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.
Autorentext
David L. Johnson has over 40 years of experience in environmental engineering and occupational safety and health practice, research, and teaching. Dr. Johnson was a practicing environmental engineer and industrial hygienist with the United States Army for 20 years, serving in a variety of positions in the United States, Europe, and the Middle East. He joined the faculty of the University of Oklahoma's College of Public Health, Department of Occupational and Environmental Health in 1991.
Inhalt
Preface xv
Acknowledgments xvii
About the Author xix
About the Companion Website xxi
1 Some Basic Concepts 1
1.1 Introduction 1
1.2 Physical versus Statistical Sampling 2
1.3 Representative Measures 3
1.4 Strategies for Representative Sampling 3
1.5 Measurement Precision 4
1.6 Probability Concepts 6
1.6.1 The Relative Frequency Approach 7
1.6.2 The Classical Approach - Probability Based on Deductive Reasoning 7
1.6.3 Subjective Probability 7
1.6.4 Complement of a Probability 7
1.6.5 Mutually Exclusive Events 8
1.6.6 Independent Events 8
1.6.7 Events that Are Not Mutually Exclusive 9
1.6.8 Marginal and Conditional Probabilities 9
1.6.9 Testing for Independence 11
1.7 Permutations and Combinations 12
1.7.1 Permutations for Sampling without Replacement 12
1.7.2 Permutations for Sampling with Replacement 13
1.7.3 Combinations 13
1.8 Introduction to Frequency Distributions 14
1.8.1 The Binomial Distribution 14
1.8.2 The Normal Distribution 16
1.8.3 The Chi-Square Distribution 20
1.9 Confidence Intervals and Hypothesis Testing 22
1.10 Summary 23
1.11 Addendum: Glossary of Some Useful Excel Functions 23
1.12 Exercises 26
References 28
2 Descriptive Statistics and Methods of Presenting Data 29
2.1 Introduction 29
2.2 Quantitative Descriptors of Data and Data Distributions 29
2.3 Displaying Data with Frequency Tables 33
2.4 Displaying Data with Histograms and Frequency Polygons 34
2.5 Displaying Data Frequency Distributions with Cumulative Probability Plots 35
2.6 Displaying Data with NED and Q-Q Plots 38
2.7 Displaying Data with Box-and-Whisker Plots 41
2.8 Data Transformations to Achieve Normality 42
2.9 Identifying Outliers 43
2.10 What to Do with Censored Values? 45
2.11 Summary 45
2.12 Exercises 46
References 48
3 Analysis of Frequency Data 49
3.1 Introduction 49
3.2 Tests for Association and Goodness-of-Fit 50
3.2.1 r × c Contingency Tables and the Chi-Square Test 50
3.2.2 Fisher's Exact Test 54
3.3 Binomial Proportions 55
3.4 Rare Events and the Poisson Distribution 57
3.4.1 Poisson Probabilities 57
3.4.2 Confidence Interval on a Poisson Count 60
3.4.3 Testing for Fit with the Poisson Distribution 61
3.4.4 Comparing Two Poisson Rates 62
3.4.5 Type I Error, Type II Error, and Power 64
3.4.6 Power and Sample Size in Comparing Two Poisson Rates 64
3.5 Summary 65
3.6 Exercises 66
References 69
4 Comparing Two Conditions 71
4.1 Introduction 71
4.2 Standard Error of the Mean 71
4.3 Confidence Interval on a Mean 72
4.4 The t-Distribution 73
4.5 Parametric One-Sample Test - Student's t-Test 74
4.6 Two-Tailed versus One-Tailed Hypothesis Tests 76
4.7 Confidence Interval on a Variance 77
4.8 Other Applications of the Confidence Interval Concept in IH/EHS Work 79
4.8.1 OSHA Compliance Determinations 79
4.8.2 Laboratory Analyses - LOB, LOD, and LOQ 80
4.9 Precision, Power, and Sample Size for One Mean 81
4.9.1 Sample Size Required to Estimate a Mean with a Stated Precision 81
4.9.2 Sample Size Required to Detect a Specified Difference in Student's t-Test 81
4.10 Iterative Solutions Using the Excel Goal Seek Utility 82
4.11 Parametric Two-Sample Tests 83
4.11.1 Confidence Interval for a Difference in Means: The Two-Sample t-Test 83
4.11.2 Two-Sample t-Test When Variances Are Equal 84
4.11.3 Verifying the Assumptions of the Two-Sample t-Test 85
4.11.3.1 Lilliefors Test for Normality 86
4.11.3.2 Shapiro-Wilk W-Test for Normality 87
4.11.3.3 Testing for Homogeneity of Variance 91
4.11.3.4 Transformations to Stabilize Variance 93
4.11.4 Two-Sample t-Test with Unequal Variances - Welch's Test 93
4.11.5 Paired Sample t-Test 95
4.11.6 Precision, Power, and Sample Size for Comparing Two Means 96
4.12 Testing for Difference in Two Binomial Proportions 99
4.12.1 Testing a Binomial Proportion for Difference from a Known Value 100
4.12.2 Testing Two Binomial Proportions for Difference 100
4.13 Nonparametric Two-Sample Tests 102
4.13.1 Mann-Whitney U Test 102
4.13.2 Wilcoxon Matched Pairs Test 104
4.13.3 McNemar and Binomial Tests for Paired Nominal Data 105
4.14 Summary 107
4.15 Exercises 107
References 111
5 Characterizing the Upper Tail of the Exposure Distribution 113
5.1 Introduction 113
5.2 Upper Tolerance Limits 113
5.3 Exceedance Fractions 115
5.4 Distribution Free Tolerance Limits 117
5.5 Summary 119
5.6 Exercises 119
References 121
6 One-Way Analysis of Variance 123
6.1 Introduction 123
6.2 Parametric One-Way ANOVA 123
6.2.1 How the Parametric ANOVA Works - Sums of Squares and the F-Test 124
6.2.2 Post hoc Multiple Pairwise Comparisons in Parametric ANOVA 127
6.2.2.1 Tukey's Test 127
6.2.2.2 Tukey-Kramer Test 128
6.2.2.3 Dunnett's Test for Comparing Means to a Control Mean 130
6.2.2.4 Planned Contrasts Using the Scheffé S Test 132
6.2.3 Checking the ANOVA Model Assumptions - NED Plots and Variance Tests 134
6.2.3.1 Levene's Test 134
6.2.3.2 Bartlett's Test 135
6.3 Nonparametric Analysis of Variance 136
6.3.1 Kruskal-Wallis Nonparametric One-Way ANOVA 137
6.3.2 Post hoc Multiple Pairwise Comparisons in Nonparametric ANOVA 139
6.3.2.1 Nemenyi's Test 139
6.3.2.2 Bonferroni-Dunn Test 140
6.4 ANOVA Disconnects 142
6.5 Summary 144
6.6 Exercises 145
References 149
7 Two-Way Analysis of Variance 151
7.1 Introduction 151
7.2 Parametric Two-Way ANOVA 151
7.2.1 Two-Way ANOVA without Interaction 154
7.2.2 Checking for Homogeneity of Variance 154
7.2.3 Multiple Pairwise Comparisons When There Is No Interaction Term 154
7.2.4 Two-Way ANOVA with Interaction 156
7.2.5 Multiple Pairwise Comparisons with Interaction 158
7.2.6 Two-Way ANOVA without Replication 160
7.2.7 Repeated-Measures ANOVA 160
7.2.8 Two-Way ANOVA …