The tool will enable you to make predictions on the number of new cases, mortality rates and more, and direct your efforts to areas where they are most needed.
This is why Addinsoft, the maker of XLSTAT, has developed a tool that allows you to gain valuable insight into the latest COVID-19 data available through the ECDC. The speed at which the virus has spread has left most countries scrambling for a response and often highlights how poorly prepared their institutions and healthcare systems are equipped to deal with a pandemic of this proportion.Īlthough the human toll and scope of this health crisis exceed any in modern history, our ability to contain, manage and overcome it will depend in part on our capacity to predict the possible evolution of the disease in the coming months and to forecast with greater accuracy what is required in terms of supplies, medical equipment, and staff, for example. PCA could be applied to explore relationships between volatile compounds and sensory attributes in different food systems.The current coronavirus outbreak represents a formidable challenge to governments and healthcare professionals around the world. PCA clustered marinated and unmarinated meats based on the presence and abundances of volatile terpenes, thiols and consumer sensory attribute scores. XLSTAT PCA output successfully reduced the number of variables into 2 components that explained 90.47% of the total variation of the data set. PCA was conducted to determine the correlations between the abundances of volatile terpenes and thiols and sensory attribute scores in marinated grilled meats, as well as to analyze if there was any clustering based on the type of meat and marination treatments employed. As a case of study, multivariate analysis is used to study the effects of unfiltered beer-based marination on the volatile terpenes and thiols, and sensory attributes of grilled ruminant meats. Interests in XLSTAT as statistical software program of choice for routine multivariate statistics has been growing due in part to its compatibility with Microsoft Excel data format. Principal component analysis (PCA) is an unsupervised multivariate analysis technique that simplifies the complexity of data by transforming them in a few dimensions showing their trends and correlations. Multivariate statistics is a tool for examining the relationship of multiple variables simultaneously.
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