An Introduction to Applied Probability
Designed 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 binomial distribution binomial random variable bivariate random variable Chapter code word components confidence interval confidence limits consider continuous random variable control limits customers degrees of freedom density function determine discrete random variables distributed random variable distribution with parameter ensemble equation error event EXAMPLE experiment exponentially distributed failure law failure rate function Find the pdf Find the probability fx(x gamma given independent random variables integral joint pdf large numbers level of significance linear maximum likelihood estimate mean and variance Neyman-Pearson lemma normal pdf normal random variable normally distributed normally distributed random number of defectives obtained OC curve outcome pdf's Poisson distribution Poisson process Poisson random variable population problem queue random sample reject H0 result sample mean sample space server shown in Figure simple theorem Type uniformly distributed variable with mean variable with parameter versus zero