By Gary L. Tietjen
Statistics is the permitted physique of equipment for summarizing or describing info and drawing conclusions from the precis measures. everybody who has information to summarize therefore wishes a few wisdom of information. 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 autonomous definitions: it offers additionally the context, functions, and similar terminology. The meant viewers falls into 5 teams with relatively diversified wishes: (1) expert statisticians who have to keep in mind a definition, (2) scientists in disciplines except statistics who want to know the suitable equipment of summarizing information, (3) scholars of statistics who have to increase their knowl fringe of their subject material and make consistent connection with it, (4) managers who could be examining statistical reviews written via their staff, and (5) reporters who have to interpret govt or medical studies and transmit the data to the public.
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Extra info for A Topical Dictionary of Statistics
X iP ) , the Xij are known, fixed constants, measured without error or with negligible error. If the model is linear in the parameters, it is a linear model as opposed to a nonlinear or curvilinear model. (A model is linear in the parameter ~j only if ay/a~i is not a function of ~J. Thus y = ~o sin x is linear, but y = sin ~oX is nonlinear. In scalar notation we represent the i-th data point of a linear model as The expected value, E(Y;) = ~o + ~IX/ + ... + ~~jp, is interpretable Regression 49 as a regression hyperplane.
After plotting the points, he observed that, on the average, sons of tall fathers were not so tall as their fathers and sons of short fathers were not so short as their fathers. He called that tendency a regression toward the mean, and the term caught on and is now applied loosely to the estimation of almost any type of relationship in which I or more of the variables is random. Statisticians restrict the word to situations in which the dependent variable is random and the independent variables are fixed, mathematical variables.
The a' uniquely determined by g and a is denoted by g(a). Given the 0' and a' uniquely determined by an invariant decision problem, and a nonrandomized decision rule d(x), the decision rule is invariant if for every g in G and every x in the sample space, d[g(x)] = g[d(x)]. A class C of decision rules is complete (essentially complete) if C is a subset of D and if, given a rule d in D but not in C, there exists a rule do in C that is better than (as good as) d. The class C of decision rules is minimal complete if C is essentially complete and no proper subclass of C is essentially complete.
A Topical Dictionary of Statistics by Gary L. Tietjen