Matches in DBpedia 2016-04 for { ?s ?p "Detrended correspondence analysis (DCA) is a multivariate statistical technique widely used by ecologists to find the main factors or gradients in large, species-rich but usually sparse data matrices that typify ecological community data. For example, Hill and Gauch (1980, p. 55) analyse the data of a vegetation survey of southeast England including 876 species in 3270 relevés. After eliminating outliers, DCA is able to identify two main axes: The first axis goes from dry to wet conditions, and the second axis from woodland to weed communities."@en }
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- Detrended_correspondence_analysis abstract "Detrended correspondence analysis (DCA) is a multivariate statistical technique widely used by ecologists to find the main factors or gradients in large, species-rich but usually sparse data matrices that typify ecological community data. For example, Hill and Gauch (1980, p. 55) analyse the data of a vegetation survey of southeast England including 876 species in 3270 relevés. After eliminating outliers, DCA is able to identify two main axes: The first axis goes from dry to wet conditions, and the second axis from woodland to weed communities.".
- Q5265809 abstract "Detrended correspondence analysis (DCA) is a multivariate statistical technique widely used by ecologists to find the main factors or gradients in large, species-rich but usually sparse data matrices that typify ecological community data. For example, Hill and Gauch (1980, p. 55) analyse the data of a vegetation survey of southeast England including 876 species in 3270 relevés. After eliminating outliers, DCA is able to identify two main axes: The first axis goes from dry to wet conditions, and the second axis from woodland to weed communities.".