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The fourth source for distribution data was the detailed distribution maps of Grey-Wilson [ 26].
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Following Kolmogorov-Smirnov testing for normality of distribution, data were analyzed by parametric and non-parametric tests, as appropriate.
Deviances were calculated by assuming the Poisson likelihood for incidence data and by assuming multinomial likelihood for stage distribution data.
The control law for developing optimal strategy are achieved by cluster regression for power distribution data.
For exponential distribution data, averages were given as median ± SEM and statistical significance was tested using the Kolmogorov-Smirnov test.
Following Kolmogoronov-Smirnov test for normal distribution, data were analyzed parametrically.
Flow cytometry and ELISA data were analyzed by unpaired Student's t-test for normal distribution data which were expressed as the mean ± SD, or Mann–Whitney test for abnormal distribution data which were expressed as median (range).
For normal distribution data, differences between two groups were assessed by unpaired Student's t test, and comparison of multiple groups involved the use of ANOVA.
For last-mile distribution data, UNICEF needs to simplify reporting formats and develop mechanisms for data gathering by and from partners.
Descriptive statistics were presented as means ± standard deviation or percentages and median with range for non-normal distribution data.
We thank L. Hunter and J. C. Ray of the Wildlife Conservation Society for providing the modern cheetah distribution data and K. Minkowski for providing the modern lion distribution data.
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