B028441 - STATISTICS FOR EXPERIMENTERS IN THE FIELD OF TECHNOLOGY

STATISTICA PER LA SPERIMENTAZIONE IN AMBITO TECNOLOGICO (R.BERNI-6 CFU)
Italian language if all the attendant students are able to understand Italian (otherwise English)
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FORECASTING METHODS (F.Cipollini-3CFU)
English if all attendant students agree; Italian otherwise

The course if composed of two parts:
STATISTICS FOR EXPERIMENTS IN THE FIELD OF TECHNOLOGY (R.BERNI-6 CFU) presents the design of experiments (DoE) and process optimization for the field of technology;
FORECASTING METHODS (F.Cipollini-3CFU) presents fundamental methods for time series forecasting.

STATISTICS FOR EXPERIMENTS IN THE FIELD OF TECHNOLOGY (R.BERNI-6 CFU)
1-Cox DR e Reid N, 2000, The theory of the design of the experiment, Chapman & Hall- (chapter n. 1, 2)
2-Montgomery DC, 1991, Design and analysis of experiment, Wiley- (chapter n. 4,5,6,9).
3-Khuri I e Cornell JA, 1987, Response surfaces: design and analyses, Ed. Marcel Dekker- (chapter n. 1, 4, 5, 7).
4-Berni R.,2014 working paper elettronico n.10;
http://local.disia.unifi.it/wp_disia/2014/wp_disia_2014_10.pdf
5-Atkinson A.C., Donev A.N., 1992, Optimum experimental design, Oxford Statistical Science
Series, Clarendon Press- (chapter n.1, 5, 9, 20);
Additional readings (supplied by the teacher) relating to: response surface methodology and case studies; optimal designs and case studies; computer experiments theory and case studies.
Please note that all the cited Textbooks are available at the Universitary Library – Polo Scienze Sociali di Novoli
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FORECASTING METHODS (F.Cipollini-3CFU)
• Montgomery, D. C., Jennings, C. L. and Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting, 2nd ed., Wiley Series in Probability and Statistics
• Additional resources supplied by the teacher about: R programming; Analysis and treatment of outliers

STATISTICS FOR EXPERIMENTS IN THE FIELD OF TECHNOLOGY (R.BERNI-6 CFU)
To develop knowledge and abilities in order to perform an efficient experimental planning, to optimize a product or a production process with respect to specific characteristics (target) of quality and/or reliability (cc1, ca1). The latter also considering the phenomenon under investigation, the real context (external source of variabilities, noises). The possible decisional and technical implications (cc7) are also taken into account.
To understand capabilities and limitations of the methods (cc8) in order to suitably join theory and practice (ca5) through the application of the theory to real data, and correctly performing the potentialities of the methods with respect to environment and the process to be studied.
To develop own abilities, starting from the design planning step up to the final optimization step, by also considering the robust process optimization.
To develop analytical and critical abilities, so as to try refinements or explore different methodologies depending on the characteristics of the data (self-learning).
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FORECASTING METHODS (F.Cipollini-3CFU)
To develop knowledges and abilities to make predictions, under uncertain conditions, using time series data (cc1, ca1). The phenomenon under investigation, the applied context and the possible decisional implications (technical – cc7 and not – cc9) are also taken into account.
To understand capabilities and limitations of the methods (cc8) so as to link suitably theory and practice (ca5).
To develop judgment and communication abilities, also using English (see modalità di verifica dell’apprendimento).
To develop analytical and critical abilities, so as to try refinements or explore different methodologies depending on the characteristics of the data (self-learning).

Calculus, linear algebra, probability, statistical inference (estimation,
confidence intervals, tests), linear regression model, programming abilities

STATISTICS FOR EXPERIMENTS IN THE FIELD OF TECHNOLOGY (R.BERNI-6 CFU)
Traditional lesson using a pen tablet (slides delivered after the lesson) and practice in the computer lab. Case studies. Individual task (or in group) on real data.
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FORECASTING METHODS (F.Cipollini-3CFU))
Traditional lesson using a pen tablet (slides delivered after the lesson) and practice in the computer lab. Alternatively, each student can use his own laptop.

Moodle

STATISTICS FOR EXPERIMENTS IN THE FIELD OF TECHNOLOGY (R.BERNI-6 CFU)
Oral examination.
Questions will be related to the main arguments of the course, as described in the diploma supplement. Particular attention is payed to the critical and constructive student's abilities, e.g.: the student must establish to be able to deeply discuss the topics of the course.
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FORECASTING METHODS (F.Cipollini-3CFU)
Exam composed of two parts:
1) Empirical analysis of a time series reported on a manuscript (possibly in English);
2) Oral exam with a short discussion of the manuscript and theoretical questions.
1) is mandatory for 2). The final grade is based mainly on 2); 1) is generally neutral in this sense but, depending on its quality, can give a penalty or an award, relative to 2), in the range [-3/30, +3/30].
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The final grade is computed as weighted average of the two parts

STATISTICS FOR EXPERIMENTS IN THE FIELD OF TECHNOLOGY (R.BERNI-6 CFU)
The first part will be devoted to the illustration of the fundamental principles of DoE. Then, starting from the local approximation in Taylor's series, the fundamental theory of response surface methodology is introduced, by considering the I and II order. In particular: polynomial models, designs and properties, the moment array. A specific attention is devoted to optimization methods, I and II order. The experimental designs are: fractional factorials (at 2, 3 levels, and mixed levels), Central Composite design.
The second part is related to the split-plot design, following the last features and developments of this plan. In this context, the concept of random effect is introduced through the mixed Response Surface models.

The third part is related to optimal design by considering the general issues and the D, and T criteria.

Finally, kriging and computer experiments are illustrated.

FINAL REMARK:
Specific attention will be devoted to the students who attended the first part of this course during his/her Degree 1st level. For these students, further readings will be supplied for integrating and avoiding overlapping.
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FORECASTING METHODS (F.Cipollini-3CFU)
• Introduction to the R environment
• Introduction to time series analysis and forecasting
• Time series and stochastic processes
• Some examples of stochastic processes: White Noise, Random Walk, AR(1), MA(1)
• Simulations
• Weakly stationary and ergodic processes
• Autocorrelation function (Acf) and portmanteau tests
• AR (Autoregressive), MA (Moving Average) and ARMA processes
• Non stationary time series and integrated ARMA processes (ARIMA)
• ARIMA processes with seasonal components
• Estimation, selection and diagnostics of an ARIMA model
• Forecasting in general and with ARIMA models
• Forecasting error measures and forecast comparisons
• Transformations of variables
• Analysis and treatment of outliers
• External regressors
• Exponential smoothing