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by Stephen B. Vardeman and J. Marcus Jobe

Cover type: HardbackEdition: 01

Copyright: 2001

Publisher: Duxbury Press

Published: 2001

International: No

List price: $200.00

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Stephen Vardeman and J. Marcus Jobe's motivating new book is appropriate for students in introductory engineering statistics courses, including chemical, mechanical, environmental, civil, electrical, and industrial. The authors stress the practical issues in data collection and the interpretation of the results of statistical studies over mathematical theory. Using real data and scenario examples to teach readers how to apply statistical methods, the book clearly and patiently helps students learn to solve engineering problems. The book's practical, applied approach encourages students to ''do'' statistics by carrying data collection and analysis projects all the way from problem formulation to preparation of professional technical reports.

1. INTRODUCTION.

Engineering Statistics: What and Why.

Basic Terminology.

Measurement: Its Importance and Difficulty. Mathematical Models, Reality, And Data Analysis.

2. DATA COLLECTION.

General Principles in the Collection of Engineering Data.

Sampling in Enumerative Studies.

Principles for Effective Experimentation.

Some Common Experimental Plans. Preparing to Collect Engineering Data.

3. ELEMENTARY DESCRIPTIVE STATISTICS.

Elementary Graphical and Tabular Treatment of Quantitative Data.

Quantiles and Related Graphical Tools.

Standard Numerical Summary Measures.

Descriptive Statistics for Qualitative and Count Data.

4. DESCRIBING RELATIONSHIPS BETWEEN VARIABLES.

Fitting a Line by Least Squares. Fitting Curves and Surfaces by Least Squares.

Fitted Effects for Factorial Data.

Transformations and Choice of Measurement Scale.

Beyond Descriptive Statistics.

5. THE PROBABILITY: THE MATHEMATICS OF RANDOMNESS.

(Discrete) Random Variables.

Continuous Random Variables.

Probability Plotting (Optional).

Joint Distributions and Independence. Functions of Several Random Variables.

6. INTRODUCTION TO FORMAL STATISTICAL INFERENCE.

Large-Sample Confidence Intervals for a Mean.

Large-Sample Significance Tests for a Mean.

One- and Two-Sample Inference for Means.

One- and Two-Sample Inference for Variances.

ONe- and Two-Sample Inference for Proportions.

Prediction and Tolerance Intervals.

7. INFERENCE OF UNSTRUCTURED MULTISAMPLE STUDIES.

The One-Way Normal Model.

Simple Confidence Intervals in Multisample Studies.

Two Simultaneous Confidence Interval Methods.

One-Way Analysis of Variance (ANOVA).

Shewhart Control Charts for Measurement Data.

Shewhart Control Charts for Qualitative and Count Data.

8. INFERENCE FOR FULL AND FRACTIONAL FACTORIAL STUDIES.

Basic Inference in Two-Way Factorials with Some Replication.

p-Factor Studies with Two Levels for Each Factor.

Standard Fractions of Two-Level Factorials, PartI: 1/2 Fractions.

Standard Fractions of Two-Level Factorials Part II: General 2p-q Studies.

9. REGRESSION ANALYSIS-INFERENCE FOR CURVE- AND SURFACE-FITTING.

Inference Methods Related to the Least Squares Fitting of a Line (Simple Linear Regression).

Inference Methods for General Least Squares Curve- and Surface-Fitting (Multiple Linear Regression).

Application of Multiple Regression in Response Surface Problems and Fctorial Analyses.

APPENDICES.

A: More On Probability and Model Fitting.

More Elemetary Probability.

Applications of Simple Probability to System Reliability Prediction.

Counting.

Probabilistic Concepts Useful in Survival Analysis.

Maximum Likelihood Fitting of Probability Models and Related Inference Methods. B: Tables.

Answers To End-Of-Section Exercises.

INDEX.

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Summary

Stephen Vardeman and J. Marcus Jobe's motivating new book is appropriate for students in introductory engineering statistics courses, including chemical, mechanical, environmental, civil, electrical, and industrial. The authors stress the practical issues in data collection and the interpretation of the results of statistical studies over mathematical theory. Using real data and scenario examples to teach readers how to apply statistical methods, the book clearly and patiently helps students learn to solve engineering problems. The book's practical, applied approach encourages students to ''do'' statistics by carrying data collection and analysis projects all the way from problem formulation to preparation of professional technical reports.

Table of Contents

1. INTRODUCTION.

Engineering Statistics: What and Why.

Basic Terminology.

Measurement: Its Importance and Difficulty. Mathematical Models, Reality, And Data Analysis.

2. DATA COLLECTION.

General Principles in the Collection of Engineering Data.

Sampling in Enumerative Studies.

Principles for Effective Experimentation.

Some Common Experimental Plans. Preparing to Collect Engineering Data.

3. ELEMENTARY DESCRIPTIVE STATISTICS.

Elementary Graphical and Tabular Treatment of Quantitative Data.

Quantiles and Related Graphical Tools.

Standard Numerical Summary Measures.

Descriptive Statistics for Qualitative and Count Data.

4. DESCRIBING RELATIONSHIPS BETWEEN VARIABLES.

Fitting a Line by Least Squares. Fitting Curves and Surfaces by Least Squares.

Fitted Effects for Factorial Data.

Transformations and Choice of Measurement Scale.

Beyond Descriptive Statistics.

5. THE PROBABILITY: THE MATHEMATICS OF RANDOMNESS.

(Discrete) Random Variables.

Continuous Random Variables.

Probability Plotting (Optional).

Joint Distributions and Independence. Functions of Several Random Variables.

6. INTRODUCTION TO FORMAL STATISTICAL INFERENCE.

Large-Sample Confidence Intervals for a Mean.

Large-Sample Significance Tests for a Mean.

One- and Two-Sample Inference for Means.

One- and Two-Sample Inference for Variances.

ONe- and Two-Sample Inference for Proportions.

Prediction and Tolerance Intervals.

7. INFERENCE OF UNSTRUCTURED MULTISAMPLE STUDIES.

The One-Way Normal Model.

Simple Confidence Intervals in Multisample Studies.

Two Simultaneous Confidence Interval Methods.

One-Way Analysis of Variance (ANOVA).

Shewhart Control Charts for Measurement Data.

Shewhart Control Charts for Qualitative and Count Data.

8. INFERENCE FOR FULL AND FRACTIONAL FACTORIAL STUDIES.

Basic Inference in Two-Way Factorials with Some Replication.

p-Factor Studies with Two Levels for Each Factor.

Standard Fractions of Two-Level Factorials, PartI: 1/2 Fractions.

Standard Fractions of Two-Level Factorials Part II: General 2p-q Studies.

9. REGRESSION ANALYSIS-INFERENCE FOR CURVE- AND SURFACE-FITTING.

Inference Methods Related to the Least Squares Fitting of a Line (Simple Linear Regression).

Inference Methods for General Least Squares Curve- and Surface-Fitting (Multiple Linear Regression).

Application of Multiple Regression in Response Surface Problems and Fctorial Analyses.

APPENDICES.

A: More On Probability and Model Fitting.

More Elemetary Probability.

Applications of Simple Probability to System Reliability Prediction.

Counting.

Probabilistic Concepts Useful in Survival Analysis.

Maximum Likelihood Fitting of Probability Models and Related Inference Methods. B: Tables.

Answers To End-Of-Section Exercises.

INDEX.

Publisher Info

Publisher: Duxbury Press

Published: 2001

International: No

Published: 2001

International: No

1. INTRODUCTION.

Engineering Statistics: What and Why.

Basic Terminology.

Measurement: Its Importance and Difficulty. Mathematical Models, Reality, And Data Analysis.

2. DATA COLLECTION.

General Principles in the Collection of Engineering Data.

Sampling in Enumerative Studies.

Principles for Effective Experimentation.

Some Common Experimental Plans. Preparing to Collect Engineering Data.

3. ELEMENTARY DESCRIPTIVE STATISTICS.

Elementary Graphical and Tabular Treatment of Quantitative Data.

Quantiles and Related Graphical Tools.

Standard Numerical Summary Measures.

Descriptive Statistics for Qualitative and Count Data.

4. DESCRIBING RELATIONSHIPS BETWEEN VARIABLES.

Fitting a Line by Least Squares. Fitting Curves and Surfaces by Least Squares.

Fitted Effects for Factorial Data.

Transformations and Choice of Measurement Scale.

Beyond Descriptive Statistics.

5. THE PROBABILITY: THE MATHEMATICS OF RANDOMNESS.

(Discrete) Random Variables.

Continuous Random Variables.

Probability Plotting (Optional).

Joint Distributions and Independence. Functions of Several Random Variables.

6. INTRODUCTION TO FORMAL STATISTICAL INFERENCE.

Large-Sample Confidence Intervals for a Mean.

Large-Sample Significance Tests for a Mean.

One- and Two-Sample Inference for Means.

One- and Two-Sample Inference for Variances.

ONe- and Two-Sample Inference for Proportions.

Prediction and Tolerance Intervals.

7. INFERENCE OF UNSTRUCTURED MULTISAMPLE STUDIES.

The One-Way Normal Model.

Simple Confidence Intervals in Multisample Studies.

Two Simultaneous Confidence Interval Methods.

One-Way Analysis of Variance (ANOVA).

Shewhart Control Charts for Measurement Data.

Shewhart Control Charts for Qualitative and Count Data.

8. INFERENCE FOR FULL AND FRACTIONAL FACTORIAL STUDIES.

Basic Inference in Two-Way Factorials with Some Replication.

p-Factor Studies with Two Levels for Each Factor.

Standard Fractions of Two-Level Factorials, PartI: 1/2 Fractions.

Standard Fractions of Two-Level Factorials Part II: General 2p-q Studies.

9. REGRESSION ANALYSIS-INFERENCE FOR CURVE- AND SURFACE-FITTING.

Inference Methods Related to the Least Squares Fitting of a Line (Simple Linear Regression).

Inference Methods for General Least Squares Curve- and Surface-Fitting (Multiple Linear Regression).

Application of Multiple Regression in Response Surface Problems and Fctorial Analyses.

APPENDICES.

A: More On Probability and Model Fitting.

More Elemetary Probability.

Applications of Simple Probability to System Reliability Prediction.

Counting.

Probabilistic Concepts Useful in Survival Analysis.

Maximum Likelihood Fitting of Probability Models and Related Inference Methods. B: Tables.

Answers To End-Of-Section Exercises.

INDEX.