By Gary L. Tietjen
Statistics is the accredited physique of equipment for summarizing or describing info and drawing conclusions from the precis measures. everybody who has facts to summarize hence wishes a few wisdom of statistics. 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, functions, and comparable terminology. The meant viewers falls into 5 teams with particularly assorted wishes: (1) specialist statisticians who have to keep in mind a definition, (2) scientists in disciplines except records who want to know the appropriate equipment of summarizing information, (3) scholars of facts who have to develop their knowl fringe of their subject material and make consistent connection with it, (4) managers who could be analyzing statistical experiences written by means of their staff, and (5) reporters who have to interpret govt or medical stories and transmit the knowledge to the public.
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Extra resources for A Topical Dictionary of Statistics
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. 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.
Mean squared error is widely used as a standard for the goodness of an estimator, but it depends, in general, upon 6. Hence, estimators with uniformly (for all 6) minimum mean square error seldom exist. A minimum variance or best estimator in a class of estimators is one that has a smaller variance than any other estimator in the class. A linear estimator is a linear combination of the observations. A BLUE estimator is best in the class of linear unbiased estimators, and a MULE estimator is the same thing: a minimum variance unbiased linear estimator.
The probability thus reflects the confidence we have in the procedure: Most of the time it yields an interval that will capture the parameter. A (1 - a) 100 percent confidence interval is an interval for an unknown parameter e constructed from a sample in such a way that if the same method were used to construct a "large" number of such intervals from independent samples, (1 - a) 100 percent of the intervals would contain the parameter e. The term (1- a) is called the confidence coefficient or confidence level.