book

Applied Statistics and Probability for Engineers, 6th Edition

Name: Applied Statistics and Probability for Engineers, 6th Edition
ISBN: 9781118539712

by Douglas C. Montgomery, George C. Runger

November 2013

Intermediate to advanced

832 pages

34h 40m

English

Wiley

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Cover Page
Title Page
Copyright
Preface
INTENDED AUDIENCEORGANIZATION OF THE BOOKWHAT'S NEW IN THIS EDITIONFEATURED IN THIS BOOKSTUDENT RESOURCESINSTRUCTOR RESOURCESCOMPUTER SOFTWAREWileyPLUSCOURSE SYLLABUS SUGGESTIONSUSING THE COMPUTERACKNOWLEDGMENTS
Contents
1: The Role of Statistics in Engineering
1-1 The Engineering Method and Statistical Thinking1-2 Collecting Engineering Data1-3 Mechanistic and Empirical Models1-4 Probability and Probability Models
2: Probability
2-1 Sample Spaces and Events2-2 Interpretations and Axioms of Probability2-3 Addition Rules2-4 Conditional Probability2-5 Multiplication and Total Probability Rules2-6 Independence2-7 Bayes' Theorem2-8 Random Variables
3: Discrete Random Variables and Probability Distributions
3-1 Discrete Random Variables3-2 Probability Distributions and Probability Mass Functions3-3 Cumulative Distribution Functions3-4 Mean and Variance of a Discrete Random Variable3-5 Discrete Uniform Distribution3-6 Binomial Distribution3-7 Geometric and Negative Binomial Distributions3-8 Hypergeometric Distribution3-9 Poisson Distribution
4: Continuous Random Variables and Probability Distributions
4-1 Continuous Random Variables4-2 Probability Distributions and Probability Density Functions4-3 Cumulative Distribution Functions4-4 Mean and Variance of a Continuous Random Variable4-5 Continuous Uniform Distribution4-6 Normal Distribution4-7 Normal Approximation to the Binomial and Poisson Distributions4-8 Exponential Distribution4-9 Erlang and Gamma Distributions4-10 Weibull Distribution4-11 Lognormal Distribution4-12 Beta Distribution
5: Joint Probability Distributions
5-1 Two or More Random Variables5-2 Covariance and Correlation5-3 Common Joint Distributions5-4 Linear Functions of Random Variables5-5 General Functions of Random Variables5-6 Moment-Generating Functions

6: Descriptive Statistics
6-1 Numerical Summaries of Data6-2 Stem-and-Leaf Diagrams6-3 Frequency Distributions and Histograms6-4 Box Plots6-5 Time Sequence Plots6-6 Scatter Diagrams6-7 Probability Plots
7: Point Estimation of Parameters and Sampling Distributions
Introduction7-1 Point Estimation7-2 Sampling Distributions and the Central Limit Theorem7-3 General Concepts of Point Estimation7-4 Methods of Point Estimation
8: Statistical Intervals for a Single Sample
Introduction8-1 Confidence Interval on the Mean of a Normal Distribution, Variance Known8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Unknown8-3 Confidence Interval on the Variance and Standard Deviation of a Normal Distribution8-4 Large-Sample Confidence Interval for a Population Proportion8-5 Guidelines for Constructing Confidence Intervals8.6 Bootstrap Confidence Interval8-7 Tolerance and Prediction Intervals
9: Tests of Hypotheses for a Single Sample
INTRODUCTION9-1 Hypothesis Testing9-2 Tests on the Mean of a Normal Distribution, Variance Known9-3 Tests on the Mean of a Normal Distribution, Variance Unknown9-4 Tests on the Variance and Standard Deviation of a Normal Distribution9-5 Tests on a Population Proportion9-6 Summary Table of Inference Procedures for a Single Sample9-7 Testing for Goodness of Fit9-8 Contingency Table Tests9-9 Nonparametric Procedures9-10 Equivalence Testing9-11 Combining P -Values
10: Statistical Inference for Two Samples
10-1 Inference on the Difference in Means of Two Normal Distributions, Variances Known10-2 Inference on the Difference in Means of two Normal Distributions, Variances Unknown10-3 A Nonparametric Test for the Difference in Two Means10-4 Paired t -Test10-5 Inference on the Variances of Two Normal Distributions10-6 Inference on Two Population Proportions10-7 Summary Table and Road Map for Inference Procedures for Two Samples
11: Simple Linear Regression and Correlation
11-1 Empirical Models11-2 Simple Linear Regression11-3 Properties of the Least Squares Estimators11-4 Hypothesis Tests in Simple Linear Regression11-5 Confidence Intervals11-6 Prediction of New Observations11-7 Adequacy of the Regression Model11-8 Correlation11-9 Regression on Transformed Variables11-10 Logistic Regression
12: Multiple Linear Regression
12-1 Multiple Linear Regression Model12-2 Hypothesis Tests In Multiple Linear Regression12-3 Confidence Intervals In Multiple Linear Regression12-4 Prediction of New Observations12-5 Model Adequacy Checking12-6 Aspects of Multiple Regression Modeling
13: Design and Analysis of Single-Factor Experiments: The Analysis of Variance
13-1 Designing Engineering Experiments13-2 Completely Randomized Single-Factor Experiment13-3 The Random-Effects Model13-4 Randomized Complete Block Design
14: Design of Experiments with Several Factors
14-1 Introduction14-2 Factorial Experiments14-3 Two-Factor Factorial Experiments14-4 General Factorial Experiments14-5 2 k Factorial Designs14-6 Blocking and Confounding in the 2 k Design14-7 FRACTIONAL REPLICATION OF THE 2 k DESIGN14-8 Response Surface Methods and Designs
15: Statistical Quality Control
Bowl of beads15-1 Quality Improvement and Statistics15-2 Introduction to Control Charts15-3 and R or S Control Charts15-4 Control Charts for Individual Measurements15-5 Process Capability15-6 Attribute Control Charts15-7 Control Chart Performance15-8 Time-Weighted Charts15-9 Other SPC Problem-Solving Tools15-10 Decision Theory15-11 Implementing SPC
Appendices
Appendix A: Statistical Tables and Charts
Appendix B: Bibliography
Appendix C: Answers to Selected Exercises
Glossary
Index
Index of Applications in Examples and Exercises

Overview

This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It is packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives. This text shows how statistics, the science of data is just as important for engineers as the mechanical, electrical, and materials sciences.

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