An Introduction to Applied ProbabilityDesigned for a curriculum that contains only 2 single one-semester course on probability. Covers the core of probability theory, considers sums of random variables, derives sampling distributions, and discusses the approximation of distributions. Includes nonstatistical and statistical applications such as hypothesis testing, confidence intervals, and regression analysis. Numerous worked examples throughout the text illustrate the material and each chapter concludes with a number of problems. |
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A₁ approximation assumed average bivariate random variable Chapter components confidence interval consider continuous random variable control limits customers degrees of freedom density function discrete random variables distributed random variable distribution function distribution with parameter ensemble equation error event EXAMPLE exponentially distributed failure law failure rate function Find the pdf fx(x fy(y given hypothesis independent random variables integral joint pdf level of significance maximum likelihood estimate mean and variance Neyman-Pearson lemma normal random variable normally distributed number of defectives obtained OC curve outcome P₁ pdf f(x pdf's Poisson distribution Poisson process Poisson random variable population probability distribution problem queue random sample rejected S₁ sample mean sample space sampling plan shown in Figure theorem uniformly distributed variable with mean variable with parameter variance o² versus H₁ X₁ σχ