Statistics for analytical scientists

Code Location Date Duration Cost

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.

Contents

The course will cover:

  • Introduction to statistics
  • Significance testing: t- and F-tests
  • Analysis of variance (ANOVA)
  • Linear regression
  • Control charts.

ONLINE COURSES

We are now offering our training courses online. Our next course will be on 22 September over two days with a short audio test session on 15 September.  Download the programme for full details.

CLASSROOM BASED COURSES

Classroom based course are delivered on one days and will be available when considered safe and appropriate according to official guidelines. 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.