## Introduction to mathematical statistics |

### From inside the book

Results 1-3 of 29

Page 155

This reliance is reflected by calling the known interval (x — 2a/ Vn, x + 2a/ Vn) a

95.4 per cent

confidence coefficient. The confidence coefficient is equal to the probability that

the random ...

This reliance is reflected by calling the known interval (x — 2a/ Vn, x + 2a/ Vn) a

95.4 per cent

**confidence interval**for /x. The number 0.954 is called theconfidence coefficient. The confidence coefficient is equal to the probability that

the random ...

Page 158

and s|, will provide a 95 per cent

variances of the two independent normal distributions are unknown but equal. A

consideration of the difficulty encountered when the unknown variances of the

two ...

and s|, will provide a 95 per cent

**confidence interval**for pi1 — /x2 when thevariances of the two independent normal distributions are unknown but equal. A

consideration of the difficulty encountered when the unknown variances of the

two ...

Page 162

is a 95 per cent

variances. Example 3. If in the preceding discussion n = 10, m = 5, s2 = 20.0, s| =

35.6, then the interval or (0.4, 17.8) is a 95 per cent

is a 95 per cent

**confidence interval**for the ratio a%/aj of the two unknownvariances. Example 3. If in the preceding discussion n = 10, m = 5, s2 = 20.0, s| =

35.6, then the interval or (0.4, 17.8) is a 95 per cent

**confidence interval**for ct2/ct2.### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Other editions - View all

### Common terms and phrases

Accordingly best critical region binomial distribution cent confidence interval Chapter chi-square distribution complete sufficient statistic conditional p.d.f. confidence interval Consider continuous type converges stochastically critical region decision function defined degrees of freedom denote a random discrete type distribution having p.d.f. Equation Example EXERCISES F distribution function of Y1 given H0 is true independent random variables inequality integral joint p.d.f. Let the random Let X1 limiting distribution marginal p.d.f. matrix maximum likelihood moment-generating function mutually stochastically independent noncentral order statistics Poisson distribution positive integer power function Pr X1 probability density functions probability set function quadratic form random experiment random interval random sample random variables X1 reject H0 respectively sample space Show significance level simple hypothesis H0 statistic for 9 statistic Y1 stochastically independent random subset testing H0 theorem type of random unbiased statistic variance a2 X1 and X2 Xn denote zero elsewhere