1. Quality engineering & Industry 4.0: data analysis as a basic tool for modeling, monitoring, control and improve.
2) Quality data modeling:
-Standard assumptions and related tests;
-Modeling patterns via linear models;
-Modeling autocorrelated data via time series analysis;
-Modeling survey data: Categorical and ordinal data
3) Quality monitoring of continuous variables
-Traditional statistical process control (SPC) for the mean and the variance
-SPC for autocorrelated data: Problems of traditional control charts for autocorrelated data;
-Model based and model-free approaches for quality control of autocorrelated data.
4) Quality modelling and monitoring for "big" data streams: multivariate data
-Modeling multivariate data
-Dimensional reduction via Principal Component Analysis
-Control chart for multivariate data - controlling the mean and the covariance
5) Toward zero-defect manufacturing: process quality and product specifications. Capability analysis. Univariate and multivariate control charts for small shifts (EWMA, CUSUM).
6) Modelling and monitoring attribute and qualitative data: control chart for defective rates and survey data
7) Quality Improvement – The role of improvement in the six-sigma roadmap. Quality improvement via empirical model building (for management engineering only- hints).