## Introduction to Mathematical Statistics |

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Page 26

The preceding discussion may be summarized in the following manner: A

continuous to the right and has F(–oo) = 0, F(OO) = 1. The probability Pr(a X → b)

is ...

The preceding discussion may be summarized in the following manner: A

**distribution function**F(z) is a nondecreasing function of ac, which is everywherecontinuous to the right and has F(–oo) = 0, F(OO) = 1. The probability Pr(a X → b)

is ...

Page 187

Since the moment-generating

depends upon n. It is true that various mathematical techniques can be used to

determine the p.d.f. of X for a fixed, but arbitrarily fixed, positive integer n. But the

...

Since the moment-generating

**function**of X depends upon n, the**distribution**of Xdepends upon n. It is true that various mathematical techniques can be used to

determine the p.d.f. of X for a fixed, but arbitrarily fixed, positive integer n. But the

...

Page 193

7.3 Limiting Moment-Generating Functions To find the limiting

indicated ...

7.3 Limiting Moment-Generating Functions To find the limiting

**distribution****function**of a random variable Y by use of the definition of limiting**distribution****function**obviously requires that we know FA(y) for each positive integer n. But, asindicated ...

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accept accordance Accordingly alternative approximately assume called cent Chapter complete compute conditional confidence interval Consider constant continuous type critical region decision defined definition degrees of freedom denote a random depend determine discrete type distribution function equal Equation event Example EXERCISES exists expected fact given Hence inequality integral interval joint p.d.f. Let X1 likelihood marginal matrix maximum mean moment-generating function mutually stochastically independent normal distribution Note observed order statistics outcome parameter Pr(X probability density functions problem proof prove random experiment random interval random sample random variable ratio reject respectively result sample space Show significance level simple hypothesis ſº stochastically independent sufficient statistic symmetric matrix Table theorem transformation true unknown variance write X1 and X2 zero elsewhere