Introduction to Mathematical Statistics |
From inside the book
Results 1-3 of 18
Page 170
... significance level of a test ( or the size of the critical region ) is the power of the test when Ho is true , the significance level of this test is 0.05 . 0 = The fact that the power of this test , when = 4 , is only 0.31 ...
... significance level of a test ( or the size of the critical region ) is the power of the test when Ho is true , the significance level of this test is 0.05 . 0 = The fact that the power of this test , when = 4 , is only 0.31 ...
Page 179
... test which corresponds to this critical region should be at least as great as the power function of any other test with the same significance level for every simple hypothesis in H1 . Definition . The critical region C is a uniformly ...
... test which corresponds to this critical region should be at least as great as the power function of any other test with the same significance level for every simple hypothesis in H1 . Definition . The critical region C is a uniformly ...
Page 189
... significance level ? 9.22 . In Example 2 , let n = m = 8 , x ( Yi - y ) 2 = - = 8 75.2 , y = 78.6 , Σ ( x : — x ) 2 = 71.2 , 54.8 . If we use the test derived in that example , do we accept or reject Ho : 01 02 at the 5 per cent ...
... significance level ? 9.22 . In Example 2 , let n = m = 8 , x ( Yi - y ) 2 = - = 8 75.2 , y = 78.6 , Σ ( x : — x ) 2 = 71.2 , 54.8 . If we use the test derived in that example , do we accept or reject Ho : 01 02 at the 5 per cent ...
Contents
CHAPTER | 1 |
TRANSFORMATION OF VARIABLES cont | 17 |
CHAPTER | 27 |
Copyright | |
14 other sections not shown
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