Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP

Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining—including big data, classification, machine learning, and visualization—is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book’s companion website. Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems.   Features two new chapters—one on Data Mining and another on Cluster Analysis Now contains R exhibits including code, graphical display, and some results MINITAB and JMP have been updated to their latest versions Emphasizes the p-value approach and includes related practical interpretations Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts Supplemented with an Instructor's-only solutions manual on a book’s companion website  Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences. 

Table of Contents:
Preface xvii Acknowledgments xxi About The Companion Site xxiii 1 Introduction 1 1.1 Designed Experiment 2 1.1.1 Motivation for the Study 2 1.1.2 Investigation 3 1.1.3 Changing Criteria 3 1.1.4 A Summary of the Various Phases of the Investigation 5 1.2 A Survey 6 1.3 An Observational Study 6 1.4 A Set of Historical Data 7 1.5 A Brief Description of What is Covered in this Book 7 Part I Fundamentals of Probability and Statistics 2 Describing Data Graphically and Numerically 13 2.1 Getting Started with Statistics 14 2.1.1 What is Statistics? 14 2.1.2 Population and Sample in a Statistical Study 14 2.2 Classification of Various Types of Data 18 2.2.1 Nominal Data 18 2.2.2 Ordinal Data 19 2.2.3 Interval Data 19 2.2.4 Ratio Data 19 2.3 Frequency Distribution Tables for Qualitative and Quantitative Data 20 2.3.1 Qualitative Data 21 2.3.2 Quantitative Data 24 2.4 Graphical Description of Qualitative and Quantitative Data 30 2.4.1 Dot Plot 30 2.4.2 Pie Chart 31 2.4.3 Bar Chart 33 2.4.4 Histograms 37 2.4.5 Line Graph 44 2.4.6 Stem-and-Leaf Plot 45 2.5 Numerical Measures of Quantitative Data 50 2.5.1 Measures of Centrality 51 2.5.2 Measures of Dispersion 56 2.6 Numerical Measures of Grouped Data 67 2.6.1 Mean of a Grouped Data 67 2.6.2 Median of a Grouped Data 68 2.6.3 Mode of a Grouped Data 69 2.6.4 Variance of a Grouped Data 69 2.7 Measures of Relative Position 70 2.7.1 Percentiles 71 2.7.2 Quartiles 72 2.7.3 Interquartile Range (IQR) 72 2.7.4 Coefficient of Variation 73 2.8 Box-Whisker Plot 75 2.8.1 Construction of a Box Plot 75 2.8.2 How to Use the Box Plot 76 2.9 Measures of Association 80 2.10 Case Studies 84 2.10.1 About St. Luke’s Hospital 85 2.11 Using JMP 86 Review Practice Problems 87 3 Elements of Probability 97 3.1 Introduction 97 3.2 Random Experiments, Sample Spaces, and Events 98 3.2.1 Random Experiments and Sample Spaces 98 3.2.2 Events 99 3.3 Concepts of Probability 103 3.4 Techniques of Counting Sample Points 108 3.4.1 Tree Diagram 108 3.4.2 Permutations 110 3.4.3 Combinations 110 3.4.4 Arrangements of n Objects Involving Several Kinds of Objects 111 3.5 Conditional Probability 113 3.6 Bayes’s Theorem 116 3.7 Introducing Random Variables 120 Review Practice Problems 122 4 Discrete Random Variables and Some Important Discrete Probability Distributions 128 4.1 Graphical Descriptions of Discrete Distributions 129 4.2 Mean and Variance of a Discrete Random Variable 130 4.2.1 Expected Value of Discrete Random Variables and Their Functions 130 4.2.2 The Moment-Generating Function-Expected Value of a Special Function of X 133 4.3 The Discrete Uniform Distribution 136 4.4 The Hypergeometric Distribution 137 4.5 The Bernoulli Distribution 141 4.6 The Binomial Distribution 142 4.7 The Multinomial Distribution 146 4.8 The Poisson Distribution 147 4.8.1 Definition and Properties of the Poisson Distribution 147 4.8.2 Poisson Process 148 4.8.3 Poisson Distribution as a Limiting Form of the Binomial 148 4.9 The Negative Binomial Distribution 153 4.10 Some Derivations and Proofs (Optional) 156 4.11 A Case Study 156 4.12 Using JMP 157 Review Practice Problems 157 5 Continuous Random Variables and Some Important Continuous Probability Distributions 164 5.1 Continuous Random Variables 165 5.2 Mean and Variance of Continuous Random Variables 168 5.2.1 Expected Value of Continuous Random Variables and Their Functions 168 5.2.2 The Moment-Generating Function and Expected Value of a Special Function of X 171 5.3 Chebyshev’s Inequality 173 5.4 The Uniform Distribution 175 5.4.1 Definition and Properties 175 5.4.2 Mean and Standard Deviation of the Uniform Distribution 178 5.5 The Normal Distribution 180 5.5.1 Definition and Properties 180 5.5.2 The Standard Normal Distribution 182 5.5.3 The Moment-Generating Function of the Normal Distribution 187 5.6 Distribution of Linear Combination of Independent Normal Variables 189 5.7 Approximation of the Binomial and Poisson Distributions by the Normal Distribution 193 5.7.1 Approximation of the Binomial Distribution by the Normal Distribution 193 5.7.2 Approximation of the Poisson Distribution by the Normal Distribution 196 5.8 A Test of Normality 196 5.9 Probability Models Commonly used in Reliability Theory 201 5.9.1 The Lognormal Distribution 202 5.9.2 The Exponential Distribution 206 5.9.3 The Gamma Distribution 211 5.9.4 The Weibull Distribution 214 5.10 A Case Study 218 5.11 Using JMP 219 Review Practice Problems 220 6 Distribution of Functions Of Random Variables 228 6.1 Introduction 229 6.2 Distribution Functions of Two Random Variables 229 6.2.1 Case of Two Discrete Random Variables 229 6.2.2 Case of Two Continuous Random Variables 232 6.2.3 The Mean Value and Variance of Functions of Two Random Variables 233 6.2.4 Conditional Distributions 235 6.2.5 Correlation between Two Random Variables 238 6.2.6 Bivariate Normal Distribution 241 6.3 Extension to Several Random Variables 244 6.4 The Moment-Generating Function Revisited 245 Review Practice Problems 249 7 Sampling Distributions 253 7.1 Random Sampling 253 7.1.1 Random Sampling from an Infinite Population 254 7.1.2 Random Sampling from a Finite Population 256 7.2 The Sampling Distribution of the Sample Mean 258 7.2.1 Normal Sampled Population 258 7.2.2 Nonnormal Sampled Population 258 7.2.3 The Central Limit Theorem 259 7.3 Sampling from a Normal Population 264 7.3.1 The Chi-Square Distribution 264 7.3.2 The Student t-Distribution 271 7.3.3 Snedecor’s F-Distribution 276 7.4 Order Statistics 279 7.4.1 Distribution of the Largest Element in a Sample 280 7.4.2 Distribution of the Smallest Element in a Sample 281 7.4.3 Distribution of the Median of a Sample and of the kth Order Statistic 282 7.4.4 Other Uses of Order Statistics 284 7.5 Using JMP 286 Review Practice Problems 286 8 Estimation of Population Parameters 289 8.1 Introduction 290 8.2 Point Estimators for the Population Mean and Variance 290 8.2.1 Properties of Point Estimators 292 8.2.2 Methods of Finding Point Estimators 295 8.3 Interval Estimators for the Mean μ of a Normal Population 301 8.3.1 σ2 Known 301 8.3.2 σ2 Unknown 304 8.3.3 Sample Size is Large 306 8.4 Interval Estimators for The Difference of Means of Two Normal Populations 313 8.4.1 Variances are Known 313 8.4.2 Variances are Unknown 314 8.5 Interval Estimators for the Variance of a Normal Population 322 8.6 Interval Estimator for the Ratio of Variances of Two Normal Populations 327 8.7 Point and Interval Estimators for the Parameters of Binomial Populations 331 8.7.1 One Binomial Population 331 8.7.2 Two Binomial Populations 334 8.8 Determination of Sample Size 338 8.8.1 One Population Mean 339 8.8.2 Difference of Two Population Means 339 8.8.3 One Population Proportion 340 8.8.4 Difference of Two Population Proportions 341 8.9 Some Supplemental Information 343 8.10 A Case Study 343 8.11 Using JMP 343 Review Practice Problems 344 9 Hypothesis Testing 352 9.1 Introduction 353 9.2 Basic Concepts of Testing a Statistical Hypothesis 353 9.2.1 Hypothesis Formulation 353 9.2.2 Risk Assessment 355 9.3 Tests Concerning the Mean of a Normal Population Having Known Variance 358 9.3.1 Case of a One-Tail (Left-Sided) Test 358 9.3.2 Case of a One-Tail (Right-Sided) Test 362 9.3.3 Case of a Two-Tail Test 363 9.4 Tests Concerning the Mean of a Normal Population Having Unknown Variance 372 9.4.1 Case of a Left-Tail Test 372 9.4.2 Case of a Right-Tail Test 373 9.4.3 The Two-Tail Case 374 9.5 Large Sample Theory 378 9.6 Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances 380 9.6.1 The Left-Tail Test 380 9.6.2 The Right-Tail Test 381 9.6.3 The Two-Tail Test 383 9.7 Tests Concerning the Difference of Means of Two Populations Having Normal Distributions with Unknown Variances 388 9.7.1 Two Population Variances are Equal 388 9.7.2 Two Population Variances are Unequal 392 9.7.3 The Paired t-Test 395 9.8 Testing Population Proportions 401 9.8.1 Test Concerning One Population Proportion 401 9.8.2 Test Concerning the Difference Between Two Population Proportions 405 9.9 Tests Concerning the Variance of a Normal Population 410 9.10 Tests Concerning the Ratio of Variances of Two Normal Populations 414 9.11 Testing of Statistical Hypotheses using Confidence Intervals 418 9.12 Sequential Tests of Hypotheses 422 9.12.1 A One-Tail Sequential Testing Procedure 422 9.12.2 A Two-Tail Sequential Testing Procedure 427 9.13 Case Studies 430 9.14 Using JMP 431 Review Practice Problems 431 Part II Statistics in Actions 10 Elements of Reliability Theory 445 10.1 The Reliability Function 446 10.1.1 The Hazard Rate Function 446 10.1.2 Employing the Hazard Function 455 10.2 Estimation: Exponential Distribution 457 10.3 Hypothesis Testing: Exponential Distribution 465 10.4 Estimation: Weibull Distribution 467 10.5 Case Studies 472 10.6 Using JMP 474 Review Practice Problems 474 11 On Data Mining 476 11.1 Introduction 476 11.2 What is Data Mining? 477 11.2.1 Big Data 477 11.3 Data Reduction 478 11.4 Data Visualization 481 11.5 Data Preparation 490 11.5.1 Missing Data 490 11.5.2 Outlier Detection and Remedial Measures 491 11.6 Classification 492 11.6.1 Evaluating a Classification Model 493 11.7 Decision Trees 499 11.7.1 Classification and Regression Trees (CART) 500 11.7.2 Further Reading 511 11.8 Case Studies 511 11.9 Using JMP 512 Review Practice Problems 512 12 Cluster Analysis 518 12.1 Introduction 518 12.2 Similarity Measures 519 12.2.1 Common Similarity Coefficients 524 12.3 Hierarchical Clustering Methods 525 12.3.1 Single Linkage 526 12.3.2 Complete Linkage 531 12.3.3 Average Linkage 534 12.3.4 Ward’s Hierarchical Clustering 536 12.4 Nonhierarchical Clustering Methods 538 12.4.1 K-Means Method 538 12.5 Density-Based Clustering 544 12.6 Model-Based Clustering 547 12.7 A Case Study 552 12.8 Using JMP 553 Review Practice Problems 553 13 Analysis of Categorical Data 558 13.1 Introduction 558 13.2 The Chi-Square Goodness-of-Fit Test 559 13.3 Contingency Tables 568 13.3.1 The 2 × 2 Case with Known Parameters 568 13.3.2 The 2 × 2 Case with Unknown Parameters 570 13.3.3 The r × s Contingency Table 572 13.4 Chi-Square Test for Homogeneity 577 13.5 Comments on the Distribution of the Lack-of-Fit Statistics 581 13.6 Case Studies 583 13.7 Using JMP 584 Review Practice Problems 585 14 Nonparametric Tests 591 14.1 Introduction 591 14.2 The Sign Test 592 14.2.1 One-Sample Test 592 14.2.2 The Wilcoxon Signed-Rank Test 595 14.2.3 Two-Sample Test 598 14.3 Mann–Whitney (Wilcoxon) W Test for Two Samples 604 14.4 Runs Test 608 14.4.1 Runs above and below the Median 608 14.4.2 The Wald–Wolfowitz Run Test 611 14.5 Spearman Rank Correlation 614 14.6 Using JMP 618 Review Practice Problems 618 15 Simple Linear Regression Analysis 622 15.1 Introduction 623 15.2 Fitting the Simple Linear Regression Model 624 15.2.1 Simple Linear Regression Model 624 15.2.2 Fitting a Straight Line by Least Squares 627 15.2.3 Sampling Distribution of the Estimators of Regression Coefficients 631 15.3 Unbiased Estimator of σ2 637 15.4 Further Inferences Concerning Regression Coefficients (β0, β1), E(Y ), and Y 639 15.4.1 Confidence Interval for β1 with Confidence Coefficient (1 − α) 639 15.4.2 Confidence Interval for β0 with Confidence Coefficient (1 − α) 640 15.4.3 Confidence Interval for E(Y |X) with Confidence Coefficient (1 − α) 642 15.4.4 Prediction Interval for a Future Observation Y with Confidence Coefficient (1 − α) 645 15.5 Tests of Hypotheses for β0 and β1 652 15.5.1 Test of Hypotheses for β1 652 15.5.2 Test of Hypotheses for β0 652 15.6 Analysis of Variance Approach to Simple Linear Regression Analysis 659 15.7 Residual Analysis 665 15.8 Transformations 674 15.9 Inference About ρ 681 15.10A Case Study 683 15.11 Using JMP 684 Review Practice Problems 684 16 Multiple Linear Regression Analysis 693 16.1 Introduction 694 16.2 Multiple Linear Regression Models 694 16.3 Estimation of Regression Coefficients 699 16.3.1 Estimation of Regression Coefficients Using Matrix Notation 701 16.3.2 Properties of the Least-Squares Estimators 703 16.3.3 The Analysis of Variance Table 704 16.3.4 More Inferences about Regression Coefficients 706 16.4 Multiple Linear Regression Model Using Quantitative and Qualitative Predictor Variables 714 16.4.1 Single Qualitative Variable with Two Categories 714 16.4.2 Single Qualitative Variable with Three or More Categories 716 16.5 Standardized Regression Coefficients 726 16.5.1 Multicollinearity 728 16.5.2 Consequences of Multicollinearity 729 16.6 Building Regression Type Prediction Models 730 16.6.1 First Variable to Enter into the Model 730 16.7 Residual Analysis and Certain Criteria for Model Selection 734 16.7.1 Residual Analysis 734 16.7.2 Certain Criteria for Model Selection 735 16.8 Logistic Regression 740 16.9 Case Studies 745 16.10 Using JMP 748 Review Practice Problems 748 17 Analysis of Variance 757 17.1 Introduction 758 17.2 The Design Models 758 17.2.1 Estimable Parameters 758 17.2.2 Estimable Functions 760 17.3 One-Way Experimental Layouts 761 17.3.1 The Model and Its Analysis 761 17.3.2 Confidence Intervals for Treatment Means 767 17.3.3 Multiple Comparisons 773 17.3.4 Determination of Sample Size 780 17.3.5 The Kruskal–Wallis Test for One-Way Layouts (Nonparametric Method) 781 17.4 Randomized Complete Block (RCB) Designs 785 17.4.1 The Friedman Fr-Test for Randomized Complete Block Design (Nonparametric Method) 792 17.4.2 Experiments with One Missing Observation in an RCB-Design Experiment 794 17.4.3 Experiments with Several Missing Observations in an RCB-Design Experiment 795 17.5 Two-Way Experimental Layouts 798 17.5.1 Two-Way Experimental Layouts with One Observation per Cell 800 17.5.2 Two-Way Experimental Layouts with r > 1 Observations per Cell 801 17.5.3 Blocking in Two-Way Experimental Layouts 810 17.5.4 Extending Two-Way Experimental Designs to n-Way Experimental Layouts 811 17.6 Latin Square Designs 813 17.7 Random-Effects and Mixed-Effects Models 820 17.7.1 Random-Effects Model 820 17.7.2 Mixed-Effects Model 822 17.7.3 Nested (Hierarchical) Designs 824 17.8 A Case Study 831 17.9 Using JMP 832 Review Practice Problems 832 18 The 2k Factorial Designs 847 18.1 Introduction 848 18.2 The Factorial Designs 848 18.3 The 2k Factorial Designs 850 18.4 Unreplicated 2k Factorial Designs 859 18.5 Blocking in the 2k Factorial Design 867 18.5.1 Confounding in the 2k Factorial Design 867 18.5.2 Yates’s Algorithm for the 2k Factorial Designs 875 18.6 The 2k Fractional Factorial Designs 877 18.6.1 One-half Replicate of a 2k Factorial Design 877 18.6.2 One-quarter Replicate of a 2k Factorial Design 882 18.7 Case Studies 887 18.8 Using JMP 889 Review Practice Problems 889 19 Response Surfaces 897 19.1 Introduction 897 19.1.1 Basic Concepts of Response Surface Methodology 898 19.2 First-Order Designs 903 19.3 Second-Order Designs 917 19.3.1 Central Composite Designs (CCDs) 918 19.3.2 Some Other First-Order and Second-Order Designs 928 19.4 Determination of Optimum or Near-Optimum Point 936 19.4.1 The Method of Steepest Ascent 937 19.4.2 Analysis of a Fitted Second-Order Response Surface 941 19.5 Anova Table for a Second-Order Model 946 19.6 Case Studies 948 19.7 Using JMP 950 Review Practice Problems 950 20 Statistical Quality Control—Phase I Control Charts 958 21 Statistical Quality Control—Phase II Control Charts 960 Appendices 961 Appendix A Statistical Tables 962 Appendix B Answers to Selected Problems 969 Appendix C Bibliography 992 Index 1003

About the Author :
BHISHAM C. GUPTA, PHD, is Professor Emeritus of Statistics in the Department of Mathematics and Statistics at the University of Southern Maine, and the co-author of Statistics and Probability with Applications for Engineers and Scientists. IRWIN GUTTMAN, PHD, is Professor Emeritus of Statistics in the Department of Mathematics at the State University of New York at Buffalo and Department of Statistics at the University of Toronto, Canada. He is the co-author of Statistics and Probability with Applications for Engineers and Scientists. KALANKA P. JAYALATH, PHD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Houston.