Advanced Statistical Analysis of Laboratory Data
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Advanced Statistical Analysis of Laboratory Data Course
Introduction:
The analytical laboratory has long played a crucial role in supporting other scientific and engineering disciplines, helping to regulate process variables or product quality, and giving data. But in many cases, the laboratory has emerged in recent years as a semi-independent institution, able to solve problems using the techniques at its disposal rather than only providing data for interpretation by others. It doesn't matter if these issues are resolved separately or through teamwork. It's crucial to acknowledge the laboratory's wide range of capabilities.
Course Objectives:
Upon the successful completion of the seminar, participants will be able to:-
- Review statistical formulas used in QC/QA and illustrate method development & validation
- Identify the proper procedure for analytical measurement & uncertainty including its uncertainty sources, error and uncertainty, method validation and traceability
- Explain the uncertainty evaluation procedure for Quantifying Uncertainty (GUM), and use prior collaborative method development and validation study data
- Calculate the combined uncertainty and analyze the results based on standard and expanded uncertainty reports
- Explain the calibration functions which include the establishment of an analytical range, determination of the calibration function, verification of linearity & precision and recovery
- Enumerate the types of Statistical Quality Control Charts (SQC) and interpret inter & intra laboratory data
Who Should Attend?
This seminar is aimed at all degree-holder staff of Analytical Laboratories. R&D and government statutory employees are encouraged to attend this outstanding seminar. Further, this seminar is very important for QA/QC employees and Third-Party Inspection and certification companies.
As a pre-requisite to attend this advanced seminar, participants shall have enough knowledge and skills in basic statistics within analytical laboratory.
Course Outlines:
Key Topics You Will Learn About:
- How to understand the strengths and weaknesses of data
- How to recognize and reduce different types of errors
- Ways to carry out significance tests
- How to correctly use outlier tests and when not to use them
- Ways of defining the limits of detection, determination, and quantification
- How to know what statistical test to use when
- How to understand the influence of sample size on statistical significance and power
- Why pooling variances gives stability to analytical results
- How to set in-house specifications
- How to apply statistical process control charts to measurement processes