Statistics for analytical scientists
|TRSTAT53||Teddington||09 Feb 2022||1 day||£550.00 +VAT||Book now|
|TRSTAT54||Online||27 Apr 2022||2 days||£550.00 +VAT||Book now|
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.
We are now offering our training courses online. The day online sessions will take place at 9.30 and 13.30 (GMT). Download the programme for full details.
CLASSROOM BASED COURSES
Classroom based course are delivered over 1 day. If the course you have been booked on cannot be delivered we will offer you the option to attend remotely if possible, or we will transfer you to the next date. If neither of these options are suitable we will provide a refund of any course fees paid. Download the course programme.
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.