You are previewing Applied Statistics and Probability for Engineers, 5th Edition.
1. Coverpage
2. Titlepage
4. Contents
5. Preface
6. INSIDE FRONT COVER Index of Applications in Examples and Exercises
7. CHAPTER 1 The Role of Statistics in Engineering
1. 1-1 The Engineering Method and Statistical Thinking
2. 1-2 Collecting Engineering Data
3. 1-3 Mechanistic and Empirical Models
4. 1-4 Probability and Probability Models
8. CHAPTER 2 Probability
1. 2-1 Sample Spaces and Events
2. 2-2 Interpretations and Axioms of Probability
4. 2-4 Conditional Probability
5. 2-5 Multiplication and Total Probability Rules
6. 2-6 Independence
7. 2-7 Bayes’ Theorem
8. 2-8 Random Variables
9. CHAPTER 3 Discrete Random Variables and Probability Distributions
10. CHAPTER 4 Continuous Random Variables and Probability Distributions
11. CHAPTER 5 Joint Probability Distributions
1. 5-1 Two or More Random Variables
2. 5-2 Covariance and Correlation
3. 5-3 Common Joint Distributions
4. 5-4 Linear Functions of Random Variables
5. 5-5 General Functions of Random Variables
12. CHAPTER 6 Descriptive Statistics
13. CHAPTER 7 Sampling Distributions and Point Estimation of Parameters
1. 7-1 Point Estimation
2. 7-2 Sampling Distributions and the Central Limit Theorem
3. 7-3 General Concepts of Point Estimation
4. 7-4 Methods of Point Estimation
14. CHAPTER 8 Statistical Intervals for a Single Sample
1. 8-1 Confidence Interval on the Mean of a Normal Distribution, Variance Known
2. 8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Unknown
3. 8-3 Confidence Interval on the Variance and Standard Deviation of a Normal Distribution
4. 8-4 Large-Sample Confidence Interval for a Population Proportion
5. 8-5 Guidelines for Constructing Confidence Intervals
6. 8-6 Tolerance and Prediction Intervals
15. CHAPTER 9 Tests of Hypotheses for a Single Sample
1. 9-1 Hypothesis Testing
2. 9-2 Tests on the Mean of a Normal Distribution, Variance Known
3. 9-3 Tests on the Mean of a Normal Distribution, Variance Unknown
4. 9-4 Tests on the Variance and Standard Deviation of a Normal Distribution
5. 9-5 Tests on a Population Proportion
6. 9-6 Summary Table of Inference Procedures for a Single Sample
7. 9-7 Testing for Goodness of Fit
8. 9-8 Contingency Table Tests
9. 9-9 Nonparametric Procedures
16. CHAPTER 10 Statistical Inference for Two Samples
1. 10-1 Inference on the Difference in Means of Two Normal Distributions, Variances Known
2. 10-2 Inference on the Difference in Means of Two Normal Distributions, Variances Unknown
3. 10-3 A Nonparametric Test for the Difference in Two Means
4. 10-4 Paired t-Test
5. 10-5 Inference on the Variances of Two Normal Distributions
6. 10-6 Inference on Two Population Proportions
7. 10-7 Summary Table and Roadmap for Inference Procedures for Two Samples
17. CHAPTER 11 Simple Linear Regression and Correlation
1. 11-1 Empirical Models
2. 11-2 Simple Linear Regression
3. 11-3 Properties of the Least Squares Estimators
4. 11-4 Hypothesis Tests in Simple Linear Regression
5. 11-5 Confidence Intervals
6. 11-6 Prediction of New Observations
7. 11-7 Adequacy of the Regression Model
8. 11-8 Correlation
9. 11-9 Regression on Transformed Variables
10. 11-10 Logistic Regression
18. CHAPTER 12 Multiple Linear Regression
1. 12-1 Multiple Linear Regression Model
2. 12-2 Hypothesis Tests in Multiple Linear Regression
3. 12-3 Confidence Intervals in Multiple Linear Regression
4. 12-4 Prediction of New Observations
6. 12-6 Aspects of Multiple Regression Modeling
19. CHAPTER 13 Design and Analysis of Single-Factor Experiments: The Analysis of Variance
1. 13-1 Designing Engineering Experiments
2. 13-2 Completely Randomized Single-Factor Experiment
3. 13-3 The Random-Effects Model
4. 13-4 Randomized Complete Block Design
20. CHAPTER 14 Design of Experiments with Several Factors
1. 14-1 Introduction
2. 14-2 Factorial Experiments
3. 14-3 Two-Factor Factorial Experiments
4. 14-4 General Factorial Experiments
5. 14-5 2k Factorial Designs
6. 14-6 Blocking and Confounding in the 2k Design
7. 14-7 Fractional Replication of the 2k Design
8. 14-8 Response Surface Methods and Designs
21. CHAPTER 15 Statistical Quality Control
1. 15-1 Quality Improvement and Statistics
2. 15-2 Introduction to Control Charts
3. 15-3 X and R or S Control Charts
4. 15-4 Control Charts for Individual Measurements
5. 15-5 Process Capability
6. 15-6 Attribute Control Charts
7. 15-7 Control Chart Performance
8. 15-8 Time-Weighted Charts
9. 15-9 Other SPC Problem-Solving Tools
10. 15-10 Implementing SPC
22. APPENDICES
1. APPENDIX A: Statistical Tables and Charts
2. APPENDIX B: Answers to Selected Exercises
3. APPENDIX C: Bibliography
23. GLOSSARY
24. INDEX
25. INDEX OF APPLICATIONS IN EXAMPLES AND EXERCISES, CONTINUED

This chapter generalizes the simple linear regression to a situation where there is more than one predictor or regressor variable. This situation occurs frequently in science and engineering; for example, in Chapter 1 we provided data on the pull strength of a wire bond on a semiconductor package and illustrated its relationship to the wire length and the die height. Understanding the relationship between strength and the other two variables may provide important insight to the engineer when the package is designed, or to the manufacturing personnel who assemble the die into the package. We used a multiple linear regression model to relate strength to wire length and die height. There are many examples of such relationships: The life of a cutting tool is related to the cutting speed and the tool angle; patient satisfaction in a hospital is related to patient age, type of procedure performed, and length of stay; and the fuel economy of a vehicle is related to the type of vehicle (car versus truck), engine displacement, horsepower, type of transmission, and vehicle weight. Multiple regression models give insight into the relationships between these variables that can have important practical implications.

This chapter shows how to fit multiple linear regression models, perform the statistical tests and confidence procedures that are analogous to those for simple linear regression, ...