Description

: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.

Posting limited to:

Professeur à temps-partiel régulier / Regular Part-Time Professor

Date Posted (YYYY/MM/DD):

2026/05/29

Applications must be received BEFORE (YYYY/MM/DD):

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