Ensuring the quality of analytical data is a vital aspect of the work of an analytical scientist. The effective planning of experiments and evaluation of data requires an understanding of statistics. Knowledge of statistics is also needed to carry out method validation and evaluate measurement uncertainty. This course is aimed at analysts and covers the statistics most commonly applied to analytical data. It will allow analysts to answer questions such as, ‘Which is the best way to summarise my data?’, ‘Is there a real difference between the results produced by different test methods?’, ‘How should I evaluate the results obtained from an instrument calibration experiment?’ The course includes laptop-based workshops using Excel.
What are the benefits?
This course will help you:
- Understand some of the most important statistical concepts used by analytical scientists
- Calculate the most commonly used statistical parameters
- Carry out significance tests to identify differences between sets of data
- Use linear regression in calibration
- Use Excel functions for the analysis of data.
The course will cover:
- Introduction to statistics
- Significance testing: t- and F-tests
- Analysis of variance (ANOVA)
- Linear regression
- Control charts.
Who should attend?
The course is aimed at analysts who need to evaluate data or carry out tasks such as method validation and uncertainty estimation. The course focuses on the practical application of statistical techniques and is suitable for those with limited or no prior experience of statistics.