ORIGINAL: NYSHunter

Table 1 states that their count for their particular sample in PA the 52% male newborn. Tbale 2 shows male newborn at 15,129 and female newborn at 13,965. Statically, it means about 50/50 because the sampleing must have some error rates, but its one study and a particular sample. By no means does it state that all fawns are 50% buck and 50% does. It was just some reasearch that I though some would like to review.

ORIGINAL: NYSHunter

Phade,

Ask that question to any statistics professor that and they will disagree with you. 52% of a sample within a prescribe error rate supports 50/50. If you want to discuss the Z score and standard deviation formula we can at great length.

ORIGINAL: NYSHunter

Phade,
I did just buy "The Deer of North America" by Leonard Lee Rue and on page 250 he discusses the ratios of the sex of fawns. He suggests that sex ratios are determined by the quality of the habitat.His ratios were never 50/50, even statically. The swing in higher buck birthing rates or higher doe birthing rates is quite pronounced. Very interesting reading.

How to measure the magnitude (strength) of relations between variables. There are very many measures of the magnitude of relationships between variables which have been developed by statisticians; the choice of a specific measure in given circumstances depends on the number of variables involved, measurement scales used, nature of the relations, etc. Almost all of them, however, follow one general principle: they attempt to somehow evaluate the observed relation by comparing it to the "maximum imaginable relation" between those specific variables. Technically speaking, a common way to perform such evaluations is to look at how differentiated are the values of the variables, and then calculate what part of this "overall available differentiation" is accounted for by instances when that differentiation is "common" in the two (or more) variables in question. Speaking less technically, we compare "what is common in those variables" to "what potentially could have been common if the variables were perfectly related." Let us consider a simple illustration. Let us say that in our sample, the average index of WCC is 100 in males and 102 in females. Thus, we could say that on average, the deviation of each individual score from the grand mean (101) contains a component due to the gender of the subject; the size of this component is 1. That value, in a sense, represents some measure of relation between Gender and WCC. However, this value is a very poor measure, because it does not tell us how relatively large this component is, given the "overall differentiation" of WCC scores. Consider two extreme possibilities:[*]If all WCC scores of males were equal exactly to 100, and those of females equal to 102, then all deviations from the grand mean in our sample would be entirely accounted for by gender. We would say that in our sample, gender is perfectly correlated with WCC, that is, 100% of the observed differences between subjects regarding their WCC is accounted for by their gender.[*]If WCC scores were in the range of 0-1000, the same difference (of 2) between the average WCC of males and females found in the study would account for such a small part of the overall differentiation of scores that most likely it would be considered negligible. For example, one more subject taken into account could change, or even reverse the direction of the difference. Therefore, every good measure of relations between variables must take into account the overall differentiation of individual scores in the sample and evaluate the relation in terms of (relatively) how much of this differentiation is accounted for by the relation in question.