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Given a function of the form y=Ax^B, (1) least squares fitting gives the coefficients as b = ...

A method of displaying simple statistical parameters including hinges, the statistical median, and upper and lower values.

L=sigma/(sigma_B), where sigma is the variance in a set of s Lexis trials and sigma_B is the variance assuming Bernoulli trials. If L<1, the trials are said to be subnormal, ...

A method for testing nested hypotheses. To apply the procedure, given a specific model, calculate the likelihood of observing the actual data. Then compare this likelihood to ...

Data resulting from the observation of a population on a number of variables over time. Whenever observations are made more than once, the data is considered to be ...

A robust estimation based on maximum likelihood arguments.

A sequence X_1, X_2, ... of random variates is called Markov (or Markoff) if, for any n, F(X_n|X_(n-1),X_(n-2),...,X_1)=F(X_n|X_(n-1)), i.e., if the conditional distribution ...

The statistical index P_(ME)=(sump_n(q_0+q_n))/(sum(v_0+v_n)), where p_n is the price per unit in period n, q_n is the quantity produced in period n, and v_n=p_nq_n is the ...

A matrix each of whose elements is a variate. These variates need not be independent, and if they are not, a correlation is said to exist between them.

A maximum likelihood estimator is a value of the parameter a such that the likelihood function is a maximum (Harris and Stocket 1998, p. 824).