- #The unscrambler x by camo software full#
- #The unscrambler x by camo software software#
- #The unscrambler x by camo software windows#
The new main features of version 7â⁕ can be categorised into
#The unscrambler x by camo software full#
The program also offers a well developed experimental design module including standard designs (screening, full and fractional factorial), mixture designs and response surface modelling. The program gives easy access, through a very informative graphic interface, to the main work horses of chemometrics, partial least squares regression (PLSR), principal component analysis and regression (PCA and PCR), multiple linear regression (MLR), and soft independent modelling of class analogy (SIMCA).
#The unscrambler x by camo software windows#
8, N-0158 Oslo, Norway Phone: Â 47 2239 6300, Fax: Â 47 2239 6632, CAMO Inc, PO Box 1628, Corvallis, Oregon 97339, USA Phone: Â 1 5, Fax: Â 1 5, CAMO Ltd, Studlands Park Avenue, Newmarket, Suffolk, CB8 7EA, UK Phone: Â 44 1638 660600, Fax: Â 44 1638 667730, CAMOmetri AB, Knivsbrunna, S-755 98 Uppsala, Sweden Phone: Â 46 18 59 11 16, Fax: Â 46 18 59 11 17, In the spring of 1999 CAMO A/S introduced version 7â⁕ of The Unscrambler for Windows 95/98 and Windows NT, a program for multivariate data analysis and experimental design.
#The unscrambler x by camo software software#
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Gasoline blending in petroleum refining.Fermentation monitoring in biotech or food.
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Hierarchical models are ideal for applications such as: This is the classic logic tree or decision tree approach, where the analysis in one step is guided by the previous step. Hierarchical models join a number of multivariate models using logic statements in order to arrive at a single, unique result. Therefore, it is often necessary to refine models based on the output of initial investigations, which is usually done manually and in many steps, becoming a laborious and time-consuming process which is prone to error.
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When analyzing process or other complex data, it can be difficult to make a global prediction or classification model that predicts well in every area.