Introduction to Mathematical Statistics |
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Page 155
... interval ( ~ – 20 / √n , x + 20 / √n ) a 95.4 per cent confidence interval for μ . The number 0.954 is called the confidence coefficient . The confidence coefficient is equal to the probability that the random interval includes the ...
... interval ( ~ – 20 / √n , x + 20 / √n ) a 95.4 per cent confidence interval for μ . The number 0.954 is called the confidence coefficient . The confidence coefficient is equal to the probability that the random interval includes the ...
Page 158
... confidence interval for μ1 — μ2 when the variances of the two independent normal distributions are unknown but equal . A consideration of the difficulty ... Intervals for Variances n Let the random variable 158 [ Ch . 5 Interval Estimation.
... confidence interval for μ1 — μ2 when the variances of the two independent normal distributions are unknown but equal . A consideration of the difficulty ... Intervals for Variances n Let the random variable 158 [ Ch . 5 Interval Estimation.
Page 162
... confidence interval for o / o . EXERCISES 5.14 . If 8.6 , 7.9 , 8.3 , 6.4 , 8.4 , 9.8 , 7.2 , 7.8 , 7.5 are the observed values of a random sample of size ... interval estimation . 162 [ Ch . 5 Interval Estimation Bayesian Interval Estimates.
... confidence interval for o / o . EXERCISES 5.14 . If 8.6 , 7.9 , 8.3 , 6.4 , 8.4 , 9.8 , 7.2 , 7.8 , 7.5 are the observed values of a random sample of size ... interval estimation . 162 [ Ch . 5 Interval Estimation Bayesian Interval Estimates.
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A₁ A₂ accept accordance Accordingly alternative approximately assume called cent Chapter complete compute Consider constant continuous type critical region decision defined definition degrees of freedom denote a random depend determine discrete type distribution function equal Equation event Example EXERCISES exists expected fact given H₁ Hence hypothesis inequality integral interval joint p.d.f. Let X1 likelihood marginal matrix maximum mean moment-generating function mutually stochastically independent normal distribution Note observed order statistics parameter probability density functions problem proof prove random experiment random interval random sample random variable ratio reject respectively result sample space Show significance level simple hypothesis stochastically independent sufficient statistic symmetric matrix Table theorem transformation true unknown variables X1 variance W₁ X₁ X₂ Y₁ Y₂ zero elsewhere μ₁