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In statistics, ranking is the data transformation in which numerical or ordinal values are replaced by their rank when the data are sorted. For example, the ranks of the numerical data 3.4, 5.1, 2.6, 7.3 are 2, 3, 1, 4. As another example, the ordinal data hot, cold, warm would be replaced by 3, 1, 2.
Ordination methods can broadly be categorized in eigenvector-, algorithm-, or model-based methods. Many classical ordination techniques, including principal components analysis, correspondence analysis (CA) and its derivatives (detrended correspondence analysis, canonical correspondence analysis, and redundancy analysis, belong to the first group).
The rank abundance curve visually depicts both species richness and species evenness. Species richness can be viewed as the number of different species on the chart i.e., how many species were ranked. Species evenness is reflected in the slope of the line that fits the graph (assuming a linear, i.e. logarithmic series, relationship).
Dave Kerby (2014) recommended the rank-biserial as the measure to introduce students to rank correlation, because the general logic can be explained at an introductory level. The rank-biserial is the correlation used with the Mann–Whitney U test, a method commonly covered in introductory college courses on statistics. The data for this test ...
As an example, consider groups of similar animals (mice, for example) under two different diet systems. The research question would be: what is the best diet? In this case, H 0 would be that there is no difference between the two diets in mice metabolism (H 0 : μ 1 = μ 2 ) and the alternative hypothesis would be that the diets have different ...
In botany, the ranks of subclass and suborder are secondary ranks pre-defined as respectively above and below the rank of order. [7] Any number of further ranks can be used as long as they are clearly defined. [7] The superorder rank is commonly used, with the ending -anae that was initiated by Armen Takhtajan's publications from 1966 onwards. [8]
The geometric series rank-abundance diagram is linear with a slope of –k, and reflects a rapid decrease in species abundances by rank (Figure 4). [12] The geometric series does not explicitly assume that species colonize an area sequentially, however, the model fits the concept of niche preemption, where species sequentially colonize a region ...
Intuitively, the Kendall correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) rank (i.e. relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully different for a ...