LEADER 01425nas 2200493 a 450 001 996209274903316 005 20210520022340.0 011 $a1879-0607 035 $a(OCoLC)38927849 035 $a(CKB)954925609895 035 $a(CONSER) 2004233031 035 $a(DE-599)ZDB1497841-6 035 $a(EXLCZ)99954925609895 100 $a19980409a19979999 sy a 101 0 $aeng 135 $aurmnu||||| 200 10$aEvolution and human behavior 210 $aNew York, NY $cElsevier Science Inc.$d1997- 300 $aTitle from contents screen (ScienceDirect, viewed Jan. 6, 2004). 300 $aRefereed/Peer-reviewed 311 $a1090-5138 531 $aEVOL HUM BEHAV 531 $aEVOL HUM BE 531 $aEVOLUTION & HUMAN BEHAVIOR 531 0 $aEvol. hum. behav. 531 1 $aEvol. hum. behav. 531 $aEVOL. HUM. BEHAV 606 $aHuman behavior$vPeriodicals 606 $aHuman evolution$vPeriodicals 606 $aPsychology$vPeriodicals 606 $aBehavior 606 $aBiological Evolution 608 $aPeriodical. 615 0$aHuman behavior 615 0$aHuman evolution 615 0$aPsychology 615 2$aBehavior. 615 2$aBiological Evolution. 676 $a155.7/05 712 02$aHuman Behavior and Evolution Society. 906 $aJOURNAL 912 $a996209274903316 996 $aEvolution and human behavior$91892256 997 $aUNISA LEADER 06319oam 22012614 450 001 9910957403503321 005 20250426110713.0 010 $a9786612841965 010 $a9781462309375 010 $a1462309372 010 $a9781452726878 010 $a1452726876 010 $a9781282841963 010 $a1282841963 010 $a9781451871036 010 $a1451871031 035 $a(CKB)3170000000055142 035 $a(EBL)1608058 035 $a(SSID)ssj0000943999 035 $a(PQKBManifestationID)11544307 035 $a(PQKBTitleCode)TC0000943999 035 $a(PQKBWorkID)10982473 035 $a(PQKB)10083407 035 $a(OCoLC)460974893 035 $a(MiAaPQ)EBC1608058 035 $a(IMF)WPIEE2008245 035 $a(IMF)WPIEA2008245 035 $aWPIEA2008245 035 $a(EXLCZ)993170000000055142 100 $a20020129d2008 uf 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aContingent Liabilities : $eIssues and Practice /$fAliona Cebotari 205 $a1st ed. 210 1$aWashington, D.C. :$cInternational Monetary Fund,$d2008. 215 $a1 online resource (62 p.) 225 1 $aIMF Working Papers 225 0$aIMF working paper ;$vWP/08/245 300 $aDescription based upon print version of record. 311 08$a9781451915563 311 08$a145191556X 320 $aIncludes bibliographical references. 327 $aContents; I. Introduction; II. Background; III. Mitigating Risks Associated with Contingent Liabilities; A. Frameworks for Dealing with Risks from Contingent Liabilities; B. When to take on Contingent Liabilities?; Boxes; 1. Market Failure and Terrorism Insurance; 2. When Are Guarantees Preferable to Other Forms of Support?; C. Strategies to Transfer Risk or Costs Related to Contingent Liabilities; Figures; 1. Typical Infrastructure PPP Project Risks and Hypothetical Allocation; 3. Estimating the Expected Cost and Market Value of Guarantees 327 $aD. Other Safeguards against Risks Related to Contingent LiabilitiesIV. Managing Retained Risk from Contingent Liabilities; A. Instruments for Managing Low Impact Liabilities; B. Instruments for Managing High Impact Liabilities; Tables; 1. Contingency Funds to Meet Calls on Contingent Liabilities: Selected Examples; V. Disclosing Contingent Liabilities; 2. IPSAS: When to Recognize and Disclose Contingent Liabilities; 3. Accounting/Statistical Standards and Transparency Initiatives: What to Disclose; 4. Legislative Requirements to Disclose Fiscal Risks: Selected Country Examples 327 $aVI. Institutional Arrangements for Managing Contingent Liability Risks5. Disclosing the Magnitude of Contingent Liabilities: Selected Country Examples; VII. Conclusion; 4. Institutional Arrangements for Managing PPP Risks; A1. Accounting Standards and Standard Setters; Annexes; I. Accounting/Statistical Standards and Contingent Liabilities; A1. Summary of the Main Requirements for Recognition and Disclosures of Contingent Liabilities; II. Measuring the Value of Contingent Liabilities; A1. The Swedish Debt Office Simulation Model; References 330 3 $aContingent liabilities have gained prominence in the analysis of public finance. Indeed, history is full of episodes in which the financial position of the public sector is substantially altered-or its true nature uncovered-as a result of government bailouts of financial or nonfinancial entities, in both the private and the public sector. The paper discusses theoretical and practical issues raised by contingent liabilities, including the rationale for taking them on, how to safeguard against the fiscal risks associated with them, how to account and budget for them, and how to disclose them. Country experiences are used to illustrate ways these issues are addressed in practice and challenges faced. The paper also points to good practices related to the mitigation, management and disclosure of risks from contingent liabilities. 410 0$aIMF Working Papers; Working Paper ;$vNo. 2008/245 606 $aContingent liabilities (Accounting) 606 $aLiabilities (Accounting) 606 $aFinance, Public$xAccounting 606 $aRisk management 606 $aAccounting$2imf 606 $aActuarial Studies$2imf 606 $aBudget planning and preparation$2imf 606 $aBudget Systems$2imf 606 $aBudget$2imf 606 $aBudgeting & financial management$2imf 606 $aBudgeting$2imf 606 $aContingent liabilities$2imf 606 $aFinance, Public$2imf 606 $aFinancial reporting, financial statements$2imf 606 $aFinancial statements$2imf 606 $aFiscal policy$2imf 606 $aFiscal risks$2imf 606 $aInsurance & actuarial studies$2imf 606 $aInsurance Companies$2imf 606 $aInsurance$2imf 606 $aNational Budget$2imf 606 $aPublic Administration$2imf 606 $aPublic finance & taxation$2imf 606 $aPublic Finance$2imf 606 $aPublic Sector Accounting and Audits$2imf 607 $aUnited States$2imf 615 0$aContingent liabilities (Accounting) 615 0$aLiabilities (Accounting) 615 0$aFinance, Public$xAccounting. 615 0$aRisk management. 615 7$aAccounting 615 7$aActuarial Studies 615 7$aBudget planning and preparation 615 7$aBudget Systems 615 7$aBudget 615 7$aBudgeting & financial management 615 7$aBudgeting 615 7$aContingent liabilities 615 7$aFinance, Public 615 7$aFinancial reporting, financial statements 615 7$aFinancial statements 615 7$aFiscal policy 615 7$aFiscal risks 615 7$aInsurance & actuarial studies 615 7$aInsurance Companies 615 7$aInsurance 615 7$aNational Budget 615 7$aPublic Administration 615 7$aPublic finance & taxation 615 7$aPublic Finance 615 7$aPublic Sector Accounting and Audits 676 $a336.343351 700 $aCebotari$b Aliona$01808816 801 0$bDcWaIMF 906 $aBOOK 912 $a9910957403503321 996 $aContingent Liabilities$94371514 997 $aUNINA LEADER 11883oam 22005293 450 001 9910971929603321 005 20251116135225.0 010 $a9781119351351$b(electronic bk.) 010 $z9781119143017 035 $a(MiAaPQ)EBC4773832 035 $a(Au-PeEL)EBL4773832 035 $a(CaPaEBR)ebr11320918 035 $a(CaONFJC)MIL984187 035 $a(OCoLC)967589474 035 $a(MiAaPQ)EBC7104516 035 $a(CKB)17683811900041 035 $a(BIP)56594245 035 $a(BIP)51761013 035 $a(EXLCZ)9917683811900041 100 $a20220831d2017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences 210 1$aNew York :$cJohn Wiley & Sons, Incorporated,$d2017. 210 4$d©2017. 215 $a1 online resource (395 pages) 311 08$aPrint version: Johnson, David L. Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences New York : John Wiley & Sons, Incorporated,c2017 9781119143017 327 $aCover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- About the Author -- About the Companion Website -- Chapter 1 Some Basic Concepts -- 1.1 Introduction -- 1.2 Physical versus Statistical Sampling -- 1.3 Representative Measures -- 1.4 Strategies for Representative Sampling -- 1.5 Measurement Precision -- 1.6 Probability Concepts -- 1.6.1 The Relative Frequency Approach -- 1.6.2 The Classical Approach - Probability Based on Deductive Reasoning -- 1.6.3 Subjective Probability -- 1.6.4 Complement of a Probability -- 1.6.5 Mutually Exclusive Events -- 1.6.6 Independent Events -- 1.6.7 Events that Are Not Mutually Exclusive -- 1.6.8 Marginal and Conditional Probabilities -- 1.6.9 Testing for Independence -- 1.7 Permutations and Combinations -- 1.7.1 Permutations for Sampling without Replacement -- 1.7.2 Permutations for Sampling with Replacement -- 1.7.3 Combinations -- 1.8 Introduction to Frequency Distributions -- 1.8.1 The Binomial Distribution -- 1.8.2 The Normal Distribution -- 1.8.3 The Chi-Square Distribution -- 1.9 Confidence Intervals and Hypothesis Testing -- 1.10 Summary -- 1.11 Addendum: Glossary of Some Useful Excel Functions -- 1.12 Exercises -- References -- Chapter 2 Descriptive Statistics and Methods of Presenting Data -- 2.1 Introduction -- 2.2 Quantitative Descriptors of Data and Data Distributions -- 2.3 Displaying Data with Frequency Tables -- 2.4 Displaying Data with Histograms and Frequency Polygons -- 2.5 Displaying Data Frequency Distributions with Cumulative Probability Plots -- 2.6 Displaying Data with NED and Q - Q Plots -- 2.7 Displaying Data with Box-and-Whisker Plots -- 2.8 Data Transformations to Achieve Normality -- 2.9 Identifying Outliers -- 2.10 What to Do with Censored Values? -- 2.11 Summary -- 2.12 Exercises -- References -- Chapter 3 Analysis of Frequency Data. 327 $a3.1 Introduction -- 3.2 Tests for Association and Goodness-of-Fit -- 3.2.1 r × c Contingency Tables and the Chi-Square Test -- 3.2.2 Fisher's Exact Test -- 3.3 Binomial Proportions -- 3.4 Rare Events and the Poisson Distribution -- 3.4.1 Poisson Probabilities -- 3.4.2 Confidence Interval on a Poisson Count -- 3.4.3 Testing for Fit with the Poisson Distribution -- 3.4.4 Comparing Two Poisson Rates -- 3.4.5 Type I Error, Type II Error, and Power -- 3.4.6 Power and Sample Size in Comparing Two Poisson Rates -- 3.5 Summary -- 3.6 Exercises -- References -- Chapter 4 Comparing Two Conditions -- 4.1 Introduction -- 4.2 Standard Error of the Mean -- 4.3 Confidence Interval on a Mean -- 4.4 The t-Distribution -- 4.5 Parametric One-Sample Test - Student's t-Test -- 4.6 Two-Tailed versus One-Tailed Hypothesis Tests -- 4.7 Confidence Interval on a Variance -- 4.8 Other Applications of the Confidence Interval Concept in IH/EHS Work -- 4.8.1 OSHA Compliance Determinations -- 4.8.2 Laboratory Analyses - LOB, LOD, and LOQ -- 4.9 Precision, Power, and Sample Size for One Mean -- 4.9.1 Sample Size Required to Estimate a Mean with a Stated Precision -- 4.9.2 Sample Size Required to Detect a Specified Difference in Student's t-Test -- 4.10 Iterative Solutions Using the Excel Goal Seek Utility -- 4.11 Parametric Two-Sample Tests -- 4.11.1 Confidence Interval for a Difference in Means: The Two-Sample t-Test -- 4.11.2 Two-Sample t-Test When Variances Are Equal -- 4.11.3 Verifying the Assumptions of the Two-Sample t-Test -- 4.11.4 Two-Sample t-Test with Unequal Variances - Welch's Test -- 4.11.5 Paired Sample t-Test -- 4.11.6 Precision, Power, and Sample Size for Comparing Two Means -- 4.12 Testing for Difference in Two Binomial Proportions -- 4.12.1 Testing a Binomial Proportion for Difference from a Known Value -- 4.12.2 Testing Two Binomial Proportions for Difference. 327 $a4.13 Nonparametric Two-Sample Tests -- 4.13.1 Mann - Whitney U Test -- 4.13.2 Wilcoxon Matched Pairs Test -- 4.13.3 McNemar and Binomial Tests for Paired Nominal Data -- 4.14 Summary -- 4.15 Exercises -- References -- Chapter 5 Characterizing the Upper Tail of the Exposure Distribution -- 5.1 Introduction -- 5.2 Upper Tolerance Limits -- 5.3 Exceedance Fractions -- 5.4 Distribution Free Tolerance Limits -- 5.5 Summary -- 5.6 Exercises -- References -- Chapter 6 One-Way Analysis of Variance -- 6.1 Introduction -- 6.2 Parametric One-Way ANOVA -- 6.2.1 How the Parametric ANOVA Works - Sums of Squares and the F-Test -- 6.2.2 Post hoc Multiple Pairwise Comparisons in Parametric ANOVA -- 6.2.3 Checking the ANOVA Model Assumptions - NED Plots and Variance Tests -- 6.3 Nonparametric Analysis of Variance -- 6.3.1 Kruskal - Wallis Nonparametric One-Way ANOVA -- 6.3.2 Post hoc Multiple Pairwise Comparisons in Nonparametric ANOVA -- 6.4 ANOVA Disconnects -- 6.5 Summary -- 6.6 Exercises -- References -- Chapter 7 Two-Way Analysis of Variance -- 7.1 Introduction -- 7.2 Parametric Two-Way ANOVA -- 7.2.1 Two-Way ANOVA without Interaction -- 7.2.2 Checking for Homogeneity of Variance -- 7.2.3 Multiple Pairwise Comparisons When There Is No Interaction Term -- 7.2.4 Two-Way ANOVA with Interaction -- 7.2.5 Multiple Pairwise Comparisons with Interaction -- 7.2.6 Two-Way ANOVA without Replication -- 7.2.7 Repeated-Measures ANOVA -- 7.2.8 Two-Way ANOVA with Unequal Sample Sizes -- 7.3 Nonparametric Two-Way ANOVA -- 7.3.1 Rank Tests -- 7.3.2 Repeated-Measures Nonparametric ANOVA - Friedman's Test -- 7.4 More Powerful Non-ANOVA Approaches: Linear Modeling -- 7.5 Summary -- 7.6 Exercises -- References -- Chapter 8 Correlation Analysis -- 8.1 Introduction -- 8.2 Simple Parametric Correlation Analysis -- 8.2.1 Testing the Correlation Coefficient for Significance. 327 $a8.2.2 Confidence Limits on the Correlation Coefficient -- 8.2.3 Power in Simple Correlation Analysis -- 8.2.4 Comparing Two Correlation Coefficients for Difference -- 8.2.5 Comparing More Than Two Correlation Coefficients for Difference -- 8.2.6 Multiple Pairwise Comparisons of Correlation Coefficients -- 8.3 Simple Nonparametric Correlation Analysis -- 8.3.1 Spearman Rank Correlation Coefficient -- 8.3.2 Testing Spearman's Rank Correlation Coefficient for Statistical Significance -- 8.3.3 Correction to Spearman's Rank Correlation Coefficient When There Are Tied Ranks -- 8.4 Multiple Correlation Analysis -- 8.4.1 Parametric Multiple Correlation -- 8.4.2 Nonparametric Multiple Correlation: Kendall's Coefficient of Concordance -- 8.5 Determining Causation -- 8.6 Summary -- 8.7 Exercises -- References -- Chapter 9 Regression Analysis -- 9.1 Introduction -- 9.2 Linear Regression -- 9.2.1 Simple Linear Regression -- 9.2.2 Nonconstant Variance - Transformations and Weighted Least Squares Regression -- 9.2.3 Multiple Linear Regression -- 9.2.4 Using Regression for Factorial ANOVA with Unequal Sample Sizes -- 9.2.5 Multiple Correlation Analysis Using Multiple Regression -- 9.2.6 Polynomial Regression -- 9.2.7 Interpreting Linear Regression Results -- 9.2.8 Linear Regression versus ANOVA -- 9.3 Logistic Regression -- 9.3.1 Odds and Odds Ratios -- 9.3.2 The Logit Transformation -- 9.3.3 The Likelihood Function -- 9.3.4 Logistic Regression in Excel -- 9.3.5 Likelihood Ratio Test for Significance of MLE Coefficients -- 9.3.6 Odds Ratio Confidence Limits in Multivariate Models -- 9.4 Poisson Regression -- 9.4.1 Poisson Regression Model -- 9.4.2 Poisson Regression in Excel -- 9.5 Regression with Excel Add-ons -- 9.6 Summary -- 9.7 Exercises -- References -- Chapter 10 Analysis of Covariance -- 10.1 Introduction -- 10.2 The Simple ANCOVA Model and Its Assumptions. 327 $a10.2.1 Required Regressions -- 10.2.2 Checking the ANCOVA Assumptions -- 10.2.3 Testing and Estimating the Treatment Effects -- 10.3 The Two-Factor Covariance Model -- 10.4 Summary -- 10.5 Exercises -- Reference -- Chapter 11 Experimental Design -- 11.1 Introduction -- 11.2 Randomization -- 11.3 Simple Randomized Experiments -- 11.4 Experimental Designs Blocking on Categorical Factors -- 11.5 Randomized Full Factorial Experimental Design -- 11.6 Randomized Full Factorial Design with Blocking -- 11.7 Split Plot Experimental Designs -- 11.8 Balanced Experimental Designs - Latin Square -- 11.9 Two-Level Factorial Experimental Designs with Quantitative Factors -- 11.9.1 Two-Level Factorial Designs for Exploratory Studies -- 11.9.2 The Standard Order -- 11.9.3 Calculating Main Effects -- 11.9.4 Calculating Interactions -- 11.9.5 Estimating Standard Errors -- 11.9.6 Estimating Effects with REGRESSION in Excel -- 11.9.7 Interpretation -- 11.9.8 Cube, Surface, and NED Plots as an Aid to Interpretation -- 11.9.9 Fractional Factorial Two-Level Experiments -- 11.10 Summary -- 11.11 Exercises -- References -- Chapter 12 Uncertainty and Sensitivity Analysis -- 12.1 Introduction -- 12.2 Simulation Modeling -- 12.2.1 Propagation of Errors -- 12.2.2 Simple Bounding -- 12.2.3 Addition in Quadrature -- 12.2.4 LOD and LOQ Revisited - Dust Sample Gravimetric Analysis -- 12.3 Uncertainty Analysis -- 12.4 Sensitivity Analysis -- 12.4.1 One-at-a-Time (OAT) Analysis -- 12.4.2 Variance-Based Analysis -- 12.5 Further Reading on Uncertainty and Sensitivity Analysis -- 12.6 Monte Carlo Simulation -- 12.7 Monte Carlo Simulation in Excel -- 12.7.1 Generating Random Numbers in Excel -- 12.7.2 The Populated Spreadsheet Approach -- 12.7.3 Monte Carlo Simulation Using VBA Macros -- 12.8 Summary -- 12.9 Exercises -- References. 327 $aChapter 13 Bayes' Theorem and Bayesian Decision Analysis. 330 $aReviews and reinforces concepts and techniques typical of a first statistics course with additional techniques useful to the IH/EHS practitioner. Includes both parametric and non-parametric techniques described and illustrated in a worker health and environmental protection practice context Illustrated through numerous examples presented in the context of IH/EHS field practice and research, using the statistical analysis tools available in Excel® wherever possible Emphasizes the application of statistical tools to IH/EHS-type data in order to answer IH/EHS-relevant questions Includes an instructor's manual that follows in parallel with the textbook, including PowerPoints to help prepare lectures and answers in the text as for the Exercises section of each chapter. 606 $aIndustrial hygiene--Statistical methods 615 0$aIndustrial hygiene--Statistical methods. 676 $a363.110727 700 $aJohnson$b David L$0149021 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910971929603321 996 $aStatistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences$94473106 997 $aUNINA