Formation/Cours

At the end of the course, the student should be able to :
– Breakdown a complex problem into smaller parts, especially when the problem is non trivial
– Formulate appropriate solution to solve each part of the conmplex problem: being able to select the relevant tools, select the right data, avoid mispresentation…
– Cross check data, identify outliers and solve missing data problems
– Collect relevant data using surveys and sampling methods
– Understand the importance of wording and variable description
– Propose creative solutions given the understanding of the data
– Master basic tools: correlation, box plot, distributions, payoff tables…
– Select the relevant method: Baye’s analysis, confidence intervals, parametric and non parametric tests… and be able to check assumptions (normality…)
– Master expect knowledge tools and understand the new trends in analysis
– Analyze numerical and especially categorical data
– Demonstrate expertise in advanced tools and methods: SPC/SQC, Acceptance Sampling, Capability, Control charts, Decision Rules
– Link statistics with management methods and quality tools such as the six-Sigma
– Formulate, model and solve optimization problems
– Be open to new developments in their field of competence and be a reference point for those developments.
– Master a professional software

I Basics: Probabilities, discrete and continous distributions, sampling, confidence intervals
II Hypothesis Testing: assumptions, parametric and non parametric tests, independent and paired samples, categorical data
III Decision rules and decision making: payoff tables, decision trees
IV Quality tools: SQC, Tolerance, specification, lot acceptance sampling
V Process management: SPC, Capability, control charts, decision rules
VI Optimization methods: canonical, standard form, solver, sensitivity analysis
V Multivatiate analysis: FA, PCA, DA