Safety Incidents - a Statistical Analysis
Performance of Safety Incidents
Statistical Analysis of Safety Incident Rates
Table of Contents
Introduction 3
Part I. Graphical Descriptive Statistics 3
Part II. Binomial Probability Distribution 4
Part III. Inferential Statistics 5
Part IV. One Sample Hypothesis T-test 5
Part V. Two Sample Hypothesis T-test 6
Part VI. Paired (matched) Observation – Two Populations Hypothesis 6
Part VII. Linear Regression and Correlation Study 7
Part VIII. ANOVA – One-Way Test of Variance 7
Part XI. Chi-square Goodness-of-Fit Test 7
Part X. Chi-square Independence Test 7
Conclusion 7
Appendix A: 9
Introduction
The measurement of safety performance is a hot topic in most companies today. Safety performance measurements seek to answer such questions as how do we compare to others? Are we getting better or worse over time? Is our management of safety effective (doing the right things)? Safety differs from many areas measured by managers because success results in the absence of an outcome (injuries or ill health) rather than a presence. As the scope of safety is vast, many stakeholders who are not safety professionals do not see safety as in such a broad way. They see “no injury” as good and “injury” as bad and that where it ends. This is one reason why incident rates and worker’s compensations costs have moved their way to the forefront of safety metrics. As an employee of a Facilities Engineering Command (a federal agency), working in the Operations Division, my role as an analyst requires me to gather and review metrics. Our safety metrics are very important to our organization as the safety and health program is dedicated to establishing a safety conscious culture throughout the command, ensuring that safe facilities are designed and constructed, ensuring that facilities and equipment are maintained in a safe, operating condition, eliminating facility, public works, and contact-related injuries and illnesses. In this paper will
Statistical Analysis of Safety Incident Rates
Table of Contents
Introduction 3
Part I. Graphical Descriptive Statistics 3
Part II. Binomial Probability Distribution 4
Part III. Inferential Statistics 5
Part IV. One Sample Hypothesis T-test 5
Part V. Two Sample Hypothesis T-test 6
Part VI. Paired (matched) Observation – Two Populations Hypothesis 6
Part VII. Linear Regression and Correlation Study 7
Part VIII. ANOVA – One-Way Test of Variance 7
Part XI. Chi-square Goodness-of-Fit Test 7
Part X. Chi-square Independence Test 7
Conclusion 7
Appendix A: 9
Introduction
The measurement of safety performance is a hot topic in most companies today. Safety performance measurements seek to answer such questions as how do we compare to others? Are we getting better or worse over time? Is our management of safety effective (doing the right things)? Safety differs from many areas measured by managers because success results in the absence of an outcome (injuries or ill health) rather than a presence. As the scope of safety is vast, many stakeholders who are not safety professionals do not see safety as in such a broad way. They see “no injury” as good and “injury” as bad and that where it ends. This is one reason why incident rates and worker’s compensations costs have moved their way to the forefront of safety metrics. As an employee of a Facilities Engineering Command (a federal agency), working in the Operations Division, my role as an analyst requires me to gather and review metrics. Our safety metrics are very important to our organization as the safety and health program is dedicated to establishing a safety conscious culture throughout the command, ensuring that safe facilities are designed and constructed, ensuring that facilities and equipment are maintained in a safe, operating condition, eliminating facility, public works, and contact-related injuries and illnesses. In this paper will