Statistical Analysis of Laboratory Data: Method Development, Method Validation, Uncertainty, Calibration, SQC and Data Interpretation

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Statistical Analysis of Laboratory Data: Method Development, Method Validation, Uncertainty, Calibration, SQC and Data Interpretation Course
Introduction:
The results of quantitative chemical measurements are inherently associated with a certain level of uncertainty, which is determined by the performance characteristics of the analytical method employed. Traditionally, measurement uncertainty has been assessed based on the repeatability and reproducibility of data. However, the concept of measurement uncertainty, as outlined in the "Guide to the Expression of Uncertainty in Measurement" published by ISO in 1993, extends beyond these factors and provides general guidelines for evaluating measurement uncertainty, incorporating both statistical (Type A) and non-statistical (Type B) uncertainties.
This course extensively covers the majority of the methods and techniques documented in the ISO guide, offering a comprehensive understanding of the principles and calculations involved in assessing measurement uncertainty in quantitative chemical analyses. Participants will gain knowledge of the fundamental principles and practical application of measurement uncertainty evaluation in the field of chemical analysis.
Course Objectives:
By the end of the training, participants will be able to:
- Learn the concepts involved in the calculation of measurement uncertainty
- Learn the calculating measurement uncertainty in a practical and pragmatic manner
- Define measurement processes
- Identify sources of measurement error
- Select appropriate error distributions
- Use different methods to evaluate measurement uncertainty
- Be knowledgeable with the measurement uncertainty by practice methods
Who Should Attend?
This course is intended for Chemists, Lab Technicians, Chemical Engineers, Instrument Engineers and Lab Supervisors/Managers.
Course Outlines:
- Introduction
- Instrument analysis data
- Peak evaluation
- Interpolated graph calibration using external/ internal standards
- Standard addition method extrapolated graph
- Errors in quantitative analysis
- Random and systematic errors in titration analysis
- The standard deviation of repeated measurements
- Distribution of errors
- Confidence limit of the mean of replicate measurements
- Measurement uncertainty
- Errors in instrumental analysis regression and correlation
- Use of regression lines for comparing analytical methods Confidence limit for X-value Outliers in regression Limit of detection Significance tests for evaluation of experimental results
- (T-test) comparison of a mean with a known value
- (T-test) comparison of the means of two samples with S1» S2
- (T-test) comparison of the means of two samples with S1 ¹ S2
- Paired T-test and One-tailed and Two-tailed tests
- (F-test) for the comparison of standard deviations
- ANOVA test analysis of several means and variances
- Testing for normality of distribution
- Outliers test
- Non-parametric or distribution-free methods
- Box and whisker plots
- Comparison of a median with a known value (the sign test)
- The confidence interval for non-parametric methods
- Comparison of the medians of two methods (the sign test)
- Comparison median of two un-depended samples (Wilcoxon Rank-Sum test)
- Comparison spread of two sets of non-parametric results (Siegel-Tukey test)
- Rank Correlation for not quantified results (spearman method)
- Non-parametric method on more than two samples (Friedman´s test)
- Non-parametric regression methods (Theil´s test) quality control harts
- Quality control charts
- Shewhart and Cusum Chart
- Experimental design and optimization methods
- Factorial designs
- Estimation of factors interaction by two-way ANOVA test
- Optimization method and Three factors design