By Gary L. Tietjen
Statistics is the permitted physique of tools for summarizing or describing info and drawing conclusions from the precis measures. every person who has facts to summarize therefore wishes a few wisdom of facts. step one in gaining that wisdom is to grasp the pro jargon. This dictionary is geared to supply greater than the standard string of remoted and self reliant definitions: it presents additionally the context, purposes, and comparable terminology. The meant viewers falls into 5 teams with really various wishes: (1) expert statisticians who have to remember a definition, (2) scientists in disciplines except information who want to know the appropriate equipment of summarizing info, (3) scholars of records who have to expand their knowl fringe of their subject material and make consistent connection with it, (4) managers who can be interpreting statistical experiences written by way of their staff, and (5) newshounds who have to interpret executive or medical reviews and transmit the data to the public.
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Extra resources for A Topical Dictionary of Statistics
An estimator T is admissible if and only if there is no other estimate better than T. An estimator is minimax if its maximum risk over all 8 is less than or equal to the maximum risk of any other estimator. The Bayes risk of an estimator is the average (expected value) of the risk, the averaging being taken over the parameter space with respect to the prior distribution of 8. For a given loss function and prior density, the Bayes estimator of 8 is the estimator with smallest Bayes risk. Bayesian methods provide a formal way of combining some notions about the uncertainties in the parameters (through the prior) with the data to obtain better information about the parameters (expressed through the posteriors).
If an electrician charges a flat fee 130 plus a fixed amount 131 per outlet when he wires a house, the relationship can be expressed as y = 130 + 131x, where y is his total fee and x is the number of outlets. There is no error in the fee because we can count the number of outlets. For a given number of outlets the fee is invariably the same. , they do not have distributions. When y is plotted against x (for any fixed value of 130 and 131), the points fall on a straight line with slope 131 and intercept 130 and we say that the relationship is linear.
That requires thatf(x;a) be expressible as (l/a)h(xla) for some density h(x) in which awill equal I. A shape parameter is a parameter in a pdf that is neither a location nor a scale parameter; it will affect the shape of the members of the family of distributions. 2 exp( -x2 /2) is a density. For the same reason, (T2 is a scale parameter. In the gamma distribution the parameter r is a shape parameter. :; a+'A) for all 'A> and all a. An estimator is most concentrated if it is more concentrated than any other estimator.