Multivariate data analysis (MVDA) is a statistical technique that can be used to analyze data with more than one variable in order to look for deviations and understand the relationships between the different data points. In practice, this can mean taking data from a number of different sources and turning it into meaningful information from which you can draw some conclusions.
In life science, biopharma and other areas of research, development and production, design of experiments (DOE) provides a systematic method to determine cause and effect relationships between factors and responses affecting a process, product or analytical system. But the key to understanding your results is effective analysis of your experimental data.
What do we mean by pre-processing of data, and why is it needed? Let's take a look at some data pre-processing methods and how they help create better models when using Principle Component Analysis (PCA) and other methods of data analytics.
In this blog post, we’ll take a closer look at a feature of the SIMCA data analytics software called the Omics skin. So what exactly is an “omics” skin?
In chemical manufacturing, the process involved in creating a breakthrough product often takes several years — with ongoing tests that may be based on trial and error as much as specifically applied knowledge. One area of development in the specialty chemicals market involves the creation of new new additives called plasticizers that can help resins or polymers retain a more supple or flexible nature.
Analyzing batch process data is a lot like juggling. You have multiple sets of data from different sources and in order to turn them into a meaningful presentation, you need a method of handling them to make sure they are all in the right place at the right time.
When to apply OPLS-DA vs PCA for metabolomics and other omics data analysis
Do you know when to use OPLS-DA and when to use PCA/SIMCA data analysis techniques? Find out how to uncover the differences in your data with these classification and discriminant analysis methods.
How Multivariate Data Analysis Can Separate the Players from the Gorillas
We have more data than ever before coming at us from many sources – both in our personal lives as well as business. Data is everywhere: from the production flow of a manufacturing floor to the sales results in a grocery store to the number of shares a page gets on Facebook. How do you sort it all out in a way that makes sense? Which data should you worry about and which should you ignore?
Producing and distributing raw materials and foodstuffs with a low profit margin is a challenging business. One major supplier has made significant gains through applying multivariate data analysis (MVDA) to their manufacturing processes and logistics.
The key to process manufacturing success is a mixture of knowledge and experience supported by mastery of data. A presentation I recently attended put this into sharp focus. A major paper manufacturer was faced with the challenge of maintaining paper smoothness during production. They approached this problem in a way that gave them enormous insight into their process, the ability to control it in real time, and ultimately lead to cost savings and maintained quality. There were also a few added benefits, including the ability to spot, diagnose and solve problems in real time.