Applied Statistics and Probability for Engineers

ISBN-10: 0471745898
ISBN-13: 9780471745891
Edition: 4th 2007 (Revised)
List price: $224.95
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Description: Montgomery and Runger's 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's packed with unique problem sets that reflect  More...

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Book details

List price: $224.95
Edition: 4th
Copyright year: 2007
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/12/2006
Binding: Hardcover
Pages: 784
Size: 8.25" wide x 10.25" long x 1.25" tall
Weight: 3.410
Language: English

Montgomery and Runger's 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's packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives. This book provides modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control. Developed with sponsorship from the National Science Foundation, this text incorporates many insights from the authors' teaching experience along with feedback from numerous adopters of previous editions. This text is suitable for a one- or two-term course in probability and statistics for all engineering majors.

The Role of Statistics in Engineering
The Engineering Method and Statistical Thinking
Collecting Engineering Data
Basic Principles
Retrospective Study
Observational Study
Designed Experiments
Observing Processes Over Time
Mechanistic and Empirical Models
Probability and Probability Models
Probability
Sample Spaces and Events
Random Experiments
Sample Spaces
Events
Counting Techniques
Interpretations of Probability
Introduction
Axioms of Probability
Addition Rules
Conditional Probability
Multiplication and Total Probability Rules
Multiplication Rule
Total Probability Rule
Independence
Bayes' Theorem
Random Variables
Discrete Random Variables and Probability Distributions
Discrete Random Variables
Probability Distributions and Probability Mass Functions
Cumulative Distribution Functions
Mean and Variance of a Discrete Random Variable
Discrete Uniform Distribution
Binomial Distribution
Geometric and Negative Binomial Distributions
Geometric Distribution
Negative Binomial Distribution
Hypergeometric Distribution
Poisson Distribution
Continuous Random Variables and Probability Distributions
Continuous Random Variables
Probability Distributions and Probability Density Functions
Cumulative Distribution Functions
Mean and Variance of a Continuous Random Variable
Continuous Uniform Distribution
Normal Distribution
Normal Approximation to the Binomial and Poisson Distributions
Exponential Distribution
Erlang and Gamma Distributions
Weibull Distribution
Lognormal Distribution
Joint Probability Distributions
Two or More Discrete Random Variables
Joint Probability Distributions
Marginal Probability Distributions
Conditional Probability Distributions
Independence
Multiple Discrete Random Variables
Multinomial Probability Distribution
Two or More Continuous Random Variables
Joint Probability Distributions
Marginal Probability Distributions
Conditional Probability Distributions
Independence
Multiple Continuous Random Variables
Covariance and Correlation
Bivariate Normal Distribution
Linear Functions of Random Variables
Several Functions of Random Variables
Random Sampling and Data Description
Numerical Summaries
Stem-and-Leaf Diagrams
Frequency Distributions and Histograms
Box Plots
Time Sequence Plots
Probability Plots
Sampling Distributions and Point Estimation of Parameters
Introduction
Sampling Distributions and the Central Limit Theorem
General Concepts of Point Estimation
Unbiased Estimators
Variance of a Point Estimator
Standard Error: Reporting a Point Estimator
Mean Squared Error of an Estimator
Methods of Point Estimation
Method of Moments
Method of Maximum Likelihood
Bayesian Estimation of Parameters
Statistical Intervals for a Single Sample
Introduction
Confidence Interval on the Mean of a Normal Distribution, Variance Known
Development of the Confidence Interval and Its Basic Properties
Choice of Sample Size
One-sided Confidence Bounds
General Method to Derive a Confidence Interval
Large-Sample Confidence Interval for [mu]
Confidence Interval on the Mean of a Normal Distribution, Variance Unknown
t Distribution
t Confidence Interval on [mu]
Confidence Interval on the Variance and Standard Deviation of a Normal Distribution
Large-Sample Confidence Interval for a Population Proportion
Guidelines for Constructing Confidence Intervals
Tolerance and Prediction Intervals
Prediction Interval for a Future Observation
Tolerance Interval for a Normal Distribution
Tests of Hypotheses for a Single Sample
Hypothesis Testing
Statistical Hypotheses
Tests of statistical Hypotheses
One-Sided and Two-Sided Hypothesis
P-Values in Hypothesis Tests
Connection between Hypothesis Tests and Confidence Intervals
General Procedure for Hypothesis Tests
Tests on the Mean of a Normal Distribution, Variance Known
Hypothesis Tests on the Mean
Type II Error and Choice of Sample Size
Large Sample Test
Tests on the Mean of a Normal Distribution, Variance Unknown
Hypothesis Tests on the Mean
P-Value for a t-Test
Type II Error and Choice of Sample Size
Tests on the Variance and Standard Deviation of a Normal Distribution
Hypothesis Tests on the Variance
Type II Error and Choice of Sample Size
Tests on a Population Proportion
Large-Sample Tests on a Proportion
Type II Error and Choice of Sample Size
Summary Table of Inference Procedures for a Single Sample
Testing for Goodness of Fit
Contingency Table Tests
Statistical Inference for Two Samples
Introduction
Inference on the Difference in Means of Two Normal Distributions, Variances Known
Hypothesis Tests on the Difference in Means, Variances Known
Type II Error and Choice of Sample Size
Confidence Interval on the Difference in Means, Variances Known
Inference on the Difference in Means of Two Normal Distributions, Variances Unknown
Hypothesis Tests on the Difference in Means, Variances Unknown
Type II Error and Choice of Sample Size
Confidence Interval on the Difference in Means, Variances Unknown
Paired t-Test
Inference on the Variances of Two Normal Distributions
F Distribution
Hypothesis Tests on the Ratio of Two Variances
Type II Error and Choice of Sample Size
Confidence Interval on the Ratio of Two Variances
Inference on Two Population Proportions
Large-Sample Tests on the Difference in Population Proportions
Type II Error and Choice of Sample Size
Confidence Interval on the Difference in Population Proportions
Summary Table and Roadmaps for Inference Procedures for Two Samples
Simple Linear Regression and Correlation
Empirical Models
Simple Linear Regression
Properties of the Least Squares Estimators
Hypothesis Tests in Simple Linear Regression
Use of t-Tests
Analysis of Variance Approach to Test Significance of Regression
Confidence Intervals
Confidence Intervals on the Slope and Intercept
Confidence Interval on the Mean Response
Prediction of New Observations
Adequacy of the Regression Model
Residual Analysis
Coefficient of Determination (R[superscript 2])
Correlation
Transformations
Logistic Regression available at www.wiley.com/college/montgomery
Multiple Linear Regression
Multiple Linear Regression Model
Introduction
Least Squares Estimation of the Parameters
Matrix Approach to Multiple Linear Regression
Properties of the Least Squares Estimators
Hypothesis Tests in Multiple Linear Regression
Test for Significance of Regression
Tests on Individual Regression Coefficients and Subsets of Coefficients
Confidence Intervals in Multiple Linear Regression
Confidence Intervals on Individual Regression Coefficients
Confidence Interval on the Mean Response
Prediction of New Observations
Model Adequacy Checking
Residual Analysis
Influential Observations
Aspects of Multiple Regression Modeling
Polynomial Regression Models
Categorical Regressors and Indicator Variables
Selection of Variables and Model Building
Multicollinearity
Design and Analysis of Single-Factor Experiments: The Analysis of Variance
Designing Engineering Experiments
Completely Randomized Single-Factor Experiment
Example: Tensile Strength
Analysis of Variance
Multiple Comparisons Following the ANOVA
Residual Analysis and Model Checking
Determining Sample Size
Random Effects Model
Fixed Versus Random Factors
ANOVA and Variance Components
Randomized Complete Block Design
Design and Statistical Analysis
Multiple Comparisons
Residual Analysis and Model Checking
Design of Experiments with Several Factors
Introduction
Factorial Experiments
Two-Factor Factorial Experiments
Statistical Analysis of the Fixed-Effects Model
Model Adequacy Checking
One Observation Per Cell
General Factorial Experiments
2[superscript k] Factorial Designs
2[superscript 2] Design
2[superscript k] Design for k [greater than or equal] 3 Factors
Single Replicate of the 2[superscript k] Design
Addition of Center Points to a 2[superscript k] Design
Blocking and Confounding in the 2[superscript k] Design
Fractional Replication of the 2[superscript k] Design
One Half Fraction of the 2[superscript k] Design
Smaller Fractions: The 2[superscript k-p] Fractional Factorial
Response Surface Methods and Designs
Nonparametric Statistics
Introduction
Sign Test
Description of the Test
Sign Test for Paired Samples
Type II Error for the Sign Test
Comparison to the t-Test
Wilcoxon Signed-Rank Test
Description of the Test
Large-Sample Approximation
Paired Observations
Comparison to the t-Test
Wilcoxon Rank-Sum Test
Description of the Test
Large-Sample Approximation
Comparison to the t-Test
Nonparametric Methods in the Analysis of Variance
Kruskal-Wallis Test
Rank Transformation
Runs Test
Statistical Quality Control
Quality Improvement and Statistics
Statistical Quality Control
Statistical Process Control
Introduction to Control Charts
Basic Principles
Design of a Control Chart
Rational Subgroups
Analysis of Patterns on Control Charts
X and R or S Control Charts
Control Charts for Individual Measurements
Process Capability
Attribute Control Charts
P Chart (Control Chart for Proportion)
U Chart (Control Chart for Defects per Unit)
Control Chart Performance
Time-Weighted Charts
Cumulative Sum Control Chart
Exponentially Weighted Moving Average Control Chart
Other SPC Problem-Solving Tools
Implementing SPC
Appendices
Statistical Tables and Charts
Summary of Common Probability Distributions
Cumulative Binomial Distribution
Cumulative Standard Normal Distribution
Percentage Points [characters not reproducible] of the Chi-Squared Distribution
Percentage Points t[subscript alpha, upsilon] of the t-distribution
Percentage Points [characters not reproducible] of the F-distribution
Operating Characteristic Curves
Critical Values for the Sign Test
Critical Values for the Wilcoxon Signed-Rank Test
Critical Values for the Wilcoxon Rank-Sum Test
Factors for Constructing Variables Control Charts
Factors for Tolerance Intervals
Answers to Selected Exercises
Bibliography available at www.wiley.com/college/montgomery
Glossary
Index
Applications in Examples and Exercises Continued

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