Introduction to Probability Theory and Statistical InferenceDiscusses probability theory and to many methods used in problems of statistical inference. The Third Edition features material on descriptive statistics. Cramer-Rao bounds for variance of estimators, two-sample inference procedures, bivariate normal probability law, F-Distribution, and the analysis of variance and non-parametric procedures. Contains numerous practical examples and exercises. |
Contents
Set Theory | 1 |
Probability | 19 |
Random Variables and Distribution Functions | 92 |
Copyright | |
10 other sections not shown
Other editions - View all
Common terms and phrases
approximation balls Bernoulli random variable Bernoulli trials bulbs called confidence interval confidence limits continuous random variable d₁ d₂ defective defined degrees of freedom density function discrete random variable discussed distribution function equal error evaluate EXAMPLE Exercise expected value exponential random variable Figure flip Fx(t Fy(t given hypothesis independent Bernoulli trials independent random integer least squares Let X1 likelihood function linear maximum likelihood estimate mean µ moment generating function multinomial n-tuple normal random variable number of elements observed values occur P₁ Poisson random variable population possible posterior probability function Px(x quantile random sample random vector real number reject sample space sample values single-element events specified standard normal statistic subsets Suppose t₁ t₂ Table unbiased estimator unknown parameter variable with parameter variance o² versus H₁ X₁ X₂ x² random variable Y₁ Y₂ μ₁ σ² ΣΧ