Course Description
This 5-day introduction to Laboratory Data Analysis presents a detailed discussion of important statistical concepts and methods of data presentation and analysis.
The training program is designed to reply to the needs of all laboratory professionals in oil and gas plants, pharmaceutical companies, petrochemical industries, food manufacturing companies and other enterprises involved in mass and continuous production.
What Do Participants Learn?
- Enhance your ability to extract more meaningful data from your experimental data sets
- Reduce the number of measurements required for certain applications
- Learn useful and unambiguous recipes for analyzing data
- Gain confidence in the use of basic statistical methods
- Learn how to best utilize MS Excel functions to analyze experimental data
- Improve your decision-making abilities
- Understand the language of data statistics
- Learn new ways to look at data
Who Should Attend?
Laboratory managers, supervisors, technicians, scientists, engineers, laboratory managers, R&D managers, manufacturing and production managers, and in addition to other quality assurance and quality control professionals who are interested in managing their functions in accordance with a data-based approach.
This course assumes no previous knowledge of statistics.
What Will the Learning Experience Include?
Phase: 1
Introduce
- Comprehensive pre-program activities include:
- Web-based information forms & surveys completed by attendee.
- Direct consultation with the attendee about the expectations.
- During the training, participants engage in data, activities, and conversations that lead to insight and knowledge.
- Participants learn from expert trainers who have both academic and business experiences.
- Highly applicable training content & instructive activities for adding depth to training topics.
- **A half-day site visit for integrating the experience & plan next steps. Opportunities to provide connections, ideas & support.
Phase: 2
Explore & Practice
Phase: 3
Apply
- Apply & sustain the learning experience by using this ongoing support:
- To ensure participant has new skills or behavior progress.
- Optional, fee-based mentoring & coaching with the trainer.
- Training materials & additional documents (e-books, pdf files, presentations and articles)
- Evaluate your training experience by giving us feedbacks and help us to reach our organizational goals.
- Participant's Evaluation
- Trainer's Evaluation
Phase: 4
EVALUATE
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