Generally, when you have a clumped distribution, you need to sample more intensively to get a good estimate of average density, and many small samples is often better than a few large ones. a large sample plot land on an area with no occurences, which might bias your density estimate. However, I should also note that you should be cautious about using a weighted mean when you have a strongly clumped distribution, since your run the risk that e.g. The CV using the arithmetric mean is 1.5, while it is 2.5-2.65 for the weighted mean, which is reasonable since you are giving more weight to the large area sample with a zero count.

If I haven't made a mistake, these will give the standard deviations 1.15 and 1.22, using your data.ДЄs for the biological interpretation, all calculations of average densities indicate a clumped distribution, since the coefficient of variation (CV) is larger than 1 (CV = sd/mean), but more so if you use a weighted mean. The first way averages the density at each plot:

What is this species' density? I see two ways to calculate the density that give completely different values. Lets imagine 5 plots of different size are sampled for a target species: plot# count area(m^2) plot_density Here we discuss the various examples of standardВ Deviation along with a detailed explanation. This has been a guide to the standardВ Deviation Examples. There are various other examples as well, which shows that Standard deviation can be calculated using other data. The above-mentioned examples are some of the examples of Standard deviation in different ways. The greater is the standard deviation of the security, the greater will be the variance between each of the prices and the mean, which shows that the price range is large. It is calculated as the square root of variance. The standard deviation measures the dispersion of the dataset, which is relative to its mean. The standard deviation is a commonly used measure of the degree of variation within a set of data values. Diversification leads to a reduction in the risk unless a perfect correlation exists between returns on portfolio investments. Base R has a function you can use to calculate standard deviation in R. The portfolio standard deviation is lower than for either stockвЂ™s individual because the stocks are diversified in different stocks. Standard Deviation of Portfolio= 56.14%.Standard Deviation of Portfolio = (Weight of Company A * Expected Return of Company A) + ((Weight of Company B * Expected Return of Company B) The formula of Standard Deviation of Portfolio HPR (Recession) = ((4-93)+2)/93 = -93.55%Д®xpected Return=(Probability of Boom * Return from Boom) + (Probability of Normal * Return from Normal) + (Probability of Recession * Return from Recession) HPR (Recession) = ((38-28) + 5)/28 = 53.57%Д¬alculation of Expected Return of Company AД®xpected Return = (Probability of Boom * Return from Boom) + (Probability of Normal * Return from Normal) + (Probability of Recession * Return from Recession).HPR = ((End of Period Value вЂ“ Original Value) + Income) / Original Value) * 100

(c) Calculate the standard deviation of the portfolio if half of the investment is done in Company A and the rest half in Company BД¬alculations of Holding Period Return (HPR) (b) Calculate the standard deviation of Company B (a) Calculate the standard deviation of Company A The common stock of Company B sells for $ 93 per share and the same offers following payoffs for the next year:

The common stock of Company A sells for $ 28 per share and the same offers following payoffs for the next year Standard deviation in the case of two companies in the portfolio Standard DeviationВ will be Square Root ofВ Variance Expected Return = (Probability of Boom * Return from Boom) + (Probability of Good * Return from Good) + (Probability of Normal * Return from Normal) + (Probability of Recession * Return from Recession)

Weighted standard deviation in rpackage