(3 units). Combinatorial analysis; probability and random variables; discrete and continuous densities and distribution functions; expectation and variance; normal (Gaussian), distributions; statistical estimation and hypothesis testing; method of least squares, correlation and regression. Basic principles and techniques for the efficient design of experiments and effective analysis of data. Topics include: the nature and analysis of process variability, comparing processes, blocking and randomization, empirical model building for quantifying relationships between process inputs and outputs, two-level factorial and fractional factorial designs for screening out inert input variables, other designs, a practical approach to experimental design. Course Component: Lecture, Tutorial Prerequisites: CHG 1125, CHG 1371, MAT 2384.
(3 units). Combinatorial analysis; probability and random variables; discrete and continuous densities and distribution functions; expectation and variance; normal (Gaussian), distributions; statistical estimation and hypothesis testing; method of least squares, correlation and regression. Basic principles and techniques for the efficient design of experiments and effective analysis of data. Topics include: the nature and analysis of process variability, comparing processes, blocking and randomization, empirical model building for quantifying relationships between process inputs and outputs, two-level factorial and fractional factorial designs for screening out inert input variables, other designs, a practical approach to experimental design. Course Component: Lecture, Tutorial Prerequisites: CHG 1125, CHG 1371, MAT 2384.
