New PDF release: A Topical Dictionary of Statistics

By Gary L. Tietjen

ISBN-10: 1461291682

ISBN-13: 9781461291688

ISBN-10: 1461319676

ISBN-13: 9781461319672

Statistics is the accredited physique of equipment for summarizing or describing info and drawing conclusions from the precis measures. every person who has info to summarize hence wishes a few wisdom of facts. step one in gaining that wisdom is to grasp the pro jargon. This dictionary is geared to provide greater than the standard string of remoted and self sustaining definitions: it offers additionally the context, purposes, and similar terminology. The meant viewers falls into 5 teams with fairly varied wishes: (1) specialist statisticians who have to keep in mind a definition, (2) scientists in disciplines except records who want to know the suitable tools of summarizing information, (3) scholars of information who have to increase their knowl­ fringe of their material and make consistent connection with it, (4) managers who can be studying statistical reviews written via their staff, and (5) newshounds who have to interpret govt or clinical stories and transmit the data to the public.

Similar statistics books

Download PDF by Roger Tarling: Statistical Modelling for Social Researchers: Principles and

This e-book explains the foundations and thought of statistical modelling in an intelligible manner for the non-mathematical social scientist trying to follow statistical modelling innovations in learn. The booklet additionally serves as an creation for these wishing to advance extra certain wisdom and abilities in statistical modelling.

Read e-book online Dependence Modeling: Vine Copula Handbook PDF

This booklet is a collaborative attempt from 3 workshops held during the last 3 years, all related to critical participants to the vine-copula technique. study and functions in vines were becoming swiftly and there's now a starting to be have to collate simple effects, and standardize terminology and strategies.

Additional info for A Topical Dictionary of Statistics

Sample text

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. Most concentrated estimators do not generally exist. The estimator T' is Pitnuin closer than T if p(lT' - al < IT - al) ;:,: 1/2 for all S. An estimator is Pitman closest if it is Pitman closer than any other estimator of S. We now classify several types of estimators according to the method used in finding them.

The normal, gamma, Poisson, and Cauchy random variables are infinitely divisible. We need to speak here about the subject of convergence in order to define the Central Limit Theorem and related terms. If we have a sequence of points, Xi> X2 , . • • , we say that the sequence converges to a point p if there is an N such that for all n>N, Xn is closer than E to p, where E is some small positive number. A sequence offunctionsfl(x),fz{x), ... converges pointwise to a functionj(x) if for every given Xo the sequence of pointsfl(xo),fz{xo), ...

A model is additive if it contains no interaction terms and nonadditive if it does. For the sake of conciseness the linear model may be written in matrix notation as Y = YP + E, where Y is the n x 1 vector of observations, X is an nx(p + 1) matrix of known constants, P is the (p + 1) x 1 vector of parameters and E then n x 1 vector of random errors. The matrix X is the design matrix, model matrix, data matrix, or incidence matrix, depending on the context. A complete description of the model depends on the assumptions made about the vector E.