Introductory Industrial Statistics

Recommended Prerequisites

Data Processing.

Teaching Methods

Teaching methods are based on a combination of conventional classes where the topics are motivated and introduced, with the support of slides, software and illustrations (theoretical classes) and classes demonstrating concepts and their computational implementation (practical classes). In the course of the classes, the students consolidate the learning outcomes with projects carried out individually or in groups, where the methods and tools are applied autonomously under the supervision of the teacher. Tutorial lessons provide further support to the students’ efforts.

Learning Outcomes

This unit aims at complementing the training path started with the discipline of data processing, by introducing competences directed to the management of variability in industrial environments. This includes aspects of decision making (statistical testing of hypotheses), monitoring (statistical processes control), prediction (multiple linear regression) and statistical design of experiments (factorial designs). The goal is to provide future engineers with the tools, knowledge and methodologies to cope with the challenges raised by the existence of variability and uncertainty in the processes where they will develop their activity.

Work Placement(s)

No

Syllabus

1.Sources of variability in industrial processes. Ishikawa diagrams.

2.Hypothesis Testing

    a.Parametric tests (ANOVA, Association)

    b.Non-parametric tests

    c.Sampling

    d.Introduction to Statistical Process Control

3.Quality prediction

    a.Active and passive data collection

    b.Multivariate Linear Regression

4.Fundamentals of statistical design of experiments

    a.Complete factorial designs (<5 factors).

Head Lecturer(s)

Marco Paulo Seabra dos Reis

Assessment Methods

Assessment
Resolution Problems: 20.0%
Project: 30.0%
Exam: 50.0%

Bibliography

Reis, M. S. Estatística Para a Melhoria de Processos – A Perspectiva Seis Sigma. Coimbra: Imprensa da Universidade de Coimbra, 2016

Montgomery, D. C.,  Runger, G. C. Applied Statistics and Probability for Engineers. 6th ed. New York: Wiley, 2014

Montgomery, D. C. Introduction to Statistical Quality Control. 6th ed. New York: Wiley, 2009

Vining, G.,  Kowalski, S. M.  Statistical Methods for Engineers. 3rd ed. Duxbury: Thomson, 2010.