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Alessandro Di Bucchianico - Teaching Activities

Alessandro teaching SPC

 

Prizes

I am the proud winner of the following teaching prizes:

  • 2000-2001: second prize for best teacher of first year industrial engineering courses

  • 2002-2003: best teacher of all courses for mathematics students

Current Courses

Introduction to Experimentation (2AS10)
This is an elective Bachelor course for all students. The course is part of a coherent elective package. This introductory course gives an introduction to experimentation and statistics by treating the scientific method and its industrial counterparts. Students also learn to record research results in a reproducible way through literate reporting.

Effectiveness of Mathematics (2WH10)
This is a kaleidoscope course for all students (but in practice mainly elected by mathematics students) taught by a larger number of mathematics instructors. Every week students are briefly introduced to another mathematics topic and are shown how mathematics can solve a specific problem. Together with Professor Remco van der Hofstad I teach the topic of collaboration graph analysis, in which we show how probability theory and statistics can be used to analyze graph data. As graph data, we chose the collaboration graph of mathematicians (cf. the Erdös Number Project) and show how with elementary statistics one can determine beforehand how large a random sample should be to obtain accurate results on the distance between mathematicians and the number of co-authors.
Linear Statistical Models (2WS40)
This is an elective Bachelor course for mathematics students. The course gives an introduction to linear regression and Analysis of Variance. Both the linear algebra approach to linear statistical models and the practical regression analysis and modelling using statistical software are treated in detail. The book is taught using the excellent introductory book by Bingham and Fry.

Advanced Statistical Models (2WS70)
This is an advanced Bachelor course on Generalized Linear Models which may also be taken by Master's students. The responsible teacher is Professor Edwin van den Heuvel, I am responsible for the practicals, in particular the support using the statistical software R.

Student projects

I have several projects available for students wishing to do a Bachelor or Master's project on applied statistics. Feel free to drop by my office to discuss what kind of project you would be interested in. I currently have the following descriptions of Bachelor projects available:

  1. Short-term prediction of water heights at the Oosterscheldekering (a Dutch storm surge barrier)
  2. Software reliability models for imperfect debugging
  3. Construction of confidence intervals when no failures occurs (inspired by a practical problem from software reliability)
  4. Estimation of quantiles of the negative binomial distribution

 

Teaching software

In order to facilitate student learning of statistical concepts, I have been developing web-based software with the help of Marko Boon (IT specialist and responsible for the Java implementations) and Emiel van Berkum. We currently have the following software:

  • Statlab (interactive tool for learning practical aspects of DOE, both for screening and optimisation)

  • Design applet (illustrates fractions and blocks of factorial fractions; users may give generators or select a fraction/block)

  • Box (illustrates simplified version of simplex optimization in DOE)

  • Probability distributions applet (shows various characterizations of distributions, including failure intensity/hazard rate)

  • Capability indices applet (illustrates differences between Cp and Cpk).

Courses that I used to teach

Quality Control in Production (1F200)
This was an introductory course on quality control for first-year management science students. The emphasis was on quality management, in particular process design,  rather than statistical issues. The textbook for this course was the excellent text  Creating Quality. Process Design for Results by W.J. Kolarik.

Production Quality of Series Production(1R360)
This was a course for third-year management science students in the special programme Series Production. This course became superfluous with the introduction of the first-year compulsory course 1F200.

Statistics for OML 2 (2DD61)
This is a premaster course on statistics for students in the Logistics Master's programme. The course contents include hypothesis testing, one- and two smaple problems, linear regression analysis, analysis of variance and the basics of experimental design.

Statistics 2 (2DB45)
This is a course on statistics for the Architecture, Building and Planning Bachelor's programme. The course contents include correlations, contigency tables, nonparametric statistics, linear regression analysis and the basics of experimental design.

Statistics for Chemical Engineering (2S070)
This was an introductory probability and statistics course, with heavy emphasis on regression analysis. This course will not be taught anymore, since statistics teaching for chemical engineering students has been integrated in their lab work.

Kansrekening (2DI25)
This is an introductory probability theory course for computer science students. Information is available through Studyweb. Together with the new course 2DI35 (taught for the first time in 2008-2009), this is a replacement for the course 2S970.
Statistics 1 for Chemical Engineering (2DS00)
This is an introductory, hands-on  statistics course, with heavy emphasis on regression analysis, including nonlinear regresssion. 

Statistics 2 for Chemical Engineering (2DS01)
This was a short advanced course in statistical methods, with emphasis on analysis of variance and design of experiments (fractional factorial designs, response surface methods, mixture designs). We will try to link these subjects as much as possible with recent developments in combinatorial chemistry and high-throughput analysis. We use the web teaching tool Statlab for interactively teaching DOE . The course has been integrated into the course 6BV04.

Statistics for Chemical Engineering (2S070)
This was a comprehensive course for chemical engineering on probability theory and statistics. The statistics part included parameter estimation, hypothesis testing and an introduction to linear regression analysis.
Mathematical Statistics 2 (2S220)
This was an advanced mathematical statistics course for mathematics students on asymptotics, empirical process theory and kernel estimators.

Statistical theory of Design of Experiments and Quality Improvement (2S320)
This was an advanced elective course for chemical engineering students in which advanced diagnostic of linear regression, Design of Experiments and Statistical Process Control was taught.
Reliabity Theory (2S380)
This was a course taught to both mathematics and industrial engineering students which I taught together with Professor Sanders from Industrial Engineering. The topics included failure rates, censoring, redundant systems, Markov analysis and safety analysis.
Mathematics 5 for Computer Science (2S970)
This was an introductory probability and statistics course for computer science students. Information is available through Studyweb. The course 2S970 is superseded by the new course 2DI25 Probability Theory and 2DI35 Statistics.
Mathematical Statistics (2S990)
This introductory statistics course for mathematics students has been superseded by 2WS05.

Industrial Statistics (2WS02)
This was a course in the SPOR Master's Programme, which I teach jointly with Emiel van Berkum. The topics are design of experiments (DOE; fractional factorial designs, response surface methods, optimal designs) and statistical process control (SPC; historical development of quality control, basic acceptance sampling schemes, measurement analysis (Gauge R & R), capability analysis: capability indices, testing for capability, Six Sigma metrics, control charts: Shewhart control charts for grouped and individual data, CUSUM and EWMA control charts, control charts for attribute data, control charts for specific situations (correlated data (in particular time series models), tool-wear charts).

Mathematical Statistics (2WS05; former code 2S990)
This course is being taught as part of the Bachelor's programme Applied Mathematics, and is also included in the Mathematics minor for. The contents include estimation theory (sufficiency, Rao-Blackwell, ML estimation of functions of parameters, robust estimation, delta method, confidence intervals), elementary testing theory (Neyman-Pearson lemma, likelihood ratio tests), and one- and two-sample problems.

Robust Design (2WS08)

This was a course for our Master's Program Industrial Mathematics. The course provided an introduction to robust design by covering the first 7 chapters of the well-known book by Phadke.

Applied Statistics (2WS10)
This was an elective course for the joint Master's programme of the three Dutch technical universities. The contents of this course follow a two-year scheme. In 2005-2006 the course was an introduction to SPC (Statistical Process Control). The main topics were historical development of quality control, basic acceptance sampling schemes, measurement analysis (Gauge R & R), capability analysis, Six Sigma metrics, Shewhart control charts for grouped and individual data, CUSUM and EWMA control charts, control charts for attribute data, control charts for specific situations (correlated data (in particular time series models), tool-wear charts). I  taught this part with assistance from Professor Albers and Dr. Kallenberg from Twente University and did so again in 2007-2008. In 2006-2007 Dr. Lopuhaä of Delft University taught the course with survival analysis as content.

Regression analysis and Analysis of Variance (2WS14)
This course was taught as part of the Bachelor's programme Applied Mathematics. In this course,  basic topics in linear regression and Analysis of Variance were covered. This course has been replaced by the new course 2WS40.

Reliability (2WS16; formerly 2WS00)
This course was taught as part of the Bachelor's programme Applied Mathematics. In this course,  basic topics in reliability theory and survival analysis like redundancy, life time distributions, censoring, Cox proportional hazards model etc. were covered.

Pratical Skills Anorganic Chemistry (6BV04)
This is not a regular course, but a practical part of the chemical engineering curriculum. The statistical theory of design of experiments is an integrated part of this course. Students will directly apply basic principles of experimental design to experiments that they must perform themselves. I taught this course together with Koo Rijpkema. The responsible teachers from the Chemical Engineering Department are Christian Müller and Pieter Magusin.