ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 98 Hours of tutorials: 3 Expository Class: 31 Interactive Classroom: 18 Total: 150
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
To acquire the managing, under a practical approach, of the diverse technologies that allow a correct and rigorous exposition, withdrawal, analysis and interpretation of information specially focused the development of new products and processes as well as the development of the existing ones.
These aims centre on the foundations and basic models of the statistical methods and on the exploratory analysis and inferencial of the information specially directed chemical engineers.
1. Descriptive Statistics
Introduction to Statistics. Qualitative and quantitative variables. Discrete and continuous variables. Frequency distribution. Graphic representations. Central measurements. Dispersion and shape measurements.
2. Probability
Historical introduction to Probability. Basic concepts. Random experiment. Sample space. Events. Definition of probability. Conditional probability. Independence. Classical theorems: Product rule, Law of total probabilities and Bayes' theorem.
3. Discrete random variables
Definition of random variable. Discrete random variable: probability distribution and distribution function. Main discrete distributions: Bernoulli, Binomial and Poisson.
4. Continuous random variables
Continuous random variable. Density function and distribution function. Characteristic measures of a random variable: mathematical expectation, variance and standard deviation. Typification of a random variable. Uniform distribution. The Exponential distribution. The Normal distribution. Approximation of other distributions by the normal distribution. Central Limit Theorem.
5. Estimation and confidence intervals
Introduction to estimation. Properties of the estimators. Confidence interval concept. Confidence interval for a proportion. Estimation and confidence intervals for the mean and variance of a normal population. Applications to the determination of the sample size.
6. Test of hypotheses
The problem of hypothesis testing: types of hypotheses, types of errors and their associated probabilities. Critical level or p-value. Hypothesis testing on a proportion. Tests on the mean and variance of a normal population.
7. Two-sample problem
Paired samples and independent samples. Comparison of two means in paired samples and in independent samples. Testing two variances. Testing two proportions.
8. Linear regression
The simple linear regression model. Estimation of the coefficients by least squares. Covariance and correlation coefficient. Estimation of the error variance. Properties of the estimators. Inference about the parameters. Prediction.
9. Introduction to linear programming
Formulation of linear programming problems. Graphical solution of the linear programming problem. Solution using R software.
Basic bibliography:
Notes of the subject in the virtual campus and the following books :
Dalgaard, P., 2008. Introductory Statistics with R. Springer (second edition). Accesible en https://link-springer-com.ezbusc.usc.gal/book/10.1007%2F978-0-387-79054…
Holický, M., 2013. Introduction to Probability and Statistics for engineers. Springer. Accesible en https://link-springer-com.ezbusc.usc.gal/book/10.1007%2F978-3-642-38300…
Complementary bibliography:
DEVORE J.L., 2005. Probabilidad y Estadística para Ingeniería y Ciencias. 6º edición. México: Thomson. ISBN 9789706864574
FREUND J.E., MILLER I., MILLER M. 2000. Estadística Matemática con Aplicaciones. 6ª edición. México: Pearson. ISBN 9701703898
MENDENHALL, W, SINCICH, T., 2016. Statistics for Engineering and the Sciences. 6ª edición. Boca Ratón: CRC Press. ISBN 9781498728850
MONTGOMERY D.C., RUNGER G.C., 1996. Probabilidad y Estadística aplicadas a la Ingeniería. 2ª edición. México: Limusa. ISBN 9789681859152
NAVIDI W., 2006. Estadística para ingenieros. México: McGraw-Hill. ISBN 9701056299
PEÑA, D., 2008. Fundamentos de Estadística. 2ª edición. Madrid: Alianza Editorial. ISBN 9788420683805
QUESADA-PALOMA V., ISIDRO A., LÓPEZ L.J., 1982. Curso y Ejercicios de Estadística.2ª edición. Reimpresión. Madrid: Alhambra. ISBN 8420508780
(As it appears in the memory of degree)
Specific competencies
CB.1. That students have demonstrated knowledge and understanding in a field of study that part of the basis of general secondary education, and is typically even level, although it is supported by advanced textbooks, includes also some aspects that imply knowledge of the forefront of their field of study .
General competitions
CG.3. Knowledge in basic and technological matters, which it qualifies for the learning of new methods and theories, and provide them with versatility to adapt to new situations.
CG.4. Aptitude to solve problems with initiative, capture of decisions,
Creativity, critical reasoning and of reporting and transmitting knowledge,
Skills and skills in the field of the chemical industrial engineering.
CG. 8 Aptitude to apply the beginning and methods of the quality.
CG.10 Aptitude to be employed at a multilingual and multidisciplinary environment.
Transverse competencies
CT.1. Capacity of analysis and synthesis
CT.2. Aptitude to organize and plan
CT.3. Oral and written communication
CT.4. Skills for the use and development of of IT applications
CT.5. Capacity of management of the information
CT.6. Resolution of problems
CT.8. Teamwork
CT.11. Aptitude to communicate with experts of other areas
CT.12. Critical reasoning and ethical commitment
CT.13. Aptitude to apply the knowledge in the practice
CT.17. Creativity
CT.19. Autonomous learning
CT.20. Initiative and entrepreneurship
Basic specific competencies
FB.1.4 Ability to solve mathematical problems that may
arise in engineering .
Ability to apply knowledge of Statistics and Optimization
The methodology to be carried out will be as follows.
The subject has a semester character with six ECTS credits, so it will have 28 hours of expository teaching and 21 hours of interactive teaching, which include computer practices with the statistical software R.
The classes will last 55 minutes and will take place in the assigned classroom, mainly using the blackboard and presentations. Student participation in classes will be encouraged, especially in the most practical aspects. They will also discuss and solve various exercises set out in bulletins that will be delivered to students to promote their personal work, also being used to evaluate their achievement.
Competences CB.1, CG.3, CG.10, CT.1, CG.8, CT.2 and CT.3 will be promoted in the lectures, CG.8, CG.4, CT.2 and CT .3 will be developed in the interactive classes and the competences CG.8, CG.10 and CT.4 in the practical classes of computer with R.
The tutorials will try to solve the doubts raised by the students about the theoretical-practical classes or about the problems that they must solve.
Students will have the support of the USC Learning Management System, through the course page, to have access to the programs, bibliography and different exercise bulletins, as well as notes on some topics and information on complementary voluntary activities and communication tools.
The assessment to be carried out in scenario 1 will be as follows.
The qualification of each student and the acquisition of the different competences will be done through continuous evaluation and the completion of a final exam. There will be a final theoretical-practical exam (both in the first opportunity and in the second opportunity) consisting of the interpretation of a series of questions, development of theory questions and problem solving. This exam will be assigned a weight of 70% in the final grade for the subject.
Continuous evaluation will represent 30% of the final grade, broken down into 10% for written controls, 5% for participation in seminars and homework delivery, and 15% for evaluations carried out in computer labs.
At the second opportunity there will be a new exam. The result of the continuous evaluation will be kept in the second opportunity of the same course.
Any student who has attended the final exam will be considered presented.
Repeating students will have the same evaluation system.
Competences CB.1, CG.3, CG.8, CT6, CT1, CT3, CT13, CT19 and FB.1.4 will be evaluated by means of the final exam.
Through continuous assessment and tutorials, the competencies CG4, CG10, CT1, CT2, CT3, CT8, CT11, CT12, CT17 and CT20 will be evaluated.
Also in the practical classes the competences CG3, CT4 and CT5 will be valued.
In cases of fraudulent completion of exercises or tests, the provisions of the "Regulations for the evaluation of the academic performance of students and for the review of grades" will apply.
Activity Hours(Classroom and personal work) ECTS
Classes magis. 28,0 42,0 2,8
Seminars 9,0 13,5 0,6
Computer labs 12,0 6,0 0,9
Tutorials in groups 2,0 8,0 0,4
Individual tutorials 2,0 3,0 0,2
Examination and review 5,0 22,0 1,1
(As it appears in the memory of degree)
To overcome successfully to matter is advisable:
- the assistance to the theoretical and practical classes and the resolution and review of the problems that they propose.
- to dedicate to the study of the matter a time regularly distributed along the four-month period.
- to verify the degree of assimilation of the concepts and of acquisition of the basic technologies of calculation, solving the exercises proposed in class.
- to use the software of the matter in the working hours of the pupil.
- to do use of the schedule of tutorships to consult any doubt that could arise.
- with the utilization of the recommended bibliography it is possible to complete or to extend any topic.
The language in which the course will be taught will be Spanish.
Maria Angeles Fernandez Sotelo
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813210
- mangeles.fernandez.sotelo [at] usc.es
- Category
- Professor: University Lecturer
Julio Gonzalez Diaz
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813207
- julio.gonzalez [at] usc.es
- Category
- Professor: University Lecturer
Ignacio Gómez Casares
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813391
- ignaciogomez.casares [at] usc.es
- Category
- Ministry Pre-doctoral Contract
| Monday | |||
|---|---|---|---|
| 11:00-12:00 | Grupo /CLE_01 | Spanish | Classroom A2 |
| Tuesday | |||
| 11:00-12:00 | Grupo /CLE_01 | Spanish | Classroom A2 |
| Wednesday | |||
| 11:00-12:00 | Grupo /CLIS_01 | Spanish | Classroom A2 |
| Thursday | |||
| 10:00-11:00 | Grupo /CLIS_02 | Spanish | Classroom A2 |
| 05.31.2024 09:15-14:00 | Grupo /CLE_01 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIS_02 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIS_01 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_01 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_03 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_04 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIS_03 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_02 | Classroom A3 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_02 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLE_01 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLIS_02 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLIS_01 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_01 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_03 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLIL_04 | Classroom A4 |
| 05.31.2024 09:15-14:00 | Grupo /CLIS_03 | Classroom A4 |
| 07.05.2024 09:15-14:00 | Grupo /CLE_01 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIS_02 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIS_01 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIL_01 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIL_03 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIL_04 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIS_03 | Classroom A1 |
| 07.05.2024 09:15-14:00 | Grupo /CLIL_02 | Classroom A1 |