Laboratory Data Analysis

Section One:

• Laboratory functions, systems and culture
• Laboratory’s role in enterprise performance
• Fundamentals, dimensions and determinants of quality in laboratory functions
• Quality planning, control and improvement
• The major quality initiatives
• Laboratory quality assurance procedures in the international standards
• Types of data in the laboratory environment
• Laboratory information management
• Procedures for assuring validity and quality in laboratory data

Section Two: 

• Laboratory Safety Program
• Safety Officer Responsibilities
• OSHA requirements
• Education and Training
• Documents and Records
• Lab Audits and Inspections

Section Three:

• Categories of data and purposes of data collection and analysis
• Data collection techniques and tools
• Data adequacy, quality, stability and integrity
• Detection and handling of outliers
• Statistical terminology
• Populations and samples
• Descriptive and inferential statistics

Day Four:

• Modeling massive data as probability distributions
• Measuring the association between the qualitative variables
• X-Y covariance and correlation analysis
• Computing, validating and applying regression models
• Setting and testing of hypothesis
• Computing confidence intervals to express uncertainty
• Robust statistical analysis of laboratory data
• Statistical analysis of calibration data

Day Five:

• Measurement system analysis (accuracy and precision)
• Assessing the bias, stability and linearity
• Analysis of gage repeatability and reproducibility
• Statistical process control (SPC) and control charts
• Measuring process capability (performance and potential indicators)
• Estimating the process yield
• Measurement method validation
• Measurement and Calibration
• Accuracy and Precision
• Standard Deviation