Statistical tools for the comprehensive practice of industrial hygiene and environmental health sciences / David L. Johnson.

Format:

Book

Author/Creator:

Johnson, David L. (David Lee), 1949- author.

Series:

New York Academy of Sciences

Language:

English

Subjects (All):

Industrial hygiene--Statistical methods.

Industrial hygiene.

Environmental health--Statistical methods.

Environmental health.

Physical Description:

1 online resource (395 pages) : illustrations, tables

Edition:

First edition.

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

Summary:

Reviews 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.

Contents:

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

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

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

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

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

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

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

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

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

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.

Notes:

Includes bibliographical references and index

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

1-119-35137-5

OCLC:

967589474