This book features the general theory of uncertainty involving the combination (convolution) of non-Gaussian, student t, and Gaussian distributions, the use of rectangular distributions to represent systematic uncertainties, and measurable and nonmeasurable uncertainties that require estimation. The author also describes sources of measurement errors and curve fitting with numerous examples of uncertainty case studies as well as useful tables and computational formulae. With additional examples in each chapter, this second edition includes new chapters on the general theory of uncertainty and applications to industry and a new section that discusses the use of orthogonal polynomials in curve fitting.