ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 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 Faculty of Optics and Optometry
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
Provide students with the statistical techniques essential in experimentation in Optics and Optometry. Train for data analysis and statistical reasoning. Learn the management of software for the resolution of statistical problems.
Item 1. Descriptive statistics
Introduction to Statistics. Variables. Frequency distributions. Graphic representations. Position and dispersion measures. Shape measures. Boxplots.
Item 2. Probability
Randomized experiment. Sample space. Events. Probability. Conditional probability. Independence of events. Product rule, law of total probabilities and Bayes' theorem. Sensitivity, specificity, positive and negative predictive values.
Item 3. Random variables
Discrete and continuous random variables. Probability distributions: mass function, density function, and distribution function. Measures of a random variable. Independence between random variables. The binomial distribution and the normal distribution. Approximation of the binomial distribution by the normal distribution.
Item 4. Estimation and confidence intervals
Introduction to Statistical Inference. Parameter estimation. Confidence intervals for the proportion and for the mean and variance of a normal population.
Item 5. Hypothesis testing
Introduction. Null and alternative hypothesis. Types of errors in a hypothesis test. Level of significance and power of a test. Stages in solving a hypothesis test. The critical level or "p-value". Hypothesis tests for the proportion and for the mean and variance of a normal population.
Item 6. Comparison of populations
Paired samples and independent samples. Comparison of two means in paired samples and in independent samples. Contrast of two variances. Contrast of two proportions. Analysis of variance with a single factor.
Item 7. Regression models
Introduction to regression models: the simple linear model. Estimation of least squares coefficients. Covariance and correlation coefficient. Estimation of the variance of the error. Inference about parameters. Prediction.
The study materials of the subject will be available on the virtual campus, in which the theoretical contents, illustrative examples, exercise bulletins for the seminars and scripts of the computer practices will be developed.
In addition, the following books are recommended:
Cao, R. et al. (2001): “Introducción a la Estadística y sus aplicaciones”, Ed. Pirámide.
Daniel, W.W. (2002): “Bioestadística. Base para el análisis de las ciencias de la salud”, Limusa Wiley.
Milton, J.S. (2007): “Estadística para Biología y Ciencias de la Salud”, McGraw-Hill-Interamericana.
Rosner, B. (2005): “Fundamentals of Biostatistics”, Duxbury Press.
1. BASIC COMPETENCES
CB1 - That the students have demonstrated knowledge and understanding in a field of study that assumes the general secondary education and it is typically at a level which, although it is supported by advanced textbooks, includes also some aspects that imply knowledge of the forefront of their field of study.
CB2 - That the students can apply their knowledge to their work or vocation in a professional manner and have the competences typically demonstrated through devising and sustaining arguments and solving problems within their field of study.
CB3 - That the students have the ability to gather and interpret relevant data (usually within their field of study) to inform judgments that include reflection on relevant social, scientific or ethical.
CB4 - That the students can communicate information, ideas, problems and solutions to both specialist and non specialized public.
CB5 - That the students have developed those learning skills necessary to undertake further studies with a high degree of autonomy.
2. GENERAL COMPETENCES
CG1 - That the students are able to tackle their professional and educational activity since respect for the code ethics of their profession, which includes, among other more specific principles of respect and promotion of rights key people, equality between people, the principles of universal accessibility and design for all and democratic values and a culture of peace.
3. CROSS-CURRICULAR COMPETENCES
CT1 - That they acquire capacity for analysis and synthesis.
CT2 - That acquire organizational skills and planning.
CT4 - They acquire the knowledge of a foreign language.
CT5 - You acquire skills related to field of study.
CT6 - That ability to acquire information management.
CT7 - They acquire the ability to solve problems.
CT8 - You acquire skills in decision-making.
CT10 - who can work in interdisciplinary team.
4. SPECIFIC COMPETENCES
CE6 - The student can evaluate and incorporate the technological advances necessary for the proper development of their business professional.
CE9 - The student can expand and update their skills to practice through continuing education.
CE13 - The student can demonstrate and implement methods of critical analysis, development of theories and their application to the field discipline of Optometry.
• 24 hours of lectures will be given in a classroom with a blackboard, where the theoretical contents of the subject and the procedures for solving practical problems will be learned.
• There will be 12 hours of interactive sessions in the seminar classroom in which exercises will be solved and the concepts of the subject will be discussed. There will be another 12 interactive hours consisting of computer practices, where you will learn the use of software for the application of statistical techniques.
• The tasks proposed in the interactive activities will be collected and corrected, which will be part of the evaluation.
• Students with exemption from class attendance must prepare the same contents and activities, which can be accessed through the virtual course.
• The final grade will be the result of a final theoretical-practical exam, which will correspond to 65% of the final grade, and the continuous evaluation during the school period, which will be the remaining 35% in the final grade.
• The 35% corresponding to the continuous evaluation will be formed by 20% obtained through tests during the computer sessions, 10% in a test on the theoretical contents and practical exercises and 5% by participation in seminars and delivery of tasks.
• Any student who participates in evaluation activities that allow reaching 50% of the grade will be considered as submitted to the evaluation.
• In the recovery the same evaluation system will be applied, so that the new exam will replace the qualification of the exam at the first opportunity, maintaining 35% of the continuous evaluation carried out in the school period.
• Students with waiver of class attendance must take the final exam in person and send the interactive activities in electronic support for evaluation.
In general, four additional hours per week of study and personal work, supplementing class attendance, should be sufficient.
To successfully pass the subject it is convenient to attend classes, both expository and interactive. Likewise, the resolution and review of the exercises proposed throughout the course must serve to achieve the objectives of the subject.
Cesar Andres Sanchez Sellero
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813208
- cesar.sanchez [at] usc.es
- Category
- Professor: University Lecturer
| Tuesday | |||
|---|---|---|---|
| 11:00-12:00 | Grupo /CLE_01 | Spanish | Classroom 3 |
| Thursday | |||
| 10:00-11:00 | Grupo /CLIS_01 | Spanish | Classroom 3 |
| 11:00-12:00 | Grupo /CLIS_02 | Spanish | Classroom 3 |
| 05.19.2025 10:00-12:00 | Grupo /CLE_01 | Classroom 1 |
| 05.19.2025 10:00-12:00 | Grupo /CLE_01 | Classroom 2 |
| 06.30.2025 10:00-12:00 | Grupo /CLE_01 | Classroom 1 |
| 06.30.2025 10:00-12:00 | Grupo /CLE_01 | Classroom 2 |