Johnson, David L.
Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences. - 1 online resource (395 pages) - New York Academy of Sciences Ser. . - New York Academy of Sciences Ser. .
Cover -- 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. 3.1 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. 4.13 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. 8.2.2 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. 10.2.1 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. Chapter 13 Bayes' Theorem and Bayesian Decision Analysis.
9781119351351
Electronic books.
Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences. - 1 online resource (395 pages) - New York Academy of Sciences Ser. . - New York Academy of Sciences Ser. .
Cover -- 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. 3.1 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. 4.13 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. 8.2.2 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. 10.2.1 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. Chapter 13 Bayes' Theorem and Bayesian Decision Analysis.
9781119351351
Electronic books.