{"product_id":"statistical-tools-for-the-comprehensive-practice-of-industrial-hygiene-and-environmental-health-sciences-9781119143017","title":"Statistical Tools for the Comprehensive Practice","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eReviews and reinforces concepts and techniques typical of a first statistics course with additional techniques useful to the IH\/EHS practitioner.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003eAbout the Author xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Some Basic Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Physical versus Statistical Sampling 2\u003c\/p\u003e \u003cp\u003e1.3 Representative Measures 3\u003c\/p\u003e \u003cp\u003e1.4 Strategies for Representative Sampling 3\u003c\/p\u003e \u003cp\u003e1.5 Measurement Precision 4\u003c\/p\u003e \u003cp\u003e1.6 Probability Concepts 6\u003c\/p\u003e \u003cp\u003e1.6.1 The Relative Frequency Approach 7\u003c\/p\u003e \u003cp\u003e1.6.2 The Classical Approach – Probability Based on Deductive Reasoning 7\u003c\/p\u003e \u003cp\u003e1.6.3 Subjective Probability 7\u003c\/p\u003e \u003cp\u003e1.6.4 Complement of a Probability 7\u003c\/p\u003e \u003cp\u003e1.6.5 Mutually Exclusive Events 8\u003c\/p\u003e \u003cp\u003e1.6.6 Independent Events 8\u003c\/p\u003e \u003cp\u003e1.6.7 Events that Are Not Mutually Exclusive 9\u003c\/p\u003e \u003cp\u003e1.6.8 Marginal and Conditional Probabilities 9\u003c\/p\u003e \u003cp\u003e1.6.9 Testing for Independence 11\u003c\/p\u003e \u003cp\u003e1.7 Permutations and Combinations 12\u003c\/p\u003e \u003cp\u003e1.7.1 Permutations for Sampling without Replacement 12\u003c\/p\u003e \u003cp\u003e1.7.2 Permutations for Sampling with Replacement 13\u003c\/p\u003e \u003cp\u003e1.7.3 Combinations 13\u003c\/p\u003e \u003cp\u003e1.8 Introduction to Frequency Distributions 14\u003c\/p\u003e \u003cp\u003e1.8.1 The Binomial Distribution 14\u003c\/p\u003e \u003cp\u003e1.8.2 The Normal Distribution 16\u003c\/p\u003e \u003cp\u003e1.8.3 The Chi-Square Distribution 20\u003c\/p\u003e \u003cp\u003e1.9 Confidence Intervals and Hypothesis Testing 22\u003c\/p\u003e \u003cp\u003e1.10 Summary 23\u003c\/p\u003e \u003cp\u003e1.11 Addendum: Glossary of Some Useful Excel Functions 23\u003c\/p\u003e \u003cp\u003e1.12 Exercises 26\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Descriptive Statistics and Methods of Presenting Data 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 Quantitative Descriptors of Data and Data Distributions 29\u003c\/p\u003e \u003cp\u003e2.3 Displaying Data with Frequency Tables 33\u003c\/p\u003e \u003cp\u003e2.4 Displaying Data with Histograms and Frequency Polygons 34\u003c\/p\u003e \u003cp\u003e2.5 Displaying Data Frequency Distributions with Cumulative Probability Plots 35\u003c\/p\u003e \u003cp\u003e2.6 Displaying Data with NED and Q–Q Plots 38\u003c\/p\u003e \u003cp\u003e2.7 Displaying Data with Box-and-Whisker Plots 41\u003c\/p\u003e \u003cp\u003e2.8 Data Transformations to Achieve Normality 42\u003c\/p\u003e \u003cp\u003e2.9 Identifying Outliers 43\u003c\/p\u003e \u003cp\u003e2.10 What to Do with Censored Values? 45\u003c\/p\u003e \u003cp\u003e2.11 Summary 45\u003c\/p\u003e \u003cp\u003e2.12 Exercises 46\u003c\/p\u003e \u003cp\u003eReferences 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Analysis of Frequency Data 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 49\u003c\/p\u003e \u003cp\u003e3.2 Tests for Association and Goodness-of-Fit 50\u003c\/p\u003e \u003cp\u003e3.2.1 r × c Contingency Tables and the Chi-Square Test 50\u003c\/p\u003e \u003cp\u003e3.2.2 Fisher’s Exact Test 54\u003c\/p\u003e \u003cp\u003e3.3 Binomial Proportions 55\u003c\/p\u003e \u003cp\u003e3.4 Rare Events and the Poisson Distribution 57\u003c\/p\u003e \u003cp\u003e3.4.1 Poisson Probabilities 57\u003c\/p\u003e \u003cp\u003e3.4.2 Confidence Interval on a Poisson Count 60\u003c\/p\u003e \u003cp\u003e3.4.3 Testing for Fit with the Poisson Distribution 61\u003c\/p\u003e \u003cp\u003e3.4.4 Comparing Two Poisson Rates 62\u003c\/p\u003e \u003cp\u003e3.4.5 Type I Error, Type II Error, and Power 64\u003c\/p\u003e \u003cp\u003e3.4.6 Power and Sample Size in Comparing Two Poisson Rates 64\u003c\/p\u003e \u003cp\u003e3.5 Summary 65\u003c\/p\u003e \u003cp\u003e3.6 Exercises 66\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Comparing Two Conditions 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 71\u003c\/p\u003e \u003cp\u003e4.2 Standard Error of the Mean 71\u003c\/p\u003e \u003cp\u003e4.3 Confidence Interval on a Mean 72\u003c\/p\u003e \u003cp\u003e4.4 The t-Distribution 73\u003c\/p\u003e \u003cp\u003e4.5 Parametric One-Sample Test – Student’s t-Test 74\u003c\/p\u003e \u003cp\u003e4.6 Two-Tailed versus One-Tailed Hypothesis Tests 76\u003c\/p\u003e \u003cp\u003e4.7 Confidence Interval on a Variance 77\u003c\/p\u003e \u003cp\u003e4.8 Other Applications of the Confidence Interval Concept in IH\/EHS Work 79\u003c\/p\u003e \u003cp\u003e4.8.1 OSHA Compliance Determinations 79\u003c\/p\u003e \u003cp\u003e4.8.2 Laboratory Analyses – LOB, LOD, and LOQ 80\u003c\/p\u003e \u003cp\u003e4.9 Precision, Power, and Sample Size for One Mean 81\u003c\/p\u003e \u003cp\u003e4.9.1 Sample Size Required to Estimate a Mean with a Stated Precision 81\u003c\/p\u003e \u003cp\u003e4.9.2 Sample Size Required to Detect a Specified Difference in Student’s t-Test 81\u003c\/p\u003e \u003cp\u003e4.10 Iterative Solutions Using the Excel Goal Seek Utility 82\u003c\/p\u003e \u003cp\u003e4.11 Parametric Two-Sample Tests 83\u003c\/p\u003e \u003cp\u003e4.11.1 Confidence Interval for a Difference in Means: The Two-Sample t-Test 83\u003c\/p\u003e \u003cp\u003e4.11.2 Two-Sample t-Test When Variances Are Equal 84\u003c\/p\u003e \u003cp\u003e4.11.3 Verifying the Assumptions of the Two-Sample t-Test 85\u003c\/p\u003e \u003cp\u003e4.11.3.1 Lilliefors Test for Normality 86\u003c\/p\u003e \u003cp\u003e4.11.3.2 Shapiro–Wilk W-Test for Normality 87\u003c\/p\u003e \u003cp\u003e4.11.3.3 Testing for Homogeneity of Variance 91\u003c\/p\u003e \u003cp\u003e4.11.3.4 Transformations to Stabilize Variance 93\u003c\/p\u003e \u003cp\u003e4.11.4 Two-Sample t-Test with Unequal Variances – Welch’s Test 93\u003c\/p\u003e \u003cp\u003e4.11.5 Paired Sample t-Test 95\u003c\/p\u003e \u003cp\u003e4.11.6 Precision, Power, and Sample Size for Comparing Two Means 96\u003c\/p\u003e \u003cp\u003e4.12 Testing for Difference in Two Binomial Proportions 99\u003c\/p\u003e \u003cp\u003e4.12.1 Testing a Binomial Proportion for Difference from a Known Value 100\u003c\/p\u003e \u003cp\u003e4.12.2 Testing Two Binomial Proportions for Difference 100\u003c\/p\u003e \u003cp\u003e4.13 Nonparametric Two-Sample Tests 102\u003c\/p\u003e \u003cp\u003e4.13.1 Mann–Whitney U Test 102\u003c\/p\u003e \u003cp\u003e4.13.2 Wilcoxon Matched Pairs Test 104\u003c\/p\u003e \u003cp\u003e4.13.3 McNemar and Binomial Tests for Paired Nominal Data 105\u003c\/p\u003e \u003cp\u003e4.14 Summary 107\u003c\/p\u003e \u003cp\u003e4.15 Exercises 107\u003c\/p\u003e \u003cp\u003eReferences 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Characterizing the Upper Tail of the Exposure Distribution 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 113\u003c\/p\u003e \u003cp\u003e5.2 Upper Tolerance Limits 113\u003c\/p\u003e \u003cp\u003e5.3 Exceedance Fractions 115\u003c\/p\u003e \u003cp\u003e5.4 Distribution Free Tolerance Limits 117\u003c\/p\u003e \u003cp\u003e5.5 Summary 119\u003c\/p\u003e \u003cp\u003e5.6 Exercises 119\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 One-Way Analysis of Variance 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 123\u003c\/p\u003e \u003cp\u003e6.2 Parametric One-Way ANOVA 123\u003c\/p\u003e \u003cp\u003e6.2.1 How the Parametric ANOVA Works – Sums of Squares and the F-Test 124\u003c\/p\u003e \u003cp\u003e6.2.2 Post hoc Multiple Pairwise Comparisons in Parametric ANOVA 127\u003c\/p\u003e \u003cp\u003e6.2.2.1 Tukey’s Test 127\u003c\/p\u003e \u003cp\u003e6.2.2.2 Tukey–Kramer Test 128\u003c\/p\u003e \u003cp\u003e6.2.2.3 Dunnett’s Test for Comparing Means to a Control Mean 130\u003c\/p\u003e \u003cp\u003e6.2.2.4 Planned Contrasts Using the Scheffé S Test 132\u003c\/p\u003e \u003cp\u003e6.2.3 Checking the ANOVA Model Assumptions – NED Plots and Variance Tests 134\u003c\/p\u003e \u003cp\u003e6.2.3.1 Levene’s Test 134\u003c\/p\u003e \u003cp\u003e6.2.3.2 Bartlett’s Test 135\u003c\/p\u003e \u003cp\u003e6.3 Nonparametric Analysis of Variance 136\u003c\/p\u003e \u003cp\u003e6.3.1 Kruskal–Wallis Nonparametric One-Way ANOVA 137\u003c\/p\u003e \u003cp\u003e6.3.2 Post hoc Multiple Pairwise Comparisons in Nonparametric ANOVA 139\u003c\/p\u003e \u003cp\u003e6.3.2.1 Nemenyi’s Test 139\u003c\/p\u003e \u003cp\u003e6.3.2.2 Bonferroni–Dunn Test 140\u003c\/p\u003e \u003cp\u003e6.4 ANOVA Disconnects 142\u003c\/p\u003e \u003cp\u003e6.5 Summary 144\u003c\/p\u003e \u003cp\u003e6.6 Exercises 145\u003c\/p\u003e \u003cp\u003eReferences 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Two-Way Analysis of Variance 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 151\u003c\/p\u003e \u003cp\u003e7.2 Parametric Two-Way ANOVA 151\u003c\/p\u003e \u003cp\u003e7.2.1 Two-Way ANOVA without Interaction 154\u003c\/p\u003e \u003cp\u003e7.2.2 Checking for Homogeneity of Variance 154\u003c\/p\u003e \u003cp\u003e7.2.3 Multiple Pairwise Comparisons When There Is No Interaction Term 154\u003c\/p\u003e \u003cp\u003e7.2.4 Two-Way ANOVA with Interaction 156\u003c\/p\u003e \u003cp\u003e7.2.5 Multiple Pairwise Comparisons with Interaction 158\u003c\/p\u003e \u003cp\u003e7.2.6 Two-Way ANOVA without Replication 160\u003c\/p\u003e \u003cp\u003e7.2.7 Repeated-Measures ANOVA 160\u003c\/p\u003e \u003cp\u003e7.2.8 Two-Way ANOVA with Unequal Sample Sizes 162\u003c\/p\u003e \u003cp\u003e7.3 Nonparametric Two-Way ANOVA 162\u003c\/p\u003e \u003cp\u003e7.3.1 Rank Tests 162\u003c\/p\u003e \u003cp\u003e7.3.1.1 The Rank Test 162\u003c\/p\u003e \u003cp\u003e7.3.1.2 The Rank Transform Test 166\u003c\/p\u003e \u003cp\u003e7.3.1.3 Other Options – Aligned Rank Tests 166\u003c\/p\u003e \u003cp\u003e7.3.2 Repeated-Measures Nonparametric ANOVA – Friedman’s Test 166\u003c\/p\u003e \u003cp\u003e7.3.2.1 Friedman’s Test without Replication 167\u003c\/p\u003e \u003cp\u003e7.3.2.2 Multiple Comparisons for Friedman’s Test without Replication 169\u003c\/p\u003e \u003cp\u003e7.3.2.3 Friedman’s Test with Replication 170\u003c\/p\u003e \u003cp\u003e7.3.2.4 Multiple Comparisons for Friedman’s Test with Replication 172\u003c\/p\u003e \u003cp\u003e7.4 More Powerful Non-ANOVA Approaches: Linear Modeling 172\u003c\/p\u003e \u003cp\u003e7.5 Summary 172\u003c\/p\u003e \u003cp\u003e7.6 Exercises 172\u003c\/p\u003e \u003cp\u003eReferences 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Correlation Analysis 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 181\u003c\/p\u003e \u003cp\u003e8.2 Simple Parametric Correlation Analysis 181\u003c\/p\u003e \u003cp\u003e8.2.1 Testing the Correlation Coefficient for Significance 184\u003c\/p\u003e \u003cp\u003e8.2.1.1 t-Test for Significance 185\u003c\/p\u003e \u003cp\u003e8.2.1.2 F-Test for Significance 186\u003c\/p\u003e \u003cp\u003e8.2.2 Confidence Limits on the Correlation Coefficient 186\u003c\/p\u003e \u003cp\u003e8.2.3 Power in Simple Correlation Analysis 187\u003c\/p\u003e \u003cp\u003e8.2.4 Comparing Two Correlation Coefficients for Difference 188\u003c\/p\u003e \u003cp\u003e8.2.5 Comparing More Than Two Correlation Coefficients for Difference 189\u003c\/p\u003e \u003cp\u003e8.2.6 Multiple Pairwise Comparisons of Correlation Coefficients 190\u003c\/p\u003e \u003cp\u003e8.3 Simple Nonparametric Correlation Analysis 190\u003c\/p\u003e \u003cp\u003e8.3.1 Spearman Rank Correlation Coefficient 190\u003c\/p\u003e \u003cp\u003e8.3.2 Testing Spearman’s Rank Correlation Coefficient for Statistical Significance 191\u003c\/p\u003e \u003cp\u003e8.3.3 Correction to Spearman’s Rank Correlation Coefficient When There Are Tied Ranks 193\u003c\/p\u003e \u003cp\u003e8.4 Multiple Correlation Analysis 195\u003c\/p\u003e \u003cp\u003e8.4.1 Parametric Multiple Correlation 195\u003c\/p\u003e \u003cp\u003e8.4.2 Nonparametric Multiple Correlation: Kendall’s Coefficient of Concordance 195\u003c\/p\u003e \u003cp\u003e8.5 Determining Causation 198\u003c\/p\u003e \u003cp\u003e8.6 Summary 198\u003c\/p\u003e \u003cp\u003e8.7 Exercises 198\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Regression Analysis 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 205\u003c\/p\u003e \u003cp\u003e9.2 Linear Regression 205\u003c\/p\u003e \u003cp\u003e9.2.1 Simple Linear Regression 207\u003c\/p\u003e \u003cp\u003e9.2.2 Nonconstant Variance – Transformations and Weighted Least Squares Regression 209\u003c\/p\u003e \u003cp\u003e9.2.3 Multiple Linear Regression 213\u003c\/p\u003e \u003cp\u003e9.2.3.1 Multiple Regression in Excel 215\u003c\/p\u003e \u003cp\u003e9.2.3.2 Multiple Regression Using the Excel Solver Utility 218\u003c\/p\u003e \u003cp\u003e9.2.3.3 Multiple Regression Using Advanced Software Packages 221\u003c\/p\u003e \u003cp\u003e9.2.4 Using Regression for Factorial ANOVA with Unequal Sample Sizes 222\u003c\/p\u003e \u003cp\u003e9.2.5 Multiple Correlation Analysis Using Multiple Regression 227\u003c\/p\u003e \u003cp\u003e9.2.5.1 Assumptions of Parametric Multiple Correlation 233\u003c\/p\u003e \u003cp\u003e9.2.5.2 Options When Collinearity Is a Problem 233\u003c\/p\u003e \u003cp\u003e9.2.6 Polynomial Regression 234\u003c\/p\u003e \u003cp\u003e9.2.7 Interpreting Linear Regression Results 234\u003c\/p\u003e \u003cp\u003e9.2.8 Linear Regression versus ANOVA 235\u003c\/p\u003e \u003cp\u003e9.3 Logistic Regression 235\u003c\/p\u003e \u003cp\u003e9.3.1 Odds and Odds Ratios 236\u003c\/p\u003e \u003cp\u003e9.3.2 The Logit Transformation 238\u003c\/p\u003e \u003cp\u003e9.3.3 The Likelihood Function 240\u003c\/p\u003e \u003cp\u003e9.3.4 Logistic Regression in Excel 240\u003c\/p\u003e \u003cp\u003e9.3.5 Likelihood Ratio Test for Significance of MLE Coefficients 241\u003c\/p\u003e \u003cp\u003e9.3.6 Odds Ratio Confidence Limits in Multivariate Models 243\u003c\/p\u003e \u003cp\u003e9.4 Poisson Regression 243\u003c\/p\u003e \u003cp\u003e9.4.1 Poisson Regression Model 243\u003c\/p\u003e \u003cp\u003e9.4.2 Poisson Regression in Excel 244\u003c\/p\u003e \u003cp\u003e9.5 Regression with Excel Add-ons 245\u003c\/p\u003e \u003cp\u003e9.6 Summary 246\u003c\/p\u003e \u003cp\u003e9.7 Exercises 246\u003c\/p\u003e \u003cp\u003eReferences 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Analysis of Covariance 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 253\u003c\/p\u003e \u003cp\u003e10.2 The Simple ANCOVA Model and Its Assumptions 253\u003c\/p\u003e \u003cp\u003e10.2.1 Required Regressions 255\u003c\/p\u003e \u003cp\u003e10.2.2 Checking the ANCOVA Assumptions 258\u003c\/p\u003e \u003cp\u003e10.2.2.1 Linearity, Independence, and Normality 258\u003c\/p\u003e \u003cp\u003e10.2.2.2 Similar Variances 258\u003c\/p\u003e \u003cp\u003e10.2.2.3 Equal Regression Slopes 258\u003c\/p\u003e \u003cp\u003e10.2.3 Testing and Estimating the Treatment Effects 259\u003c\/p\u003e \u003cp\u003e10.3 The Two-Factor Covariance Model 261\u003c\/p\u003e \u003cp\u003e10.4 Summary 261\u003c\/p\u003e \u003cp\u003e10.5 Exercises 261\u003c\/p\u003e \u003cp\u003eReference 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Experimental Design 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 265\u003c\/p\u003e \u003cp\u003e11.2 Randomization 266\u003c\/p\u003e \u003cp\u003e11.3 Simple Randomized Experiments 266\u003c\/p\u003e \u003cp\u003e11.4 Experimental Designs Blocking on Categorical Factors 267\u003c\/p\u003e \u003cp\u003e11.5 Randomized Full Factorial Experimental Design 270\u003c\/p\u003e \u003cp\u003e11.6 Randomized Full Factorial Design with Blocking 271\u003c\/p\u003e \u003cp\u003e11.7 Split Plot Experimental Designs 272\u003c\/p\u003e \u003cp\u003e11.8 Balanced Experimental Designs – Latin Square 273\u003c\/p\u003e \u003cp\u003e11.9 Two-Level Factorial Experimental Designs with Quantitative Factors 274\u003c\/p\u003e \u003cp\u003e11.9.1 Two-Level Factorial Designs for Exploratory Studies 274\u003c\/p\u003e \u003cp\u003e11.9.2 The Standard Order 275\u003c\/p\u003e \u003cp\u003e11.9.3 Calculating Main Effects 276\u003c\/p\u003e \u003cp\u003e11.9.4 Calculating Interactions 278\u003c\/p\u003e \u003cp\u003e11.9.5 Estimating Standard Errors 278\u003c\/p\u003e \u003cp\u003e11.9.6 Estimating Effects with REGRESSION in Excel 279\u003c\/p\u003e \u003cp\u003e11.9.7 Interpretation 280\u003c\/p\u003e \u003cp\u003e11.9.8 Cube, Surface, and NED Plots as an Aid to Interpretation 280\u003c\/p\u003e \u003cp\u003e11.9.9 Fractional Factorial Two-Level Experiments 282\u003c\/p\u003e \u003cp\u003e11.10 Summary 282\u003c\/p\u003e \u003cp\u003e11.11 Exercises 283\u003c\/p\u003e \u003cp\u003eReferences 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Uncertainty and Sensitivity Analysis 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 285\u003c\/p\u003e \u003cp\u003e12.2 Simulation Modeling 285\u003c\/p\u003e \u003cp\u003e12.2.1 Propagation of Errors 286\u003c\/p\u003e \u003cp\u003e12.2.2 Simple Bounding 287\u003c\/p\u003e \u003cp\u003e12.2.2.1 Sums and Differences 287\u003c\/p\u003e \u003cp\u003e12.2.2.2 Products and Ratios 287\u003c\/p\u003e \u003cp\u003e12.2.2.3 Powers 289\u003c\/p\u003e \u003cp\u003e12.2.3 Addition in Quadrature 289\u003c\/p\u003e \u003cp\u003e12.2.3.1 Sums and Differences 289\u003c\/p\u003e \u003cp\u003e12.2.3.2 Products and Ratios 290\u003c\/p\u003e \u003cp\u003e12.2.3.3 Powers 292\u003c\/p\u003e \u003cp\u003e12.2.4 LOD and LOQ Revisited – Dust Sample Gravimetric Analysis 292\u003c\/p\u003e \u003cp\u003e12.3 Uncertainty Analysis 295\u003c\/p\u003e \u003cp\u003e12.4 Sensitivity Analysis 296\u003c\/p\u003e \u003cp\u003e12.4.1 One-at-a-Time (OAT) Analysis 296\u003c\/p\u003e \u003cp\u003e12.4.2 Variance-Based Analysis 297\u003c\/p\u003e \u003cp\u003e12.5 Further Reading on Uncertainty and Sensitivity Analysis 297\u003c\/p\u003e \u003cp\u003e12.6 Monte Carlo Simulation 297\u003c\/p\u003e \u003cp\u003e12.7 Monte Carlo Simulation in Excel 298\u003c\/p\u003e \u003cp\u003e12.7.1 Generating Random Numbers in Excel 298\u003c\/p\u003e \u003cp\u003e12.7.2 The Populated Spreadsheet Approach 299\u003c\/p\u003e \u003cp\u003e12.7.3 Monte Carlo Simulation Using VBA Macros 299\u003c\/p\u003e \u003cp\u003e12.8 Summary 303\u003c\/p\u003e \u003cp\u003e12.9 Exercises 303\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Bayes’ Theorem and Bayesian Decision Analysis 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 309\u003c\/p\u003e \u003cp\u003e13.2 Bayes’ Theorem 310\u003c\/p\u003e \u003cp\u003e13.3 Sensitivity, Specificity, and Positive and Negative Predictive Value in Screening\u003c\/p\u003e \u003cp\u003eTests 310\u003c\/p\u003e \u003cp\u003e13.4 Bayesian Decision Analysis in Exposure Control Banding 312\u003c\/p\u003e \u003cp\u003e13.4.1 Introduction to BDA 312\u003c\/p\u003e \u003cp\u003e13.4.2 The Prior Distribution and the Parameter Space 314\u003c\/p\u003e \u003cp\u003e13.4.3 The Posterior Distribution and Likelihood Function 314\u003c\/p\u003e \u003cp\u003e13.4.4 Relative Influences of the Prior and the Data 315\u003c\/p\u003e \u003cp\u003e13.4.5 Frequentist versus Bayesian Perspectives 316\u003c\/p\u003e \u003cp\u003e13.5 Exercises 316\u003c\/p\u003e \u003cp\u003eReferences 318\u003c\/p\u003e \u003cp\u003eA z-Tables of the Standard Normal Distribution 321\u003c\/p\u003e \u003cp\u003eB Critical Values of the Chi-Square Distribution 327\u003c\/p\u003e \u003cp\u003eC Critical Values for the t-Distribution 329\u003c\/p\u003e \u003cp\u003eD Critical Values for Lilliefors Test 331\u003c\/p\u003e \u003cp\u003eReference 332\u003c\/p\u003e \u003cp\u003eE Shapiro–Wilk W Test 𝜶 Coefficients and Critical Values 333\u003c\/p\u003e \u003cp\u003eReference 336\u003c\/p\u003e \u003cp\u003eF Critical Values of the F Distribution for 𝜶 = 0.05 337\u003c\/p\u003e \u003cp\u003eG Critical U Values for the Mann–Whitney U Test 341\u003c\/p\u003e \u003cp\u003eReference 342\u003c\/p\u003e \u003cp\u003eH Critical Wilcoxon Matched Pairs Test t Values 343\u003c\/p\u003e \u003cp\u003eReference 344\u003c\/p\u003e \u003cp\u003eI K Values for Upper Tolerance Limits 345\u003c\/p\u003e \u003cp\u003eReference 346\u003c\/p\u003e \u003cp\u003eJ Exceedance Fraction 95% Lower Confidence Limit versus Z 347\u003c\/p\u003e \u003cp\u003eReference 347\u003c\/p\u003e \u003cp\u003eK q Values for Tukey’s, Tukey–Kramer, and Nemenyi’s MSD Tests 349\u003c\/p\u003e \u003cp\u003eL q′ Values for Dunnett’s Test 351\u003c\/p\u003e \u003cp\u003eReference 353\u003c\/p\u003e \u003cp\u003eM Q Values for the Bonferroni–Dunn MSD Test 355\u003c\/p\u003e \u003cp\u003eN Critical Spearman Rank Correlation Test Values 357\u003c\/p\u003e \u003cp\u003eO Critical Values of Kendall’s W 359\u003c\/p\u003e \u003cp\u003eReference 361\u003c\/p\u003e \u003cp\u003eIndex 363\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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