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Analysis of Lab Data with Excel 

Tue/Wed, 16-17/6/2020

Disculpe las molestias, la página solicitada no está disponible en español.

The course covers measurement accuracy, comparison of two samples, Analysis of Variance (ANOVA) including interlaboratory trials, as well as linear regression (including confidence limits for the regression parameters) and calibration.
This course requires statistical knowledge at the level of "Visualization of Lab Data" or equivalent, as well as elementary knowledge of Excel.


What would you learn ?
This 2-day course is aimed at those who need to interpret, evaluate and guarantee the quality of results obtained from their analytic laboratories. After the course, participants will be able to apply the relevant statistical tools using Excel. In particular, emphasis is made on data visualization and modelling. Concepts and methods needed e.g. for validation purposes are addressed, such as the analysis of variance (ANOVA) for the comparison of several samples or analysis of inter-laboratory experiments, and linear regression for prediction and calibration.

The emphasis is on the practical application of these methods. Exercises using real examples constitute an important part of the course. The course uses Excel with supplementary macros ( EasyStat ).


Who should attend ?
  • Lab technicians and supervisors, chemists, engineers
  • Elementary statistical knowledge is assumed (as given in the course "Visualization of Lab Data" )
  • Basic knowledge of Excel is essential

Which topics are covered ?
 Basic Concepts
 Simple graphical representation of results (Boxplot & Histogram)
Confidence interval for the mean
Applications in validation (Accuracy, Trueness and Precision)
Outliers and outlier tests
 Comparison of Samples (Series of Measurements)
 Graphical comparisons (parallel boxplots)
Statistical Tests for the difference between 2 samples
Analysis of variance for the comparison of several samples
  Inter-laboratory Experiments
 Reproducibility of measurement methods
Variance components in the evaluation of inter-laboratory trials
 Linear Regression
 Fitting a straight line
Confidence intervals on the slope and intercept
Goodness of fit and residual analysis
Transformations to achieve linearity
Prediction
Regression through the origin
 Calibration
 Calibration as inverse Regression
Confidence intervals


Any questions ?
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