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If the dependent variable is referred to as an "explained variable" then the term "predictor variable" is preferred by some authors for the independent variable. [22] An example is provided by the analysis of trend in sea level by Woodworth (1987). Here the dependent variable (and variable of most interest) was the annual mean sea level at a ...
The rate at which a population increases in size if there are no density-dependent forces regulating the population is known as the intrinsic rate of increase. It is d N d t = r N {\displaystyle {\mathrm {d} N \over \mathrm {d} t}=rN} where the derivative d N / d t {\displaystyle dN/dt} is the rate of increase of the population, N is the ...
Density-dependent fecundity. Density-dependent fecundity exists, where the birth rate falls as competition increases. In the context of gastrointestinal nematodes, the weight of female Ascaris lumbricoides and its rates of egg production decrease as host infection intensity increases. Thus, the per-capita contribution of each worm to ...
The saddlepoint approximation method, initially proposed by Daniels (1954) [1] is a specific example of the mathematical saddlepoint technique applied to statistics, in particular to the distribution of the sum of independent random variables.
when joint probability density function between two random variables is known, the copula density function is known, and one of the two marginal functions are known, then, the other marginal function can be calculated, or
Centered on each sample, a Gaussian kernel is drawn in gray. Averaging the Gaussians yields the density estimate shown in the dashed black curve. In statistics, probability density estimation or simply density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The ...
In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...
The goal of density estimation is to take a finite sample of data and to make inferences about the underlying probability density function everywhere, including where no data are observed. In kernel density estimation, the contribution of each data point is smoothed out from a single point into a region of space surrounding it.