Instructor Led Component Analysis Training
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From Camo Process AS
Multivariate Data Analysis MVA and Design of Experiments DoE - Chemometrics Spectroscopic Applications
This integrated course has been designed to give participants a strong foundation in the basics and fundamentals of Multivariate Data Analysis and DoE. The course combines theoretical studies and practical workshops, ensuring that each participant gets individual focus, and understands the practical uses of MVA & DOE applications in R&D, Product Development, PAT, Laboratory, Quality Control &
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Multivariate Data Analysis - I 3 Days
The Multivariate Data Analysis Level 1 training is a general, multipurpose introductory course. No prior knowledge of multivariate methods are required for the participants.
Who should participate in this program? The courses have been designed for individuals:
Involved in consumer insights, R&D, product development, process optimization, quality control & monitoring
Working with
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From Rapid Progress Marketing and Modeling, LLC
The Essentials of Data Mining and Predictive Modeling for Internet and Direct Marketers



DESCRIPTION
"Data Mining and Predictive Modeling" are intertwined buzzwords that wea ve all heard. And perhaps youa re not quite sure what they mean or how they differ. While this is an incredibly important question to answer, the even more vital question is, "How do I make them meaningful?" This course addresses all of these questions in detail.
Though frequently thought of in terms of
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Advanced Data Mining and Predictive Modeling for Internet and Direct Marketers



DESCRIPTION
"Advanced" data mining and predictive modeling dives into the more sophisticated data mining and modeling techniques that an increasing number of enterprises are employing to generate huge impacts on their bottom lines, whether it be through the internet or through direct marketing channels.
Long gone are the days when data was scarce. In the modern world, wea re drowning in data
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From Systats Consulting Sdn. Bhd.
Advanced Statistical Process Control
...the various sources of process variability using variance component analysis. This knowledge allows the determination of the right sampling plan for rational subgrouping to capture important sources of variability in the process and also to determine the right type of control chart for monitoring and control.
If SPC is not set up to deal with these frequently occurring non-standard
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From Creascience
Multivariate Data Analysis School


The 5-day course, offered in English, focuses on the practical aspects of the most widely used multivariate methods: Principal Component Analysis (PCA), Factor Analysis, Correspondence Analysis, Cluster Analysis, Discriminant Analysis and Canonical Analysis. Lots of time for hands-on!
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Multivariate Techniques for Sensory and Consumer Studies


...ys, classical multivariate techniques including Principal Component Analysis (PCA), Factor Analysis, Correspondence Analysis, Cluster Analysis and Discriminant Analysis are presented and their usefulness for sensory and consumer data is illustrated with case studies based on real data.
The last day is dedicated to alternative and less known applications of these methods to address specific
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Principal Component Analysis and its Applications

...Component Analysis (PCA) is a multivariate method which can identify redundancy or correlation among a set of measurements or variables for the purpose of data reduction. This powerful exploratory tool provides insightful graphical summaries with ability to include additional information as well.
This training course discusses the limitations of traditional descriptive tools for exploring
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From Camo Inc.
Monimuuttujamenetelm t 1 (Multivariate Data Analysis - Finnish Course)


The course hax been designed for individuals:
* Involved in consumer insights, R&D, product development, process optimization, quality control & monitoring.
* Working with spectroscopic instruments (NIR, FTIR, UV, UV/VIS, NMR, DAS, Raman, Mass Spectroscopy) chromatography instruments (LC, CE, GC, HPLC), production data & sensory data, R&D, quality control or production processes.
Course Topics
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Multivariate Data Analysis


Introduction to multivariate data modeling
When are multivariate methods useful?
Principles and applications of:
Principal Component Analysis (PCA)
Multivariate regression: Multi Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS)
Relevant data collection
Multivariate modeling step by step
Pretreatment and scaling
Detecting and dealing with
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