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EEB375H1 (1)
Final

# EEB375 Stats Summary for Exam

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School
University of Toronto St. George
Department
Ecology & Evolutionary Biology
Course
EEB375H1
Professor
Laurel Duquette
Semester
Fall

Description
Estimating Population Dispersion • s /mean > 1.0 = clumped distribution • s /mean = 1.0 = random distribution • s /mean < 1.0 = uniform distribution Statistics ∑ (x−X) 2 • Variance (mean deviation): s = n−1 2 • Standard deviation: STD =√s = ∑ (x−X) 2 √ n−1 STD • Standard error: SE = √N • Confidence Interval: CI = 2*SE • Upper CI = average + CI • Lower CI = average – CI Variance • Population Dispersion = Mean One measure is simply the sum of the deviations of each obs from the mean. But since this sum will always be zero, the average variance would always be zero as well. - The other method is to square each deviation and then sum the values. Squaring the deviations eliminates the zeros which means that the sum will be a positive, non-zero number that accurately reflects the degree of deviation (larger deviation means a larger value of variance and vice versa). The drawback is that the variance is a unit-less measure so it hard to equate it back to the original measurements. - SD is the square root of Variance which means that SD expresses deviation in the original units of measure. R
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