Statistical Models in EngineeringA detailed treatment on the use of statistical models representing physical phenomena. Considers the relevance of the popular normal distribution models and the applicability of exponential distribution in reliability problems. Introduces and discusses the use of alternate models such as gamma, beta and Weibull distributions. Features expansive coverage of system performance and describes an exact method known as the transformation of variables. Deals with techniques on assessing the adequacy of a chosen model including both graphical and analytical procedures. Contains scores of illustrative examples, most of which have been adapted from actual problems. |
Contents
Introduction | 1 |
for a Continuous Random Variable | 29 |
Continuous Statistical Distributions | 68 |
Copyright | |
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95 per cent applicable assumed model asymptotic distribution b₁ b₂ beta distribution binomial distribution Biometrika calculated cent confidence cent of observations Chapter chi-square component variables continuous random variable corresponding cumulative distribution cumulative probabilities discrete random variable discussed distribution approximation E. S. Pearson Engineering Equation estimate evaluate expected value exponential distribution extreme value distribution failure following example frequently gamma distribution given data given in Reference hazard function independent John Wiley Johnson joint probability known log-normal distribution methods minutes minutes minutes Normal deviates normal distribution obtained occur outcomes Pearson distribution percentiles points Poisson distribution probability density function probability function probability paper probability plots problem procedure random value reliability represent sample space shown in Fig specified standard normal system performance Table tabulated theoretical Theory tossing trials Type I asymptotic uniform distribution variate Weibull distribution Wiley and Sons x₁ Y₁ York