Many elderly people are afraid of falling – and for good reasons. Falls can have serious consequences for the individual but also the fear of falling could have serious effects on health and independence. A new research project at Luleå University of Technology in Sweden has taken a closer look at fall-related concerns among elderly people, using multivariate data analysis, MVDA, with the ultimate goal of finding diagnostic and training methods that could help reduce falls. Results from the first studies have given some interesting answers.
Could data analytics aid in the diagnosis of severe neurological diseases? In a recent study, a research group at Umeå University has conducted statistical data analysis of biomarkers from patients suffering from Amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig’s disease) and Parkinson’s disease to investigate whether data analytics could help in the diagnosis of – and help distinguish between – the two diseases.
When it comes to continuous quality improvement and removing defects from a process, Six Sigma continues to be the gold standard in manufacturing and process management. This structured, data-driven methodology for discovering problems relies on rigorous analysis of production and process data. For many companies, engaging in a Six Sigma process can be time consuming or even a bit daunting.
Formative assessment has come into focus in recent years. In Sweden, the use of formative assessment is typically emphasized in the curriculum of upper secondary schools. However, scientific studies show both positive as well as no effects at all of formative assessment on student performance.
Furthermore, formative assessment has proved to be time consuming, which obviously is a problem if it has no effects on learning. A new thesis by Daniel Larsson at the Linnæus University, Sweden, shows that multivariate data analysis, MVDA, can be used to give some answers about the effectiveness of such teaching practices.
In pharmaceutical and other industries that rely on spectroscopy and multivariate calibration for quality control of manufacturing processes, optimizing the analysis of spectral data is imperative. Using a tool that is specifically designed with spectral analytics in mind can make the job faster, easier and more reliable.
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 this blog post we will take a closer look at OPLS*, or Orthogonal PLS, a method to model process data. The advantage of OPLS compared to PLS is that you can uncover hidden details and get a more precise understanding of your data – all of which will help you build better predictive models of your processes.
In a previous blog post we discussed how SIMCA-online can help you make complex data simple and ensure that you get maximum value from your data.
In this blog post we will introduce a number of benefits of the newly released versions of SIMCA 15 and SIMCA-online 15. To mention just a few things, you get a much improved ability to model and control complete processes, including processes with a very high complexity. You also get a much better connection between SIMCA and SIMCA-online so that information can flow in both ways.
Using advanced data analytics models in real time opens up a whole new world of possibilities for improving your production processes. Not only does real-time process monitoring provide a level of confidence in your process performance, it can also help improve the overall quality of your production output.