Applied Statistics, with Emphasis on Risk Management in R and D, QA QC, and Manufacturing - 0 views
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Health Care Fraud and Abuse: Protecting the Organization in Face of Enhanced Enforcement Activity US Seminar Conference Jim Sheldon Dean HIPAA Regulations Security Rules HIPPA Training Risk Analysis Mitigation Human Subjects
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Roger Steven on 16 Mar 162-day In-person Seminar Applied Statistics, with Emphasis on Risk Management in R&D, QA/QC, and Manufacturing Overview: The 2-day seminar explains how to apply statistics to manage risk in R&D, QA/QC, and Manufacturing, with examples derived mainly from the medical device design/manufacturing industry. The flow of topics over the 2 days is as follows: ISO standards and FDA/MDD regulations regarding the use of statistics. Basic vocabulary and concepts. Statistical Process Control Statistical methods for Design Verification Statistical methods for Product/Process Qualification Metrology: the statistical analysis of measurement uncertainty, and how it is used to establish QC specifications How to craft "statistically valid conclusion statements" (e.g., for reports) Summary, from a risk management perspective Agenda Day One Lecture 1: Regulatory Requirements Lecture 2: Vocabulary and Concepts Lecture 3: Confidence Intervals (attribute and variables data) Lecture 4: Normality Tests and Normality Transformations Lecture 5: Statistical Process Control (with focus on XbarR charts) Lecture 6: Confidence/Reliability calculations for Proportions Lecture 7: Confidence/Reliability calculations for Normally distributed data (K-tables) Lecture 8: Process Capability Indices calculations(Cp, Cpk, Pp, Ppk) Day Two Lecture 1: Confidence/Reliability calculations using Reliability Plotting (e.g., for non-normal data and/or censored studies) Lecture 2: Confidence/Reliability calculations for MTTF and MTBF (this typically applies only to electronic equipment) Lecture 3: Statistical Significance: t-Tests and related "power" estimations Lecture 4: Statistical Significance: ANOVA calculations Lecture 5: Metrology (Gage R&R, Correlation, Linearity, Bias , and Uncertainty Budgets) Lecture 6: QC Sampling Plans (C=0 and Z1.4 attribute AQL plans, and alternatives to such plans) Lecture 7: Statistically valid statements for use in reports Lecture 8: Summary and Impleme