Quadrat Analysis_RW_Thomas

In fact the poisson is a limiting case of the

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Unformatted text preview: and the proof is as follows: (this proof may be omitted on the first reading) Let r and n tend to infinity such that the ration r/n tends to a Table 1. Usually, for any probability distribution, it is not necessary to small differences between the respective results listed in Table 1. However, as a general rule, the Poisson will only give an acceptable approximation if all the following conditions are fulfilled: (26) (27) (28) The deductions leading to the establishment of the random probability model presented here are based on results in Feller (1957) and Gray (1967, p.52). However, in the literature of quadrat analysis alternative derivations of the Poisson and the binomial models may be found in Rogers (1974, p.3) and Greig-Smith (1964, p.12). They begin by assuming that the study area is capable of being sub-divided into an infinite number of quadrats, that is space is continuous, their deductions then lead to the establishment of the Poisson as the pure random model, with the binomial being a limiting case of the Poisson capable of predicting minor departures from randomness. Essentially, our derivation is appropriate for quadrat censuring where...
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This note was uploaded on 02/15/2012 for the course GEO 6938 taught by Professor Staff during the Summer '08 term at University of Florida.

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