Introduction to Mathematical Statistics |
From inside the book
Results 1-3 of 81
Page 2
... called the relative frequency of the event A in these N experiments . A relative frequency is usually quite erratic ... called the probability that the outcome of the random experiment is in A ; sometimes it is called the proba- bility ...
... called the relative frequency of the event A in these N experiments . A relative frequency is usually quite erratic ... called the probability that the outcome of the random experiment is in A ; sometimes it is called the proba- bility ...
Page 16
... called the probability density function . The two types of distributions that we shall describe by a probability density function are called , respectively , the discrete type and the continuous type . For simplicity of presentation ...
... called the probability density function . The two types of distributions that we shall describe by a probability density function are called , respectively , the discrete type and the continuous type . For simplicity of presentation ...
Page 17
... called the probability density function . The two types of distributions that we shall describe by a probability density function are called , respectively , the discrete type and the continuous type . For simplicity of presentation ...
... called the probability density function . The two types of distributions that we shall describe by a probability density function are called , respectively , the discrete type and the continuous type . For simplicity of presentation ...
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