By Gary L. Tietjen
Statistics is the permitted physique of tools for summarizing or describing information and drawing conclusions from the precis measures. every person who has information to summarize hence 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 self sustaining definitions: it presents additionally the context, functions, and comparable terminology. The meant viewers falls into 5 teams with really varied wishes: (1) specialist statisticians who have to keep in mind a definition, (2) scientists in disciplines except facts who want to know the appropriate tools of summarizing info, (3) scholars of statistics who have to expand their knowl fringe of their subject material and make consistent connection with it, (4) managers who should be studying statistical reviews written through their staff, and (5) newshounds who have to interpret govt or medical stories and transmit the data to the public.
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Extra resources for A Topical Dictionary of Statistics
Addison-Wesley. The very important topic of nonparametric statistics is well covered in Conover, W. J. 1980. Practical Nonparametric Statistics. 2nd ed. New York: Wiley and Sons. 5 Regression A common problem is that of estimating a linear relationship between 2 variables, x and y. 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.
We assume a linear relationship between x and y. In that case x and yare random variables with a bivariate distribution. Galton selected a group of men and measured the height y of each man and the height x of his father. 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.
A desirable property of a decision rule is that it be invariant. Let P(O) be a family of distributions with 0 in a parameter space 8. The family of distributions is invariant under a group G of transformations if for every g in G and 0 in 8 there exists a unique 0' in 8 such that the distribution of g(X) is P(O') when the distribution of X is P(O). , L(O,a) = L(O' ,a'). 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)].