## Introduction to Mathematical Statistics |

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Page 191

At this point a theorem in

language of

special case is taken, adequate for the purposes of this book, the theorem may

be ...

At this point a theorem in

**matrix**algebra is helpful. When translated from thelanguage of

**matrices**into the language of probability and statistics, and when aspecial case is taken, adequate for the purposes of this book, the theorem may

be ...

Page 219

Our answer is that, for the problems under consideration, it was easier to avoid

computing co Co –4 Mx,y(t1, t2) = s s e”——e #"dr dy —oo - -oo 2tr0102 V1 — p”

-o-o-o] on 1 Without the use of the algebra of

Our answer is that, for the problems under consideration, it was easier to avoid

computing co Co –4 Mx,y(t1, t2) = s s e”——e #"dr dy —oo - -oo 2tr0102 V1 — p”

-o-o-o] on 1 Without the use of the algebra of

**matrices**, it is. 1 [or-2,4-o-o-o-o].Page 220

Without the use of the algebra of

that oi” tho-H2 poi as to ta-Fos” ts” Mx,y(t1, to) - .” + —g- - In Section 11.3 there

will be investigated a test of the hypothesis that two random variables having a ...

Without the use of the algebra of

**matrices**, it is indeed a tedious chore to showthat oi” tho-H2 poi as to ta-Fos” ts” Mx,y(t1, to) - .” + —g- - In Section 11.3 there

will be investigated a test of the hypothesis that two random variables having a ...

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### Common terms and phrases

Accordingly best critical region binomial distribution cent confidence interval chi-square distribution composite hypothesis conditional p.d.f. confidence interval Consider continuous type degrees of freedom denote a random discrete type distribution function distribution having p.d.f. distribution with mean ExAMPLE Exercises f(zi function F(z hypothesis H1 independent random variables integral Jacobian joint p.d.f. joint sufficient statistics Let the random Let X1 limiting distribution marginal p.d.f. moment-generating function mutually stochastically independent Mx(t My(t normal distribution n(z null hypothesis null simple hypothesis one-to-one transformation order statistics p.d.f. of Yi Poisson distribution positive integer Pr(a Pr(X Pr(Y probability density functions quadratic form random experiment random interval random sample random variables X1 respectively ſ ſ sample space Show significance level ſº stochastically independent random subset sufficient statistic Yi theorem tion type of random unbiased statistic values X1 and X2 Xn denote zero elsewhere