Attività formativa monodisciplinare
Codice dell'attività formativa: 

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
Insegnamento (nome in italiano): 
Insegnamento (nome in inglese): 
Tipo di attività formativa: 
Attività formativa Affine/Integrativa
Tipo di insegnamento: 
Settore disciplinare: 
Anno di corso: 
Anno accademico di offerta: 
Responsabile della didattica: 

Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Secondo Semestre
Obbligo di frequenza: 
Ore di attività frontale: 
Attività formative affini o integrative
Materiali didattici: 

There are no prerequisites but it is advisable to know the basic themes of probability theory and statistical inference. Probability theory: casual experiments, probability measure, probability spaces, random variables, Normal distribution, use of the table of the normal and T-distribution. Statistical Inference: parametric statistical models, random samples statistics, sample distributions, distribution of the sample mean, point estimate, confidence intervals, testing hypotheses, simple linear regression model.

Educational goals

The course contributes to the educational objectives of the course of study, with regard to the area of basic disciplines, in particular to statistical quality control and statistical analysis of economic and financial models.
At the end of the course the student will be able to choose the most suitable model to describe both simple and complex phenomena, taking into consideration the unique aspects of the phenomenon to be analysed and formalised. She or he will be able also to design and analyze the principal techniques of statistical quality control. She or he will have a good knowledge of the R enviromen to analyse real data.

Course content

Simple and multi-linear regression analysis, matrix approach. Analysis of Variance ANOVA. Linear modelling for qualitative variables, generalised linear models. Experimentation in industry. Principles of experimental planning. Design of experiments. Complete two-level factorial design. Statistical quality control. Control charts for variables, control charts for the mean and variance of a process. Average run length (ARL) and run length, for control charts. CUSUM and EWMA charts. Brief look at ARL and OC for CUSUM charts. Laboratory and Exercises: Application of all the topics studied to real data sets. Real Data analysis in R environment.

Textbooks and reading lists

Probability & Statistics for Engineers & Scientists, Walpole, Myers, Myers, Pearson. (Chapters 12--15)
Statistical quality control, a modern introduction, sixth edition, (international student version), D.C. Montgomery, Wiley. (pages: 200)
Design and Analysis of Experiments, 7th Edition, D. C. Montgomery, Wiley. (pages: 100)
Teacher material on R and exercises

Teaching methods

Frontal lectures with numerous examples and discussion of practical cases.
Exercises in classroom with active participation of the students.

Assessment and Evaluation

The assessment of learning is verified through a final written test of 2 hours and thirty minutes. The test consists of three exercises with multiple points. Each exercise has a number of points that varies between two and eight with a total score ranging from 8 to 14 for a total of 30 points. With the written test, both the theoretical knowledge of the statistical analysis concepts presented in the course and the critical ability of the student to apply them to specific cases occur. The final grade is the score of the written test. There is no oral exam.

Further information

During the written exam the student has to bring: a calculator (no telephone or smart phone or tablet) and a text book that contains statistical tables. She or he can also bring one sheet of A4 format (retro-verso) with the informations that could be usefull during the exam (formula and/or examples). The exam procedures are the same for attending and non-attending students.