Statistics and probability with applications for engineers and scientists / Bhisham C. Gupta and Irwin Guttman.

Includes bibliographical references (pages 863-866) and index.

Preface
Chapter 1. Introduction
1 1.1 Designed Experiment
1.1.1 Motivation for the Study
1.1.2 Investigation
1.1.3 Changing Criteria
1.1.4 A Summary of the Various Phases of the Investigation
1.2 A Survey
1.3 An Observational Study
1.4 A Set of Historical Data
1.5 A Brief Description of What is Covered in This Book

PART I
Chapter 2. Describing Data Graphically and Numerically
2.1 Getting Started with Statistics
2.1.1 What Is Statistics?
2.1.2 Population and Sample in a Statistical Study
2.2 Classification of Various Types of Data
2.2.1 Nominal Data
2.2.2 Ordinal Data
2.2.3 Interval Data
2.2.4 Ratio Data
2.3 Frequency Distribution Tables for Qualitative and Quantitative Data
2.3.1 Qualitative Data
2.3.2 Quantitative Data
2.4 Graphical Description of Qualitative and Quantitative Data
2.4.1 Dot Plot
2.4.2 Pie Chart
2.4.3 Bar Chart
2.4.4 Histograms
2.4.5 Line Graph
2.4.6 Stem-and-Leaf Plot
2.5 Numerical Measures of Quantitative Data
2.5.1 Measures of Centrality
2.5.2 Measures of Dispersion
2.6 Numerical Measures of Grouped Data
2.6.1 Mean of a Grouped Data
2.6.2 Median of a Grouped Data
2.6.3 Mode of a Grouped Data
2.6.4 Variance of a Grouped Data
2.7 Measures of Relative Position
2.7.1 Percentiles
2.7.2 Quartiles
2.7.3 Interquartile Range
2.7.4 Coefficient of Variation
2.8 Box-Whisker Plot
2.8.1 Construction of a Box Plot
2.8.2 How to Use the Box Plot
2.9 Measures of Association
2.10 Case Studies
2.11 Using JMP1
Review Practice Problems

Chapter 3. Elements of Probability
3.1 Introduction
3.2 Random Experiments, Sample Spaces, and Events
3.2.1 Random Experiments and Sample Spaces
3.2.2 Events
3.3 Concepts of Probability
3.4 Techniques of Counting Sample Points
3.4.1 Tree Diagram
3.4.2 Permutations
3.4.3 Combinations
3.4.4 Arrangements of n Objects Involving Several Kinds of Objects
3.5 Conditional Probability
3.6 Bayes's Theorem
3.7 Introducing Random Variables
Review Practice Problems

Chapter 4. Discrete Random Variables and Some Important Discrete Probability Distributions
4.1 Graphical Descriptions of Discrete Distributions
4.2 Mean and Variance of a Discrete Random Variable
4.2.1 Expected Value of Discrete Random Variables and Their Functions
4.2.2 The Moment-Generating Function--Expected Value of a Special Function of X
4.3 The Discrete Uniform Distribution
4.4 The Hypergeometric Distribution
4.5 The Bernoulli Distribution
4.6 The Binomial Distribution
4.7 The Multinomial Distribution
4.8 The Poisson Distribution
4.8.1 Definition and Properties of the Poisson Distribution
4.8.2 Poisson Process
4.8.3 Poisson Distribution as a Limiting Form of the Binomial
4.9 The Negative Binomial Distribution
4.10 Some Derivations and Proofs (Optional)
4.11 A Case Study
4.12 Using JMP 135 Review Practice Problems

Chapter 5. Continuous Random Variables and Some Important Continuous Probability Distributions
5.1 Continuous Random Variables
5.2 Mean and Variance of Continuous Random Variables
5.2.1 Expected Value of Continuous Random Variables and Their Function
5.2.2 The Moment-Generating Function--Expected Value of a Special Function of X
5.3 Chebychev's Inequality
5.4 The Uniform Distribution
5.4.1 Definition and Properties
5.4.2 Mean and Standard Deviation of the Uniform Distribution
5.5 The Normal Distribution
5.5.1 Definition and Properties
5.5.2 The Standard Normal Distribution
5.5.3 The Moment-Generating Function of the Normal Distribution
5.6 Distribution of Linear Combination of Independent Normal Variables
5.7 Approximation of the Binomial and Poisson Distribution by the Normal Distribution
5.7.1 Approximation of the Binomial Distribution by the Normal Distribution
5.7.2 Approximation of the Poisson Distribution by the Normal Distribution
5.8 A Test of Normality
5.9 Probability Models Commonly Used in Reliability Theory
5.9.1 The Lognormal Distribution
5.9.2 The Exponential Distribution
5.9.3 The Gamma Distribution
5.9.4 The Weibull Distribution
5.10 A Case Study
5.11 Using JMP 192 Review Practice Problems

Chapter 6. Distribution of Functions of Random Variables
6.1 Introduction
6.2 Distribution Functions of Two Random Variables
6.2.1 Case of Two Discrete Random Variables
6.2.2 Case of Two Continuous Random Variables
6.2.3 The Mean Value and Variance of Functions of Two Random Variables
6.2.4 Conditional Distributions
6.2.5 Correlation between Two Random Variables
6.2.6 Bivariate Normal Distribution
6.3 Extension to Several Random Variables
6.4 The Moment-Generating Function Revisited
Review Practice Problems

Chapter 7. Sampling Distributions
7.1 Random Sampling
7.1.1 Random Sampling from an Infinite Population
7.1.2 Random Sampling from a Finite Population
7.2 The Sampling Distribution of the Mean
7.2.1 Normal Sampled Population
7.2.2 Nonnormal Sampled Population
7.2.3 The Central Limit Theorem
7.3 Sampling from a Normal Population
7.3.1 The Chi-Square Distribution
7.3.2 The Student t-Distribution
7.3.3 Snedecor's F-Distribution
7.4 Order Statistics
7.5 Using JMP
Review Practice Problems

Chapter 8. Estimation of Population Parameters
8.1 Introduction
8.2 Point Estimators for the Population Mean and Variance
8.2.1 Properties of Point Estimators
8.2.2 Methods of Finding Point Estimators
8.3 Interval Estimators for the Mean m of a Normal Population
8.3.1 s2 Known
8.3.2 s2 Unknown
8.3.3 Sample Size Is Large
8.4 Interval Estimators for the Difference of Means of Two Normal Populations
8.4.1 Variances Are Known
8.4.2 Variances Are Unknown
8.5 Interval Estimators for the Variance of a Normal Population
8.6 Interval Estimator for the Ratio of Variances of Two Normal Populations
8.7 Point and Interval Estimators for the Parameters of Binomial Populations
8.7.1 One Binomial Population
8.7.2 Two Binomial Populations
8.8 Determination of Sample Size
8.8.1 One Population Mean
8.8.2 Difference of Two Population Means
8.8.3 One Population Proportion
8.8.4 Difference of Two Population Proportions
8.9 Some Supplemental Information
8.10 A Case Study
8.11 Using JMP 299 Review Practice Problems

Chapter 9. Hypothesis Testing
9.1 Introduction
9.2 Basic Concepts of Testing a Statistical Hypothesis
9.2.1 Hypothesis Formulation
9.2.2 Risk Assessment
9.3 Tests Concerning the Mean of a Normal Population Having Known Variance
9.3.1 Case of a One-Tail (Left-Sided) Test
9.3.2 Case of a One-Tail (Right-Sided) Test
9.3.3 Case of a Two-Tail Test
9.4 Tests Concerning the Mean of a Normal Population Having Unknown Variance
9.4.1 Case of a Left-Tail Test
9.4.2 Case of a Right-Tail Test
9.4.3 The Two-Tail Case
9.5 Large Sample Theory
9.6 Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances
9.6.1 The Left-Tail Test
9.6.2 The Right-Tail Test
9.6.3 The Two-Tail Test
9.7 Tests Concerning the Difference of Means of Two Populations Having Normal Distributions with Unknown Variances
9.7.1 Two Population Variances Are Equal
9.7.2 Two Population Variances Are Unequal
9.7.3 The Paired t-Test
9.8 Testing Population Proportions
9.8.1 Test Concerning One Population Proportion
9.8.2 Test Concerning the Difference between Two Population Proportions
9.9 Tests Concerning the Variance of a Normal Population
9.10 Tests Concerning the Ratio of Variances of Two Normal Populations
9.11 Testing of Statistical Hypotheses Using Confidence Intervals
9.12 Sequential Tests of Hypotheses
9.12.1 A One-Tail Sequential Testing Procedure 3
9.12.2 A Two-Tail Sequential Testing Procedure
9.13 Case Studies
9.14 Using JMP
Review Practice Problems

PART II Chapter 10. Elements of Reliability Theory
10.1 The Reliability Function
10.1.1 The Hazard Rate Function
10.1.2 Employing the Hazard Function
10.2 Estimation: Exponential Distribution
10.3 Hypothesis Testing: Exponential Distribution
10.4 Estimation: Weibull Distribution
10.5 Case Studies
10.6 Using JMP
Review Practice Problems

Chapter 11. Statistical Quality Control--Phase I Control Charts
11.1 Basic Concepts of Quality and Its Benefits
11.2 What a Process Is and Some Valuable Tools
11.2.1 Check Sheet
11.2.2 Pareto Chart
11.2.3 Cause-and-Effect (Fishbone or Ishikawa) Diagram
11.2.4 Defect Concentration Diagram
11.3 Common and Assignable Causes
11.3.1 Process Evaluation
11.3.2 Action on the Process
11.3.3 Action on Output
11.3.4 Variation
11.4 Control Charts
11.4.1 Preparation for Use of Control Charts
11.4.2 Benefits of a Control Chart
11.4.3 Control Limits Versus Specification Limits
11.5 Control Charts for Variables
11.5.1 Shewhart X and R Control Charts
11.5.2 Shewhart X and R Control Charts When Process Mean m and Process Standard Deviation s Are Known
11.5.3 Shewhart X and S Control Charts
11.6 Control Charts for Attributes
11.6.1 The p Chart: Control Chart for the Fraction of Nonconforming Units
11.6.2 The p Chart: Control Chart for the Fraction Nonconforming with Variable Sample Sizes
11.6.3 The np Control Chart: Control Chart for the Number of Nonconforming Units
11.6.4 The c Control Chart
11.6.5 The u Control Chart
11.7 Process Capability
11.8 Case Studies
11.9 Using JMP
Review Practice Problems

Chapter 12. Statistical Quality Control--Phase II Control Charts
12.1 Introduction
12.2 Basic Concepts of CUSUM Control Chart
12.3 Designing a CUSUM Control Chart
12.3.1 Two-Sided CUSUM Control Chart Using a Numerical Procedure
12.3.2 The Fast Initial Response (FIR) Feature for CUSUM Control Chart
12.3.3 The Combined Shewhart--CUSUM Control Chart
12.3.4 The CUSUM Control Chart for Controlling Process Variability
12.4 The Moving Average (MA) Control Chart
12.5 The Exponentially Weighted Moving Average (EWMA) Control Chart
12.6 Case Studies
12.7 Using JMP
Review Practice Problems

Chapter 13. Analysis of Categorical Data
13.1 Introduction
13.2 The Chi-Square Goodness-of-Fit Test
13.3 Contingency Tables
13.3.1 The 2 2 Case Parameters Known
13.3.2 The 2 2 Case with Unknown Parameters
13.3.3 The r s Contingency Table
13.4 Chi-Square Test for Homogeneity
13.5 Comments on the Distribution of the Lack-of-Fit Statistics
13.6 Case Studies
Review Practice Problems

Chapter 14. Nonparametric Tests
14.1 Introduction
14.2 The Sign Test
14.2.1 One-Sample Test
14.2.2 The Wilcoxon Signed-Rank Test
14.2.3 Two-Sample Test
14.3 Mann--Whitney (Wilcoxon) W Test for Two Samples
14.4 Runs Test
14.4.1 Runs Above and Below the Median
14.4.2 The Wald--Wolfowitz Run Test
14.5 Spearman Rank Correlation
14.6 Using JMP
Review Practice Problems

Chapter 15. Simple Linear Regression Analysis
15.1 Introduction
15.2 Fitting the Simple Linear Regression Model
15.2.1 Simple Linear Regression Model
15.2.2 Fitting a Straight Line by Least Squares
15.2.3 Sampling Distribution of the Estimators of Regression Coefficients
15.3 Unbiased Estimator of s2
15.4 Further Inferences Concerning Regression Coefficients (b0, b1), E(Y), and Y
15.4.1 Confidence Interval for b1 with Confidence Coefficient (1 a)
15.4.2 Confidence Interval for b0 with Confidence Coefficient (1a)
15.4.3 Confidence Interval for E(YjX) with Confidence Coefficient (1 a)
15.4.4 Prediction Interval for a Future Observation Y with Confidence Coefficient (1 a)
15.5 Tests of Hypotheses for b0 and b1
15.5.1 Test of Hypotheses for b1
15.5.2 Test of Hypotheses for b0
15.6 Analysis of Variance Approach to Simple Linear Regression Analysis
15.7 Residual Analysis
15.8 Transformations
15.9 Inference About r
15.10 A Case Study
15.11 Using JMP
Review Practice Problems

Chapter 16. Multiple Linear Regression Analysis
16.1 Introduction
16.2 Multiple Linear Regression Models
16.3 Estimation of Regression Coefficients
16.3.1 Estimation of Regression Coefficients Using Matrix Notation
16.3.2 Properties of the Least-Squares Estimators
16.3.3 The Analysis of Variance Table
16.3.4 More Inferences about Regression Coefficients
16.4 Multiple Linear Regression Model Using Quantitative and Qualitative Predictor Variables
16.4.1 Single Qualitative Variable with Two Categories
16.4.2 Single Qualitative Variable with Three or More Categories
16.5 Standardized Regression Coefficients
16.5.1 Multicollinearity
16.5.2 Consequences of Multicollinearity
16.6 Building Regression Type Prediction Models
16.6.1 First Variable to Enter into the Model
16.7 Residual Analysis and Certain Criteria for Model selection
16.7.1 Residual Analysis
16.7.2 Certain Criteria for Model Selection
16.8 Logistic Regression
16.9 Case Studies
16.10 Using JMP
Review Practice Problems

Chapter 17. Analysis of Variance
17.1 Introduction
17.2 The Design Models
17.2.1 Estimable Parameters
17.2.2 Estimable Functions
17.3 One-Way Experimental Layouts
17.3.1 The Model and Its Analysis
17.3.2 Confidence Intervals for Treatment Means
17.3.3 Multiple Comparisons
17.3.4 Determination of Sample Size
17.3.5 The Kruskal--Wallis Test for One-Way Layouts (Nonparametric Method)
17.4 Randomized Complete Block Designs
17.4.1 The Friedman Fr-Test for Randomized Complete Block Design (Nonparametric Method)
17.4.2 Experiments with One Missing Observation in an RCB-Design Experiment
17.4.3 Experiments with Several Missing Observations in an RCB-Design Experiment
17.5 Two-Way Experimental Layouts
17.5.1 Two-Way Experimental Layouts with One Observation per Cell
17.5.2 Two-Way Experimental Layouts with r>1 Observations per Cell
17.5.3 Blocking in Two-Way Experimental Layouts
17.5.4 Extending Two-Way Experimental Designs to n-Way Experimental Layouts
17.6 Latin Square Designs
17.7 Random-Effects and Mixed-Effects Models
17.7.1 Random-Effects Model
17.7.2 Mixed-Effects Model
17.7.3 Nested (Hierarchical) Designs
17.8 A Case Study
17.9 Using JMP
Review Practice Problems

Chapter 18. The 2k Factorial Designs
18.1 Introduction
18.2 The Factorial Designs
18.3 The 2k Factorial Design
18.4 Unreplicated 2k Factorial Designs
18.5 Blocking in the 2k Factorial Design
18.5.1 Confounding in the 2k Factorial Design
18.5.2 Yates's Algorithm for the 2k Factorial Designs
18.6 The 2k Fractional Factorial Designs
18.6.1 One-half Replicate of a 2k Factorial Design
18.6.2 One-quarter Replicate of a 2k Factorial Design
18.7 Case Studies
18.8 Using JMP
Review Practice Problems

Chapter 19. Response Surfaces This chapter is not included in text, but is available for download via the book's website: www.wiley.com/go/statsforengineers Appendices

Appendix A. Statistical Tables
Appendix B. Answers to Selected Problems
Appendix C. Bibliography
Index

"This book covers applied statistics and probability for undergraduate students in engineering and the natural sciences"--