy of Experimental Design
8.2 One-Way Analysis of Variance (One-Way ANOVA): Completely Randomized Design
8.3 Tests for the Equality of Several Variances
Exercises
8.4 Multiple Comparisons
Exercises
8.5 Concept of Blocks and the Randomized Complete Block Design
Exercises
8.6 Random Effects Models
8.7 Case Study for One-Way Experiment
Exercises
Review Exercises
8.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
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