## 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. |

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accept actually approximation Assume assumptions average balls Bernoulli binomial called compute conditional confidence confidence interval confidence limits constant contains continuous defective defined DEFINITION degrees of freedom density function derive described discrete discussed distribution function elements equal equivalent error estimate evaluate event EXAMPLE Exercise experiment exponential fact failure Figure frequently Fx(t given gives H₁ hypothesis independent interval least linear marginal maximum likelihood mean measure normal normal random variable observed values occur outcomes parameter Poisson population positive possible prior probability function probability law random sample random variable range reasonable reject respectively result sample space selected Show simple specified squares statistic subsets success Suppose Table Theorem trials true unbiased variable with parameter variance versus X₁ Y₁ Y₂