The teaching course is divided in four parts: 1) Analysis of variance and basics on design of experiments; 2) full factorial design and fractional factorial design at 2 levels; 3-quality through the experimental design and Taguchi's methods; Points #1,2,3- ROSSELLA BERNI 4)multi-attribute valuation methods: 1.conjoint analysis; 2. choice experiments and modelling (logit model). Point #4: Nedka D. NIKIFOROVA.
Lectur notes available on the teacher's web-site: http://local.disia.unifi.it/berni/
Levi R., Lombardo A., Vicario G., 2006, progettazione e analisi degli esperimenti, McGraw-Hill libri capp.: 4-6-7-8.
In addition, for the multi-attribute valuation methods: 1-Working paper Conjoint analysis.https://local.disia.unifi.it/pubblicazioni_DS/abstract-2005.php#wp2005_06
2-testo consultabile: Brasini, Freo, Tassinari F., Tassinari G.; Marketing e Pubblicità, Il Mulino, Ed.1999-parte 3 cap.4.
Learning Objectives
The aim of the teaching course is to learn how to improve the quality of a product or a production process in a company through design of experiments, ANOVA and Taguchi's Philosophy (robust design and two-step procedure); the second aim is to give the ability for evaluating a new product or service by a consumer's point of view (Consumers' preferences) by applying the multi-attribute valuation methods.
Prerequisites
Statistical inference
Teaching Methods
Theoretical lecturers and laboratories, for helping the students to be able to apply the studied statistical methods, by also considering case-study presentations.
Further information
Two locations:
PIN- Prato and
Dept. of Statistics, Computer Science, Applications- DISIA (Florence) for laboratories.
The Moodle Platform will be also used.
Type of Assessment
Oral examination.
More precisely, the questions will cover all the specific topics of the teaching course.
Particular attention will be payed to the critical and constructive student's abilities; the student should show his/her ability to apply the main concept of the statistical quality control methods in order to improve a product/process in a company. Moreover, he/she will show the comprehension and the ability to apply the multi-attribute valuation methods for setting a new product/service.
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The final vote (6 CFU) is computed as the average of the two votes obtained in the two parts formed by 3 CFU.
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Course program
1-One-way and two-way analysis of Variance; introduction to the experimental design: trial, replication, randomization, source of variability, factor role (experimental factor and sub-experimental factor); factor at fixed levels; experimental planning.
2-Full factorial design; interaction; fractional factorial design at two levels: Resolution, confounding effects, alias pattern, building of the fractional factorials. Fractional factorial design 3- robust design, and two-step procedure ( the Taguchi’s philosophy).
4- Customer satisfaction: basic notes; background questionnaire;
introduction to multi-attribute evaluation methods, revealed and stated preferences and Random Utility theory; basic theory and steps for the conjoint analysis (both rating and ranking-based); building of the profiles in the conjoint analysis; data analysis in the conjoint analysis; choice experiments: introduction, basic theory, willingness to pay. Alternatives and choice-sets in a choice experiment. The logit model.
Notes on heteroscedasticity of the alternatives and respondents’ heterogeneity.
Case studies.
Sustainable Development Goals 2030
In the teaching course case studies will be also related to sustainability topics, e.g. to industry, productivity, and consumers' sustainability