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Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences
Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences
Autore Johnson David L
Pubbl/distr/stampa New York : , : John Wiley & Sons, Incorporated, , 2017
Descrizione fisica 1 online resource (395 pages)
Disciplina 363.110727
Soggetto topico Industrial hygiene--Statistical methods
Soggetto genere / forma Electronic books.
ISBN 9781119351351
9781119143017
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910795834003321
Johnson David L  
New York : , : John Wiley & Sons, Incorporated, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences
Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences
Autore Johnson David L
Pubbl/distr/stampa New York : , : John Wiley & Sons, Incorporated, , 2017
Descrizione fisica 1 online resource (395 pages)
Disciplina 363.110727
Soggetto topico Industrial hygiene--Statistical methods
ISBN 9781119351351
9781119143017
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910971929603321
Johnson David L  
New York : , : John Wiley & Sons, Incorporated, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui