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by George C. Runger, Douglas C. Montgomery
Applied Statistics and Probability for Engineers, 6th Edition
Cover Page
Title Page
Copyright
Preface
INTENDED AUDIENCE
ORGANIZATION OF THE BOOK
WHAT'S NEW IN THIS EDITION
FEATURED IN THIS BOOK
STUDENT RESOURCES
INSTRUCTOR RESOURCES
COMPUTER SOFTWARE
WileyPLUS
COURSE SYLLABUS SUGGESTIONS
USING THE COMPUTER
ACKNOWLEDGMENTS
Contents
1: The Role of Statistics in Engineering
1-1 The Engineering Method and Statistical Thinking
1-2 Collecting Engineering Data
1-3 Mechanistic and Empirical Models
1-4 Probability and Probability Models
2: Probability
2-1 Sample Spaces and Events
2-2 Interpretations and Axioms of Probability
2-3 Addition Rules
2-4 Conditional Probability
2-5 Multiplication and Total Probability Rules
2-6 Independence
2-7 Bayes' Theorem
2-8 Random Variables
3: Discrete Random Variables and Probability Distributions
3-1 Discrete Random Variables
3-2 Probability Distributions and Probability Mass Functions
3-3 Cumulative Distribution Functions
3-4 Mean and Variance of a Discrete Random Variable
3-5 Discrete Uniform Distribution
3-6 Binomial Distribution
3-7 Geometric and Negative Binomial Distributions
3-8 Hypergeometric Distribution
3-9 Poisson Distribution
4: Continuous Random Variables and Probability Distributions
4-1 Continuous Random Variables
4-2 Probability Distributions and Probability Density Functions
4-3 Cumulative Distribution Functions
4-4 Mean and Variance of a Continuous Random Variable
4-5 Continuous Uniform Distribution
4-6 Normal Distribution
4-7 Normal Approximation to the Binomial and Poisson Distributions
4-8 Exponential Distribution
4-9 Erlang and Gamma Distributions
4-10 Weibull Distribution
4-11 Lognormal Distribution
4-12 Beta Distribution
5: Joint Probability Distributions
5-1 Two or More Random Variables
5-2 Covariance and Correlation
5-3 Common Joint Distributions
5-4 Linear Functions of Random Variables
5-5 General Functions of Random Variables
5-6 Moment-Generating Functions
6: Descriptive Statistics
6-1 Numerical Summaries of Data
6-2 Stem-and-Leaf Diagrams
6-3 Frequency Distributions and Histograms
6-4 Box Plots
6-5 Time Sequence Plots
6-6 Scatter Diagrams
6-7 Probability Plots
7: Point Estimation of Parameters and Sampling Distributions
Introduction
7-1 Point Estimation
7-2 Sampling Distributions and the Central Limit Theorem
7-3 General Concepts of Point Estimation
7-4 Methods of Point Estimation
8: Statistical Intervals for a Single Sample
Introduction
8-1 Confidence Interval on the Mean of a Normal Distribution, Variance Known
8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Unknown
8-3 Confidence Interval on the Variance and Standard Deviation of a Normal Distribution
8-4 Large-Sample Confidence Interval for a Population Proportion
8-5 Guidelines for Constructing Confidence Intervals
8.6 Bootstrap Confidence Interval
8-7 Tolerance and Prediction Intervals
9: Tests of Hypotheses for a Single Sample
INTRODUCTION
9-1 Hypothesis Testing
9-2 Tests on the Mean of a Normal Distribution, Variance Known
9-3 Tests on the Mean of a Normal Distribution, Variance Unknown
9-4 Tests on the Variance and Standard Deviation of a Normal Distribution
9-5 Tests on a Population Proportion
9-6 Summary Table of Inference Procedures for a Single Sample
9-7 Testing for Goodness of Fit
9-8 Contingency Table Tests
9-9 Nonparametric Procedures
9-10 Equivalence Testing
9-11 Combining P -Values
10: Statistical Inference for Two Samples
10-1 Inference on the Difference in Means of Two Normal Distributions, Variances Known
10-2 Inference on the Difference in Means of two Normal Distributions, Variances Unknown
10-3 A Nonparametric Test for the Difference in Two Means
10-4 Paired t -Test
10-5 Inference on the Variances of Two Normal Distributions
10-6 Inference on Two Population Proportions
10-7 Summary Table and Road Map for Inference Procedures for Two Samples
11: Simple Linear Regression and Correlation
11-1 Empirical Models
11-2 Simple Linear Regression
11-3 Properties of the Least Squares Estimators
11-4 Hypothesis Tests in Simple Linear Regression
11-5 Confidence Intervals
11-6 Prediction of New Observations
11-7 Adequacy of the Regression Model
11-8 Correlation
11-9 Regression on Transformed Variables
11-10 Logistic Regression
12: Multiple Linear Regression
12-1 Multiple Linear Regression Model
12-2 Hypothesis Tests In Multiple Linear Regression
12-3 Confidence Intervals In Multiple Linear Regression
12-4 Prediction of New Observations
12-5 Model Adequacy Checking
12-6 Aspects of Multiple Regression Modeling
13: Design and Analysis of Single-Factor Experiments: The Analysis of Variance
13-1 Designing Engineering Experiments
13-2 Completely Randomized Single-Factor Experiment
13-3 The Random-Effects Model
13-4 Randomized Complete Block Design
14: Design of Experiments with Several Factors
14-1 Introduction
14-2 Factorial Experiments
14-3 Two-Factor Factorial Experiments
14-4 General Factorial Experiments
14-5 2 k Factorial Designs
14-6 Blocking and Confounding in the 2 k Design
14-7 FRACTIONAL REPLICATION OF THE 2 k DESIGN
14-8 Response Surface Methods and Designs
15: Statistical Quality Control
Bowl of beads
15-1 Quality Improvement and Statistics
15-2 Introduction to Control Charts
15-3 and R or S Control Charts
15-4 Control Charts for Individual Measurements
15-5 Process Capability
15-6 Attribute Control Charts
15-7 Control Chart Performance
15-8 Time-Weighted Charts
15-9 Other SPC Problem-Solving Tools
15-10 Decision Theory
15-11 Implementing SPC
Appendices
Appendix A: Statistical Tables and Charts
Appendix B: Bibliography
Appendix C: Answers to Selected Exercises
Glossary
Index
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