## STATISTICS AND OPTIMIZATION

### Course

Code: 1204

Degree: Bachelor's in Electronic Engineering and Industrial Automation

School of Engineering of Elche

Year: Year 1 of Bachelor's in Electronic Engineering and Industrial Automation

Semester: Spring

Type: Core

Language: Spanish

ECTS credits: 6 Lecture: 3 Laboratory: 3 | Hours: 150 Directed: 60 Shared: 30 Autonomous: 60 |

Subject matter: Mathematics

Module: Core

Department: Statistics, Mathematics and Informatics

Course instructors are responsible for the course content descriptions in English.

### Description

### Faculty

Name | Coordinator | Lecture | Laboratory |
---|---|---|---|

SANCHEZ SORIANO, JOAQUIN | ■ | ■ | |

LOPEZ ROMERO, MANUEL | ■ | ||

ESTAÑ PEREÑA, MARIA TERESA | ■ |

### Professional interest

On the other hand, the use of algorithms has become widely spread in different processes in the field of industrial engineering such as software design, technical drawing or designing robots and electronic components. Thus, some basic knowledge of numerical algorithms is needed in the formation of an industrial engineer.

Finally, the engineering systems grow in complexity. Therefore, the modeling and optimization of systems is becoming increasingly necessary to improve its performance and efficiency. For example, the design of a production line requires an analysis that includes, first, to model the process and, later on, to determine the optimum values of the parameters that describe it. Likewise, the use of models and optimization techniques is necessary for improving the decision-making at any business level, which obviously includes any company in the industrial field.

### Competencies and learning outcomes

#### General competencies

- Knowledge about basic and technological material that enables learning new methods and theories, providing versatility for adapting to new situations.

#### Specific competencies

- Capacity for resolving mathematical problems that arise in engineering. Aptitude for applying knowledge about linear algebra, geometry, differential geometry, differential and integral calculus, differential equations and in partial derivatives, numerical methods, numerical algorithms, statistics, and optimization.

#### Objectives (Learning outcomes)

- 01Make correct use of statistical tools sufficient to solve problems involving data analysis
- 02Understand the basic concepts of probability to model problems involving uncertainty or random elements.
- 03Understand the different characteristics that describe the numerical algorithms and their performance.
- 04Acquiring skills and ability to model usual engineering systems in the specific field of industrial engineering.
- 05Make correct use of optimization techniques to solve problems in the specific field of industrial engineering.
- 06Interpret and be able to communicate adequately the results of the analysis performed with the statistical and optimization techniques.

### Contents

#### Teaching units

#### Association between objectives and units

Objective/Unit | U1 | U2 | U3 | U4 | U5 |
---|---|---|---|---|---|

01 | |||||

02 | |||||

03 | |||||

04 | |||||

05 | |||||

06 |

#### Basic bibliography

- Hillier, Frederick S. Lieberman, Gerald J. "Investigación de operaciones". Madrid[etc.] McGraw-Hill 2006.
- Mendenhall,William. Sincich, Terry. "Probabilidad y estadística para ingeniería y ciencias". México [etc.] Prentice-Hall Hispanoamericana 1997.
- Taha, Hamdy A. Meza Staines, Guadalupe tr. "Investigación de operaciones una introducción". México Prentice Hall cop. 1998.
- Winston, Wayne L. "Investigación de operaciones [aplicaciones y algoritmos]". México D. F. Grupo Editorial Iberoamérica cop. 1994.

#### Links

#### Software

- Microsoft Office 2010
- Microsoft Office 2013

### Methodology and grading

#### Methodology

**Lecture:**Pass on knowledge and activate cognitive processes in students, encouraging their participation.**Problem-based learning:**Develop active learning strategies through problem solving that promote thinking, experimentation, and decision making in the student.**Solving exercises and problems:**Exercise, test, and apply previous knowledge through routine repetition.

#### Grading

The final grade will be obtained as follows:

FINAL GRADE = 0.75xEF + 0.2xEC + 0.05xT,

where

EF = final exam grade (0-10);

EC = computer practice score (0-10).

T = score for tutorials, participation in class and in activities to be programmed (0-10). The number of units and the quality of the units will be fundamentally evaluated.

To pass the subject will be necessary to have passed EF with a grade of at least 5 points out of 10 and EC with a grade of at least 4 points out of 10 and also obtain a FINAL NOTE equal or higher than 5 points.

CHARACTERIZATION AND FINAL QUALIFICATION OF EXAMINATIONS (EF):

The exams will be written to solve problems and questions related to the contents of the subject. Each problem or issue will be scored according to the quality of its approach and numerical resolution.

The subject consists of two blocks of differentiated contents, so the final exam will consist of two parts, one for each block.

Each block of contents is composed of the following didactic units:

1. BLOCK 1 (STATISTICS): Didactic units 1, 2 and 3.

2. BLOCK 2 (OPTIMIZATION): Didactic Units 4 and 5.

The weight of each block is as follows:

1. BLOCK 1: 50% of the EF score.

2. BLOCK 2: 50% of the EF score.

In order to facilitate the passing of the subject, two opportunities will be given for each block, i.e. two exams will be carried out for each of the blocks. The temporary programming of the exams is as follows:

1. The first exam (first opportunity) of each block will be made during the course once the corresponding contents have been given. The first exam of BLOCK 1 will be at the end of MARCH or at the beginning of APRIL and the first exam of BLOCK 2 will be at the end of MAY.

2. The second exam (second chance) of each of the blocks will be held on the date determined for the final exam of the subject.

IMPORTANT: To pass EF using the first opportunity of each block, students must obtain a score of at least 2 points out of 5 or, equivalently 4 points out of 10, in each exam.

Those who in the ordinary call of June had not passed EF with at least 5 points out of 10 points, will be examined of both blocks in the following calls.

CHARACTERIZATION AND SCORING OF COMPUTER PRACTICES (EC):

Two options for evaluating practices are offered:

Option 1: Completion of two partial exams online. In order to qualify for this option, all face-to-face practices must be attended to during the course and each of the two partial exams to be taken throughout the course must be passed with a grade equal to or greater than 4 points.

Option 2: Conduct a face-to-face final exam that will consist of solving problems with EXCEL, without using any supporting material, except for the help offered by the EXCEL program itself.

CHARACTERIZATION AND QUALIFICATION SCORE FOR TUTORIALS, PARTICIPATION IN CLASS AND IN THE ACTIVITIES TO BE PROGRAMED:

The number of activities and the quality of the activities will be fundamentally evaluated. A minimum grade is not necessary to overcome this part.

A UNIQUE FINAL EXAM ALTERNATIVE

Those students who want to make a unique final exam must make the written (EF) and computer exams plannedfor the day of the corresponding official call. To pass the subject a final grade of at least 5 points out of 10 points must be obtained, the final grade is calculated as follows:

FINAL GRADE = 0.75xEF + 0.25xEC

#### Assessment test characteristics

They are described previously.

#### Correction criteria

They are described previously.