Introduction to Mathematical Statistics |
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Page 59
... interval [ ≈ — ( 20 / √√ñ ) , x + ( 20 / √n ) ] has known end points . Obviously we cannot say that 0.95 is the ... confidence interval for u . The number 0.95 is called the confidence co- efficient . The confidence coefficient is ...
... interval [ ≈ — ( 20 / √√ñ ) , x + ( 20 / √n ) ] has known end points . Obviously we cannot say that 0.95 is the ... confidence interval for u . The number 0.95 is called the confidence co- efficient . The confidence coefficient is ...
Page 129
... interval ( with prescribed confidence coefficient ) for o2 . The fact that nS2 / 2 has a chi - square dis- tribution with n − 1 degrees of freedom , whatever the ... CONFIDENCE INTERVALS FOR VARIANCES Confidence Intervals for Variances.
... interval ( with prescribed confidence coefficient ) for o2 . The fact that nS2 / 2 has a chi - square dis- tribution with n − 1 degrees of freedom , whatever the ... CONFIDENCE INTERVALS FOR VARIANCES Confidence Intervals for Variances.
Page 133
... interval [ Ã – ( bS / √ n - - 1 1 ) , X + ( bS / √ n = - 0.95 , 1 ) ] is a random interval having probability 0.95 of including the unknown fixed point ( parameter ) μ . If the experimental values of X1 ... CONFIDENCE INTERVALS FOR MEANS.
... interval [ Ã – ( bS / √ n - - 1 1 ) , X + ( bS / √ n = - 0.95 , 1 ) ] is a random interval having probability 0.95 of including the unknown fixed point ( parameter ) μ . If the experimental values of X1 ... CONFIDENCE INTERVALS FOR MEANS.
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