Based on your data, we can give you clues about trends, relationships, deviations, patterns.
You want a more permanent tool? We can provide dedicated solutions, created for your case.
You are in doubt how to approach a problem, what actions to take, which experiment set-up is the best? We can give you advice based on a pragmatic statistical approach.
Numerical solutions for (systems of) differential equations. From launching of a space missile to description of complex (bio ) chemical processes. May serve to make predictions, and supports development of models which help to understand basic mechanisms.
Fitting data to linear, non-linear or dynamic models. Simple models, multi response models, multivariate models, time-related models. Estimation of most likely values for the parameters in a model, and their accuracies. Often least square optimization is used. For cases where measurement errors are significant in both predictor and response variables, orthogonal regression is the method of choice.
An application of a multi-response non-linear regression model. In the example graph, loss of quality of a product can be described by a 1st order kinetic model (exponential decay equation). Stability is measured at different temperatures. The curves can be related by the Arrhenius equation. Integrated (simultaneous) analysis of all three curves allows prediction of shelf life for any temperature regime.
Generation of (millions of) random number is the basis of this method. Can be used for prediction of complicated processes, when mechanisms are more or less known, but subject to uncertainties. The weather forecast is a typical example of an application of Monte-Carlo simulation. But there are many more areas where stochastic prediction is more useful than deterministic prediction, for instance: business or financial forecasts, worst-case versus best case scenario analysis.
Most processes in real life show smaller or larger variations. It can be difficult to recognize gradual changes against the background of ‘natural’ variation. Ability to detect step changes as result of (unnoticed) changes in external conditions can be very valuable. In addition to step changes, also changes in trend can be important to recognize.
Bringing structure in smaller or larger data sets can be very helpful for the recognition of patterns, fishing out relevant cases, identifying relationships. Clustering can be agglomerative (grouping together) of divisive (splitting up). The number of clusters can be pre-determined or can follow from the data. The separation between clusters can be sharp or be fuzzy.
Availability of massive amounts of data is more and more common these days. This calls for effective methods of handling these data; methods that take a step further than what is possible in a simple spread sheet program. A palette of techniques is available to get the crux out of large data sets. For instance, fishing out the differences between “good” and “bad” performance. Multivariate techniques like stepwise regression, principal component analysis, and partial least squares may reveal complex relationships.
Contact us to see whether we can shine a light on your data ...
Pileus’ founder and director, Johannes de Hollander, had an education in chemistry, holds a PhD in microbiology, and has multi-year experience in (bio)process development. Modeling and simulation always played a crucial part in his work. Large scale data handling, statistics and data mining became more prominent during the years. The combination of an experimentalists approach with analytical excellence provides the perfect mix for solving data-oriented problems in a wide range of fields.
Office location and postal address
Burgemeester van Lierestraat 36
4436 AL Oudelande
(+31) (0) 649 238 972