### Teachings

Select Academic Year:     2016/2017 2017/2018 2018/2019 2019/2020 2020/2021 2021/2022
Professor
MASSIMILIANO GROSSO (Tit.)
Period
First Semester
Teaching style
Convenzionale
Lingua Insegnamento
ITALIANO

Informazioni aggiuntive

Course Curriculum CFU Length(h)
[70/88]  CHEMICAL AND BIOTECHNOLOGICAL PROCESS ENGINEERING [88/00 - Ord. 2020]  PERCORSO COMUNE 9 90

### Objectives

The course aims to supply the main concepts required for an understanding of the fundamental concepts of statistical inference from experimental data, and optimization theory of functions.
The student will be able to deal with problems related to the parameter estimation for linear and non-linear models, and she/he will be able to (i) interpret and select the best model for the description of the experimental reality, (ii) identify the most appropriate parametric conditions for a given objective function.
The student will be capable of recognizing the statistical nature of an engineering problem and he will have gained a sensitivity in interpreting the experimental data and she/he can find out the degree of confidence about the conclusions that can be drawn from his analysis.

### Prerequisites

Calculus, Geometry

### Contents

Introduction:
Descriptive statistics
Lectures. 2 - Exercises: 2 Laboratory: 0
Probability theory
Random experiment: definition Definition of scalar random variables Mathematical models for the scalar random variables: cumulative distribution function (CDF) and probability density function (PDF) Random vector variables Mathematical models for the random vector variables.
Lectures 8 - Exercises 4 Laboratory: 0
Parameter identification of scalar and vector random variables Definition of Statistical Estimators Statistical Estimators: Method of Moments, Method of Least Squares, Method of Maximum likelihood Main properties of the statistical estimators
Lectures: 9 - Exercises: 3 - Laboratory: 5
Confidence ranges for the statistical estimators
Lectures: 4 - Exercises: 2 - Laboratory: 4
Statistical tests of hypothesis:
Analysis of Variance
Lectures: 9 - Exercises: 3 - Laboratory: 6
Parameter estimation of physical models
Linear models: linear regression Statistical estimators for the linear regression: properties and confidence ranges Statistical tests for the linear regression
Lectures: 9 - Exercises: 3 - Laboratory: 6
Non linear models:
Outline on the numerical methods - Assessment on models adequacy
Lectures: 8 - Exercises: 3 - Laboratory: 0
Total hours: 90
Credits: 9

### Teaching Methods

Lectures: 49 h
Exercises: 20 h
Laboratory: 21 h
Total: 90 h
The teaching will be mainly released in the presence, integrated and "augmented" with online strategies, in order to ensure the fruition in an innovative way and inclusive

### Verification of learning

Knowledge and understanding of the student is evaluated through evaluation of a team paper work and an oral interview that will be held in-presence or remotely using computer aids.
The student is required to demonstrate to know the theory of the statistics applied to the analysis of experimental data and the parameter inference for mathematical models. It is important that the student has a deep knowledge of the typical chemical engineering processes. The answers need to be motivated using the theoretical concepts tackled in the course. The student is also required to analyze particular situations proposed by the teacher during the exam. It is also evaluated the line of reasoning for the student presentation and the use of a proper technical language.
The student is evaluated according to the following criteria:
- knowledge and basic understanding of the concepts: 18/30-21/30;
- knowledge and understanding and ability of applying knowledge: 23/30-26/30;
- knowledge and understanding, ability of applying knowledge and ability to handle complex problem of model inference: 27/30-30/30.

### Texts

Lecture notes provided by the teacher.
Applied Statistics and Probability for Engineers, Montgomery and Runger, Wiley