Upon the successful completion of this course, the participants will have an understanding of:

• Concepts involved in the calculation of measurement uncertainty.
• Calculating measurement uncertainty in a practical and pragmatic manner.
• Defining measurement processes.
• Identifying sources of measurement error.
• Selecting appropriate error distributions.
• Using different methods to evaluate measurement uncertainty.
• Measurement uncertainty by practice methods.

The course is designed for chemists, lab technicians, chemical engineers, instrument engineers and lab supervisors/managers.

This interactive Training will be highly interactive, with opportunities to advance your opinions and ideas and will include;

• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

• 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
• 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)
• 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 est)
• 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

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration