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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accepted apply approximation arrivals assumed average binomial bivariate calculated called Chapter choose chosen components conditional consider constant contains continuous customers defective defined degrees of freedom density determine distribution elsewhere equation error estimate event EXAMPLE expected experiment exponential expression fact failure Figure Find function given gives hypothesis independent independent random variables integral interval known least less likelihood limits maximum mean measurements normal random variable normally distributed observations obtained occurs outcome P₁ parameter particular Poisson population possible probability problem properties queue referred region reject reliability respectively result sample mean sample space shown simple single situation square standard tables term true Type unknown variable with parameter variance versus H₁ yields