ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 26 Total: 53
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)
To provide students with the statistical techniques essential in experimentation in Optics and Optometry. Train you for data analysis and statistical reasoning. Learn how to use software to solve statistical problems.
Topic 1. Descriptive statistics
Introduction to Statistics. Variables. Frequency distributions. Graphic representations. Position and dispersion measures. Shape measures. Box plots. Relationship between variables: contingency tables, scatter plot, covariance and correlation.
Topic 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.
Topic 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. Normality checks: the QQ-plot and the Shapiro-Wilk statistic.
Topic 4. Estimation and confidence intervals
Introduction to Statistical Inference. Parameter estimation. Confidence intervals for proportion and for mean and variance of a normal population.
Topic 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 the resolution of a hypothesis test. The p-value. Hypothesis tests for the proportion and for the mean and variance of a normal population.
Topic 6. The two-sample problem
Paired samples and independent samples. Comparison of two means in paired samples and in independent samples. Contrast of two variances. Contrast of two proportions.
Topic 7. Analysis of variance
The model of analysis of variance with a single factor. The F-test. Multiple comparisons. Diagnosis of the model.
Topic 8. Regression models
Introduction to Regression Models: The Simple Linear Model. Coefficients estimation by least squares. 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 cards for the seminars and scripts for the computer practices will be developed.
The following books are recommended as a complement:
Crujeiras, R.M. and Faraldo Roca, P. (2010): "Manual de estadística básica para ciencias de la salud”. Universidade de Santiago de Compostela.
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.
Knowledge:
· Con_20. To know the statistical techniques essential in experimentation in
Optics and Optometry.
Skills and Abilities:
· HyD_1. Think in an integrated way and approach problems from different points of view.
seen with critical reasoning.
· HyD_2. Organize and plan work.
· HyD_3. Interpret results and identify consistent and inconsistent elements.
· HyD_4. Work as a team.
· HyD_5. Maintain an ethical commitment, as well as a commitment to equality and equality.
integration.
· HyD_8. Know how to analyse data and interpret experimental results specific to the
fields of Optics and Optometry.
· HyD_13. Apply the general methods of Statistics to Optometry and Statistical Sciences.
vision.
Competences:
· Comp_1. Students must have the ability to gather and interpret relevant data
to make judgments that include reflection on relevant issues of a social nature,
scientific or ethical.
· Comp_3. That the students have developed those learning skills
necessary to undertake further studies with a high degree of autonomy.
· Comp_4. That students know how to apply theoretical-practical knowledge
acquired in the degree of a professional form and are competent in the
problem solving, as well as in the elaboration/defense of
arguments in both academic and professional contexts related to
Optics and Optometry.
· Comp_5. Ability to learn autonomously, to work in a team, to organize
time and resources, and to acquire new knowledge and techniques in Optics and
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 hours of interactive work consisting of computer practice, where the use of software for the application of statistical techniques will be learned.
• 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 content and activities, which they will be able to access through the virtual course.
• The overall grade of the subject will be the result of a final theoretical-practical exam, which will correspond to 55% of the total grade, and of the continuous evaluation during the teaching period, which will be the remaining 45% of the total grade.
• The 45% corresponding to the evaluation of the interactive activities will be made up of 20% obtained through tests during the computer sessions, 20% in a test on the theoretical content and practical exercises and 5% by participation in the seminars and delivery of assignments.
• Any student who participates in evaluation activities that allow 50% of the grade to be achieved will be considered as presented for evaluation.
• The same evaluation system will be applied in the recovery, so that the new exam will replace the grade of the exam at the first opportunity, maintaining 45% of the continuous evaluation carried out in the school period.
• Students with exemption from class attendance must take the final exam in person and send the interactive activities in electronic format for evaluation.
In general, four additional hours per week of study and personal work, which complement class attendance, should be sufficient.
To successfully pass the subject, it is advisable 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
- 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 |
| 05.18.2026 10:00-12:00 | Grupo /CLE_01 | Classroom 1 |
| 05.18.2026 10:00-12:00 | Grupo /CLE_01 | Classroom 2 |
| 06.29.2026 10:00-12:00 | Grupo /CLE_01 | Classroom 1 |
| 06.29.2026 10:00-12:00 | Grupo /CLE_01 | Classroom 2 |