Introduction to Mathematical Statistics |
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Page 17
... continuous type of random variable ( s ) ] or its sum [ for the discrete type of random variable ( s ) ] over all real values of its argument ( s ) is one . If f ( x ) is the p.d.f. of a continuous type of random variable X and if A is ...
... continuous type of random variable ( s ) ] or its sum [ for the discrete type of random variable ( s ) ] over all real values of its argument ( s ) is one . If f ( x ) is the p.d.f. of a continuous type of random variable X and if A is ...
Page 20
... continuous type of random variable ( s ) ] or its sum [ for the discrete type of random variable ( s ) ] over all real values of its argument ( s ) is one . If f ( x ) is the p.d.f. of a continuous type of random variable X and if A is ...
... continuous type of random variable ( s ) ] or its sum [ for the discrete type of random variable ( s ) ] over all real values of its argument ( s ) is one . If f ( x ) is the p.d.f. of a continuous type of random variable X and if A is ...
Page 57
... continuous type of random variable . It is called the conditional p.d.f. of the continuous type of random variable X2 , given that the continuous type of random variable X1 has the value x1 . When ƒ2 ( x2 ) > 0 , the conditional p.d.f. ...
... continuous type of random variable . It is called the conditional p.d.f. of the continuous type of random variable X2 , given that the continuous type of random variable X1 has the value x1 . When ƒ2 ( x2 ) > 0 , the conditional p.d.f. ...
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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