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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approximation assumed average binomial distribution bivariate random variable cell Chapter chart coefficient 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 experiment exponentially distributed failure law failure rate function Find the pdf Find the probability fx(x 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 obtained OC curve outcomes P₁ pdf f(x pdf's Poisson distribution Poisson random variable population probability distribution problem queue reliability sample mean sample space sampling plan shown in Figure simple theorem uniformly distributed variable with parameter variance o² versus H₁ X₁ Y₁ μο