## Introduction to Mathematical Statistics |

### From inside the book

Results 1-3 of 53

Page 55

And in the latter part of Example 5 we investigated some of the properties of the

distribution of an arbitrary linear function Y = k1A 1 + k2X2 + . ... For example,

suppose X1, X2, ..., Xn is a random sample from some

1).

And in the latter part of Example 5 we investigated some of the properties of the

distribution of an arbitrary linear function Y = k1A 1 + k2X2 + . ... For example,

suppose X1, X2, ..., Xn is a random sample from some

**normal distribution n**(**z**; u,1).

Page 57

Let X1, X2, Xs be a random sample of size 3 from a

Determine the probability that the largest sample item exceeds 8. (3.27. Show

that the moment-generating function of the sum Y of the items of a random

sample ...

Let X1, X2, Xs be a random sample of size 3 from a

**normal distribution n**(**z**; 6, 4).Determine the probability that the largest sample item exceeds 8. (3.27. Show

that the moment-generating function of the sum Y of the items of a random

sample ...

Page 132

Let two independent random samples of sizes n = 16 and m = 10, taken from two

independent

yield £ = 3.6, 81° = 4.14, j = 13.6, 83° = 7.26. Find a 90 per cent confidence ...

Let two independent random samples of sizes n = 16 and m = 10, taken from two

independent

**normal distributions n**(**z**; ul, a 1°) and n(y; us, a 3%), respectively,yield £ = 3.6, 81° = 4.14, j = 13.6, 83° = 7.26. Find a 90 per cent confidence ...

### 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 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