Introduction to Mathematical Statistics |
From inside the book
Results 1-3 of 19
Page 260
... significance level of the test and the power of the test when Ho is false . 10.4 . Sketch , as in Figure 10.1 , the graphs of the power functions of Tests 1 , 2 , and 3 of Example 1 of this section . = 10.5 . Let us assume that the life ...
... significance level of the test and the power of the test when Ho is false . 10.4 . Sketch , as in Figure 10.1 , the graphs of the power functions of Tests 1 , 2 , and 3 of Example 1 of this section . = 10.5 . Let us assume that the life ...
Page 306
... level of significance , by a chi - square test . If the observed frequencies of the sets A ,, i = 1 , 2 , ... , 8 , are respectively 60 , 96 , 140 , 210 , 172 , 160 , 88 , and 74 , would Ho be accepted at the ( approximate ) 5 per cent .
... level of significance , by a chi - square test . If the observed frequencies of the sets A ,, i = 1 , 2 , ... , 8 , are respectively 60 , 96 , 140 , 210 , 172 , 160 , 88 , and 74 , would Ho be accepted at the ( approximate ) 5 per cent .
Page 338
... significance level of this sequence με ... = = με = of tests of the equality of means is b - 1 α = 1 II ( 1 α . ) . i = 1 με Но απ This means that , if μ1 = μ2 is rejected , using W1 , at significance level a1 , then Ho : = μ is ...
... significance level of this sequence με ... = = με = of tests of the equality of means is b - 1 α = 1 II ( 1 α . ) . i = 1 με Но απ This means that , if μ1 = μ2 is rejected , using W1 , at significance level a1 , then Ho : = μ is ...
Other editions - View all
Common terms and phrases
A₁ A₂ Accordingly best critical region c₁ cent confidence interval chi-square distribution complete sufficient statistic compute conditional p.d.f. confidence interval Consider continuous type critical region decision function defined degrees of freedom denote a random discrete type distribution function distribution having p.d.f. Equation Example EXERCISES function F(x given H₁ hypothesis H independent random variables integral joint p.d.f. k₁ Let X1 Let Y₁ limiting distribution marginal p.d.f. matrix maximum likelihood moment-generating function mutually stochastically independent noncentral normal distribution order statistics p.d.f. of Y₁ P(A₁ p₁ Poisson distribution positive integer probability density functions quadratic form random experiment random interval random sample random variables X1 respectively sample space Show significance level simple hypothesis statistic Y₁ stochastically independent random sufficient statistic theorem unbiased statistic variance o² w₁ X₁ X₁ and X2 X₂ Y₂ Z₁ zero elsewhere μ₁ μ₂ Σ Σ