TY  - BOOK
AU  - Gupta,Bhisham C.
AU  - Guttman,Irwin
TI  - Statistics and probability with applications for engineers and scientists / 
SN  - 9781118464045 (hardback)
U1  - 000SA.01 23
PY  - 2013///
CY  - New Jersey
PB  - John Wiley
KW  - Probabilities
KW  - Mathematical statistics
KW  - MATHEMATICS / Probability & Statistics / General
KW  - Engineering
KW  - statistical methods
KW  - Science
N1  - 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
N2  - "This book covers applied statistics and probability for undergraduate students in engineering and the natural sciences"--
ER  -