Introduction to Mathematical Statistics |
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Page 9
... random experiment is an element of a set . Let A be the sample space of a random experiment . If A is one - dimensional , one random variable X is defined on this space ; if A is two - dimensional , two random variables X and Y are ...
... random experiment is an element of a set . Let A be the sample space of a random experiment . If A is one - dimensional , one random variable X is defined on this space ; if A is two - dimensional , two random variables X and Y are ...
Page 50
... random experiment is to be repeated n independent times , and on each repetition the experiment will terminate in one of two mutually exclusive ways , say , success or failure . Assume further that the probability of success , say , p ...
... random experiment is to be repeated n independent times , and on each repetition the experiment will terminate in one of two mutually exclusive ways , say , success or failure . Assume further that the probability of success , say , p ...
Page 165
... random experiment has the p.d.f. f ( x ; 0 ) = 1 , 0 < x < 0 , = O elsewhere . Past experience with this random experiment indicated that = 1. However , it is suspected , due possibly to some changes made in the method of performing the ...
... random experiment has the p.d.f. f ( x ; 0 ) = 1 , 0 < x < 0 , = O elsewhere . Past experience with this random experiment indicated that = 1. However , it is suspected , due possibly to some changes made in the method of performing the ...
Contents
CHAPTER | 1 |
TRANSFORMATION OF VARIABLES cont | 17 |
CHAPTER | 27 |
Copyright | |
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Common terms and phrases
A₁ A₂ best critical region binomial distribution c₁ cent confidence interval chi-square distribution composite hypothesis conditional p.d.f. confidence interval Consider continuous type critical region degrees of freedom denote a random discrete type distribution function distribution having p.d.f. distribution with mean EXAMPLE Exercises function of Y₁ hypothesis H₁ independent random variables integral Jacobian joint p.d.f. joint sufficient statistics Let the random Let X1 Let Y₁ limiting distribution marginal p.d.f. moment-generating function mutually stochastically independent Mx(t My(t normal distribution n(x null hypothesis null simple hypothesis one-to-one transformation order statistics p.d.f. of Y₁ p₁ Poisson distribution positive integer Pr(a Pr(X Pr(Y probability density functions r₁ random experiment random sample random variables X1 Show significance level statistical hypothesis stochastically independent random theorem type of random unbiased statistic values variance o² X₁ X1 and X2 X₂ Xn denote Y₂ zero elsewhere μ₁ σ²