{"product_id":"nonparametric-statistical-methods-isbn-9780470387375","title":"Nonparametric Statistical Methods","description":"\u003cp\u003ePraise for the Second Edition\u003cbr\u003e “This book should be an essential part of the personal library of every practicing statistician.”\u003ci\u003e—\u003c\/i\u003eTechnometrics\u003c\/p\u003e \u003cp\u003e \u003cbr\u003e Thoroughly revised and updated, the new edition of \u003ci\u003eNonparametric Statistical Methods\u003c\/i\u003e includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.\u003c\/p\u003e \u003cp\u003eWritten by leading statisticians, \u003ci\u003eNonparametric Statistical Methods, Third Edition\u003c\/i\u003e provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the \u003ci\u003eThird Edition\u003c\/i\u003e features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition\u003c\/li\u003e \u003cli\u003eNew chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics\u003c\/li\u003e \u003cli\u003eProblems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science\u003c\/li\u003e \u003c\/ul\u003e \u003ci\u003eNonparametric Statistical Methods, Third Edition\u003c\/i\u003e is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.   \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. Advantages of Nonparametric Methods 1\u003c\/p\u003e \u003cp\u003e1.2. The Distribution-Free Property 2\u003c\/p\u003e \u003cp\u003e1.3. Some Real-World Applications 3\u003c\/p\u003e \u003cp\u003e1.4. Format and Organization 6\u003c\/p\u003e \u003cp\u003e1.5. Computing with R 8\u003c\/p\u003e \u003cp\u003e1.6. Historical Background 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. The Dichotomous Data Problem 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 11\u003c\/p\u003e \u003cp\u003e2.1. A Binomial Test 11\u003c\/p\u003e \u003cp\u003e2.2. An Estimator for the Probability of Success 22\u003c\/p\u003e \u003cp\u003e2.3. A Confidence Interval for the Probability of Success (Wilson) 24\u003c\/p\u003e \u003cp\u003e2.4. Bayes Estimators for the Probability of Success 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. The One-Sample Location Problem 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 39\u003c\/p\u003e \u003cp\u003ePaired Replicates Analyses by Way of Signed Ranks 39\u003c\/p\u003e \u003cp\u003e3.1. A Distribution-Free Signed Rank Test (Wilcoxon) 40\u003c\/p\u003e \u003cp\u003e3.2. An Estimator Associated with Wilcoxon’s Signed Rank Statistic (Hodges–Lehmann) 56\u003c\/p\u003e \u003cp\u003e3.3. A Distribution-Free Confidence Interval Based on Wilcoxon’s Signed Rank Test (Tukey) 59\u003c\/p\u003e \u003cp\u003ePaired Replicates Analyses by Way of Signs 63\u003c\/p\u003e \u003cp\u003e3.4. A Distribution-Free Sign Test (Fisher) 63\u003c\/p\u003e \u003cp\u003e3.5. An Estimator Associated with the Sign Statistic (Hodges–Lehmann) 76\u003c\/p\u003e \u003cp\u003e3.6. A Distribution-Free Confidence Interval Based on the Sign Test (Thompson, Savur) 80\u003c\/p\u003e \u003cp\u003eOne-Sample Data 84\u003c\/p\u003e \u003cp\u003e3.7. Procedures Based on the Signed Rank Statistic 84\u003c\/p\u003e \u003cp\u003e3.8. Procedures Based on the Sign Statistic 90\u003c\/p\u003e \u003cp\u003e3.9. An Asymptotically Distribution-Free Test of Symmetry (Randles–Fligner–Policello–Wolfe, Davis–Quade) 94\u003c\/p\u003e \u003cp\u003eBivariate Data 102\u003c\/p\u003e \u003cp\u003e3.10. A Distribution-Free Test for Bivariate Symmetry (Hollander) 102\u003c\/p\u003e \u003cp\u003e3.11. Efficiencies of Paired Replicates and One-Sample Location Procedures 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. The Two-Sample Location Problem 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 115\u003c\/p\u003e \u003cp\u003e4.1. A Distribution-Free Rank Sum Test (Wilcoxon, Mann and Whitney) 115\u003c\/p\u003e \u003cp\u003e4.2. An Estimator Associated with Wilcoxon’s Rank Sum Statistic (Hodges–Lehmann) 136\u003c\/p\u003e \u003cp\u003e4.3. A Distribution-Free Confidence Interval Based on Wilcoxon’s Rank Sum Test (Moses) 142\u003c\/p\u003e \u003cp\u003e4.4. A Robust Rank Test for the Behrens–Fisher Problem (Fligner–Policello) 145\u003c\/p\u003e \u003cp\u003e4.5. Efficiencies of Two-Sample Location Procedures 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. The Two-Sample Dispersion Problem and Other Two-Sample Problems 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 151\u003c\/p\u003e \u003cp\u003e5.1. A Distribution-Free Rank Test for Dispersion–Medians Equal (Ansari–Bradley) 152\u003c\/p\u003e \u003cp\u003e5.2. An Asymptotically Distribution-Free Test for Dispersion Based on the Jackknife–Medians Not Necessarily Equal (Miller) 169\u003c\/p\u003e \u003cp\u003e5.3. A Distribution-Free Rank Test for Either Location or Dispersion (Lepage) 181\u003c\/p\u003e \u003cp\u003e5.4. A Distribution-Free Test for General Differences in Two Populations (Kolmogorov–Smirnov) 190\u003c\/p\u003e \u003cp\u003e5.5. Efficiencies of Two-Sample Dispersion and Broad Alternatives Procedures 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. The One-Way Layout 202\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 202\u003c\/p\u003e \u003cp\u003e6.1. A Distribution-Free Test for General Alternatives (Kruskal–Wallis) 204\u003c\/p\u003e \u003cp\u003e6.2. A Distribution-Free Test for Ordered Alternatives (Jonckheere–Terpstra) 215\u003c\/p\u003e \u003cp\u003e6.3. Distribution-Free Tests for Umbrella Alternatives (Mack–Wolfe) 225\u003c\/p\u003e \u003cp\u003e6.3A. A Distribution-Free Test for Umbrella Alternatives, Peak Known (Mack–Wolfe) 226\u003c\/p\u003e \u003cp\u003e6.3B. A Distribution-Free Test for Umbrella Alternatives, Peak Unknown (Mack–Wolfe) 241\u003c\/p\u003e \u003cp\u003e6.4. A Distribution-Free Test for Treatments Versus a Control (Fligner–Wolfe) 249\u003c\/p\u003e \u003cp\u003eRationale For Multiple Comparison Procedures 255\u003c\/p\u003e \u003cp\u003e6.5. Distribution-Free Two-Sided All-Treatments Multiple Comparisons Based on Pairwise Rankings–General Configuration (Dwass, Steel, and Critchlow–Fligner) 256\u003c\/p\u003e \u003cp\u003e6.6. Distribution-Free One-Sided All-Treatments Multiple Comparisons Based on Pairwise Rankings-Ordered Treatment Effects (Hayter–Stone) 265\u003c\/p\u003e \u003cp\u003e6.7. Distribution-Free One-Sided Treatments-Versus-Control Multiple Comparisons Based on Joint Rankings (Nemenyi, Damico–Wolfe) 271\u003c\/p\u003e \u003cp\u003e6.8. Contrast Estimation Based on Hodges–Lehmann Two-Sample Estimators (Spjøtvoll) 278\u003c\/p\u003e \u003cp\u003e6.9. Simultaneous Confidence Intervals for All Simple Contrasts (Critchlow–Fligner) 282\u003c\/p\u003e \u003cp\u003e6.10. Efficiencies of One-Way Layout Procedures 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. The Two-Way Layout 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 289\u003c\/p\u003e \u003cp\u003e7.1. A Distribution-Free Test for General Alternatives in a Randomized Complete Block Design (Friedman, Kendall-Babington Smith) 292\u003c\/p\u003e \u003cp\u003e7.2. A Distribution-Free Test for Ordered Alternatives in a Randomized Complete Block Design (Page) 304\u003c\/p\u003e \u003cp\u003eRationale for Multiple Comparison Procedures 315\u003c\/p\u003e \u003cp\u003e7.3. Distribution-Free Two-Sided All-Treatments Multiple Comparisons Based on Friedman Rank Sums–General Configuration (Wilcoxon, Nemenyi, McDonald-Thompson) 316\u003c\/p\u003e \u003cp\u003e7.4. Distribution-Free One-Sided Treatments Versus Control Multiple Comparisons Based on Friedman Rank Sums (Nemenyi, Wilcoxon-Wilcox, Miller) 322\u003c\/p\u003e \u003cp\u003e7.5. Contrast Estimation Based on One-Sample Median Estimators (Doksum) 328\u003c\/p\u003e \u003cp\u003eIncomplete Block Data–Two-Way Layout with Zero or One Observation Per Treatment–Block Combination 331\u003c\/p\u003e \u003cp\u003e7.6. A Distribution-Free Test for General Alternatives in a Randomized Balanced Incomplete Block Design (BIBD) (Durbin–Skillings–Mack) 332\u003c\/p\u003e \u003cp\u003e7.7. Asymptotically Distribution-Free Two-Sided All-Treatments Multiple Comparisons for Balanced Incomplete Block Designs (Skillings–Mack) 341\u003c\/p\u003e \u003cp\u003e7.8. A Distribution-Free Test for General Alternatives for Data From an Arbitrary Incomplete Block Design (Skillings–Mack) 343\u003c\/p\u003e \u003cp\u003eReplications–Two-Way Layout with at Least One Observation for Every Treatment–Block Combination 354\u003c\/p\u003e \u003cp\u003e7.9. A Distribution-Free Test for General Alternatives in a Randomized Block Design with an Equal Number c(\u0026gt;1) of Replications Per Treatment–Block Combination (Mack–Skillings) 354\u003c\/p\u003e \u003cp\u003e7.10. Asymptotically Distribution-Free Two-Sided All-Treatments Multiple Comparisons for a Two-Way Layout with an Equal Number of Replications in Each Treatment–Block Combination (Mack–Skillings) 367\u003c\/p\u003e \u003cp\u003eAnalyses Associated with Signed Ranks 370\u003c\/p\u003e \u003cp\u003e7.11. A Test Based on Wilcoxon Signed Ranks for General Alternatives in a Randomized Complete Block Design (Doksum) 370\u003c\/p\u003e \u003cp\u003e7.12. A Test Based on Wilcoxon Signed Ranks for Ordered Alternatives in a Randomized Complete Block Design (Hollander) 376\u003c\/p\u003e \u003cp\u003e7.13. Approximate Two-Sided All-Treatments Multiple Comparisons Based on Signed Ranks (Nemenyi) 379\u003c\/p\u003e \u003cp\u003e7.14. Approximate One-Sided Treatments-Versus-Control Multiple Comparisons Based on Signed Ranks (Hollander) 382\u003c\/p\u003e \u003cp\u003e7.15. Contrast Estimation Based on the One-Sample Hodges–Lehmann Estimators (Lehmann) 386\u003c\/p\u003e \u003cp\u003e7.16. Efficiencies of Two-Way Layout Procedures 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. The Independence Problem 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 393\u003c\/p\u003e \u003cp\u003e8.1. A Distribution-Free Test for Independence Based on Signs (Kendall) 393\u003c\/p\u003e \u003cp\u003e8.2. An Estimator Associated with the Kendall Statistic (Kendall) 413\u003c\/p\u003e \u003cp\u003e8.3. An Asymptotically Distribution-Free Confidence Interval Based on the Kendall Statistic (Samara-Randles, Fligner–Rust, Noether) 415\u003c\/p\u003e \u003cp\u003e8.4. An Asymptotically Distribution-Free Confidence Interval Based on Efron’s Bootstrap 420\u003c\/p\u003e \u003cp\u003e8.5. A Distribution-Free Test for Independence Based on Ranks (Spearman) 427\u003c\/p\u003e \u003cp\u003e8.6. A Distribution-Free Test for Independence Against Broad Alternatives (Hoeffding) 442\u003c\/p\u003e \u003cp\u003e8.7. Efficiencies of Independence Procedures 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Regression Problems 451\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 451\u003c\/p\u003e \u003cp\u003eOne Regression Line 452\u003c\/p\u003e \u003cp\u003e9.1. A Distribution-Free Test for the Slope of the Regression Line (Theil) 452\u003c\/p\u003e \u003cp\u003e9.2. A Slope Estimator Associated with the Theil Statistic (Theil) 458\u003c\/p\u003e \u003cp\u003e9.3. A Distribution-Free Confidence Interval Associated with the Theil Test (Theil) 460\u003c\/p\u003e \u003cp\u003e9.4. An Intercept Estimator Associated with the Theil Statistic and Use of the Estimated Linear Relationship for Prediction (Hettmansperger–McKean–Sheather) 463\u003c\/p\u003e \u003cp\u003ek(≥2) Regression Lines 466\u003c\/p\u003e \u003cp\u003e9.5. An Asymptotically Distribution-Free Test for the Parallelism of Several Regression Lines (Sen, Adichie) 466\u003c\/p\u003e \u003cp\u003eGeneral Multiple Linear Regression 475\u003c\/p\u003e \u003cp\u003e9.6. Asymptotically Distribution-Free Rank-Based Tests for General Multiple Linear Regression (Jaeckel, Hettmansperger–McKean) 475\u003c\/p\u003e \u003cp\u003eNonparametric Regression Analysis 490\u003c\/p\u003e \u003cp\u003e9.7. An Introduction to Non-Rank-Based Approaches to Nonparametric Regression Analysis 490\u003c\/p\u003e \u003cp\u003e9.8. Efficiencies of Regression Procedures 494\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Comparing Two Success Probabilities 495\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 495\u003c\/p\u003e \u003cp\u003e10.1. Approximate Tests and Confidence Intervals for the Difference between Two Success Probabilities (Pearson) 496\u003c\/p\u003e \u003cp\u003e10.2. An Exact Test for the Difference between Two Success Probabilities (Fisher) 511\u003c\/p\u003e \u003cp\u003e10.3. Inference for the Odds Ratio (Fisher, Cornfield) 515\u003c\/p\u003e \u003cp\u003e10.4. Inference for k Strata of 2 × 2 Tables (Mantel and Haenszel) 522\u003c\/p\u003e \u003cp\u003e10.5. Efficiencies 534\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Life Distributions and Survival Analysis 535\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 535\u003c\/p\u003e \u003cp\u003e11.1. A Test of Exponentiality Versus IFR Alternatives (Epstein) 536\u003c\/p\u003e \u003cp\u003e11.2. A Test of Exponentiality Versus NBU Alternatives (Hollander–Proschan) 545\u003c\/p\u003e \u003cp\u003e11.3. A Test of Exponentiality Versus DMRL Alternatives (Hollander–Proschan) 555\u003c\/p\u003e \u003cp\u003e11.4. A Test of Exponentiality Versus a Trend Change in Mean Residual Life (Guess–Hollander–Proschan) 563\u003c\/p\u003e \u003cp\u003e11.5. A Confidence Band for the Distribution Function (Kolmogorov) 568\u003c\/p\u003e \u003cp\u003e11.6. An Estimator of the Distribution Function When the Data are Censored (Kaplan–Meier) 578\u003c\/p\u003e \u003cp\u003e11.7. A Two-Sample Test for Censored Data (Mantel) 594\u003c\/p\u003e \u003cp\u003e11.8. Efficiencies 605\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Density Estimation 609\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 609\u003c\/p\u003e \u003cp\u003e12.1. Density Functions and Histograms 609\u003c\/p\u003e \u003cp\u003e12.2. Kernel Density Estimation 617\u003c\/p\u003e \u003cp\u003e12.3. Bandwidth Selection 624\u003c\/p\u003e \u003cp\u003e12.4. Other Methods 628\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Wavelets 629\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 629\u003c\/p\u003e \u003cp\u003e13.1. Wavelet Representation of a Function 630\u003c\/p\u003e \u003cp\u003e13.2. Wavelet Thresholding 644\u003c\/p\u003e \u003cp\u003e13.3. Other Uses of Wavelets in Statistics 655\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Smoothing 656\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 656\u003c\/p\u003e \u003cp\u003e14.1. Local Averaging (Friedman) 657\u003c\/p\u003e \u003cp\u003e14.2. Local Regression (Cleveland) 662\u003c\/p\u003e \u003cp\u003e14.3. Kernel Smoothing 667\u003c\/p\u003e \u003cp\u003e14.4. Other Methods of Smoothing 675\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Ranked Set Sampling 676\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 676\u003c\/p\u003e \u003cp\u003e15.1. Rationale and Historical Development 676\u003c\/p\u003e \u003cp\u003e15.2. Collecting a Ranked Set Sample 677\u003c\/p\u003e \u003cp\u003e15.3. Ranked Set Sampling Estimation of a Population Mean 685\u003c\/p\u003e \u003cp\u003e15.4. Ranked Set Sample Analogs of the Mann–Whitney–Wilcoxon Two-Sample Procedures (Bohn–Wolfe) 717\u003c\/p\u003e \u003cp\u003e15.5. Other Important Issues for Ranked Set Sampling 737\u003c\/p\u003e \u003cp\u003e15.6. Extensions and Related Approaches 742\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. An Introduction to Bayesian Nonparametric Statistics via the Dirichlet Process 744\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 744\u003c\/p\u003e \u003cp\u003e16.1. Ferguson’s Dirichlet Process 745\u003c\/p\u003e \u003cp\u003e16.2. A Bayes Estimator of the Distribution Function (Ferguson) 749\u003c\/p\u003e \u003cp\u003e16.3. Rank Order Estimation (Campbell and Hollander) 752\u003c\/p\u003e \u003cp\u003e16.4. A Bayes Estimator of the Distribution When the Data are Right-Censored (Susarla and Van Ryzin) 755\u003c\/p\u003e \u003cp\u003e16.5. Other Bayesian Approaches 759\u003c\/p\u003e \u003cp\u003eBibliography 763\u003c\/p\u003e \u003cp\u003eR Program Index 791\u003c\/p\u003e \u003cp\u003eAuthor Index 799\u003c\/p\u003e \u003cp\u003eSubject Index 809\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eMYLES HOLLANDER\u003c\/b\u003e is Robert O. Lawton Distinguished Professor of Statistics and Professor Emeritus at the Florida State University in Tallahassee. He served as editor of the Theory and Methods Section of the \u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, 1993–96, and he received the Gottfried E. Noether Senior Scholar Award from the American Statistical Association in 2003.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDOUGLAS A. WOLFE\u003c\/b\u003e is Professor and Chair Emeritus in the Department of Statistics at Ohio State University in Columbus. He is a two-time recipient of the Ohio State University Alumni Distinguished Teaching Award, in 1973–74 and 1988–89.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eERIC CHICKEN\u003c\/b\u003e is Associate Professor at the Florida State University in Tallahassee. He is active in modern nonparametric statistics research fields, including functional analysis, sequential methods, and complex system applications.\u003c\/p\u003e  \u003cp\u003ePraise for the \u003ci\u003eSecond Edition\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"This book should be an essential part of the personal library of every practicing statistician.\"\u003cbr\u003e —\u003ci\u003eTechnometrics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThoroughly revised and updated, the new edition of \u003ci\u003eNonparametric Statistical Methods\u003c\/i\u003e includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.\u003c\/p\u003e \u003cp\u003eWritten by leading statisticians, \u003ci\u003eNonparametric Statistical Methods, Third Edition\u003c\/i\u003e provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the \u003ci\u003eThird Edition\u003c\/i\u003e features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition\u003c\/li\u003e \u003cli\u003eNew chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics\u003c\/li\u003e \u003cli\u003eProblems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eNonparametric Statistical Methods, Third Edition\u003c\/i\u003e is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989696757989,"sku":"NP9780470387375","price":142.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470387375.jpg?v=1761785145","url":"https:\/\/k12savings.com\/es\/products\/nonparametric-statistical-methods-isbn-9780470387375","provider":"K12savings","version":"1.0","type":"link"}