ECTS credits ECTS credits: 4.5
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 2.25 Expository Class: 18 Interactive Classroom: 18 Total: 137.25
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Mathematics
Areas: Geometry and Topology
Center Faculty of Pharmacy
Call: Second Semester
Teaching: Sin docencia (Extinguida)
Enrolment: No Matriculable | 1st year (Yes)
Once summarized and analyzed the sample information collected (these topics were covered in Mathematics and Statistics I), the objective is now, by using Statistical Inference, to test whether a situation is derived from a given probability model and to infer to the population the available knowledge of that model. In particular, from the data obtained from a random sample, the aim is to be able to apply appropriate statistical procedures to infer unknown characteristics of the population and to computee the error of the estimate.
- Apply the concepts of regression, contrasts and confidence intervals, using a statistical, package to the physical, chemical, biological data or data extracted from medico-pharmaceutical databases, and interpretation of the results.
- Provide a basic ability to design experiments based on statistical criteria.
ITEM 1: INTRODUCTION TO STATISTICAL INFERENCE.
1.1 Population and sample.
1.2 Parameter. Statistic.
1.3 Distribution of different statistics. Central Limit Theorem
1.4 Point estimation. Properties of estimators
1.5 Estimation by confidence intervals: the basics. Confidence level.
1.6 Confidence intervals for the population mean, variance and proportion
1.7 Determination of the sample size
1.8 Confidence intervals for the ratio of variances, difference of means and difference of proportions
ITEM 2: HYPOTHESIS TESTING
2.1 Statistical Hypothesis. Approach and method
2.2 Types of error. Decision criteria. Critical level or P-value. Power of a statistical test.
2.3 Interpretation of hypothesis testing. Relationship between confidence intervals and hypothesis testing.
2.4 One sample test for the mean, proportion and variance
2.5 Two samples test: comparison of two variances, comparison of two means (independent samples, paired samples), comparison of two proportions
2.6 Analysis of categorical data: contingency tables. Chi-square test. 2 × 2 tables. Research design. Homogeneity of variance tests. Tests of independence.
2.7 Goodness-of-fit: chi-square test of Pearson, the Kolmogorov-Smirnov test; normality test.
ITEM 3: REGRESSION AND CORRELATION
3.1 Introduction. General concepts
3.2 Regression: least squares method, regression lines
3.3 Total variance. Residual variance and explained variance
3.4 Correlation: linear correlation coefficient
3.5 Other regression models: exponential model and potential model
3.6 Hypothesis testing of regression parameters
- Cao R. Abad, Francisco M. Fernandez, et al, "Introduction to statistics and its applications" Ed Pyramid (Grupo Anaya, SA), Madrid, 2001.
- Theodore Colton, "Statistics in Medicine" Ed Masson-Litle, Brown, SA, Barcelona, 1995.
- Martín Andrés, A., Luna del Castillo, J. D., "Biostatistics for the Health Sciences" SL Standard Ed (4th edition), Madrid, 1994.
- Milton, JS, "Statistics for Biology and Health Sciences" McGraw-Hill Interamericana, Madrid, 2001.
- Peña Sánchez de Rivera D., "Statistical Models and Methods. I. Fundamentals "Alianza editorial, SA, Madrid, 2000.
- V. Quesada, Isidoro A., López LA, 'Course and exercises of Statistics "Ed Alhambra SA, Madrid, 1982.
- Sánchez M., Frutos G., Cuesta LP, "Statistical and Applied Mathematics. Edit studies aimed at Pharmacy Editorial Síntesis SA, Madrid, 1996.
Transversal skills.
CP01 Critical and self-critical capacity.
CI01 Ability of analysis and synthesis.
CI07 Basic Computer Skills
CI08 Information management skills (ability to search and analyze information from
Various sources
CI09 Troubleshooting
CI10 Decision making
CS01 Ability to apply knowledge in practice
CS03 Ability to learn
Specific skills.
FM01 Apply the knowledge of Mathematics to the pharmaceutical sciences.
FM02 Apply computational techniques and data processing, in relation to data information
Physical, chemical and biological
FM03 Design experiments based on statistical criteria.
FM04 Evaluate scientific data related to medicines and medical devices.
FM05 Use the statistical analysis applied to the pharmaceutical sciences.
Since the course is essentially practical emphasis will be on developing the content with simplicity without sacrificing accuracy.
- Large group lectures: in each class time will be devoted to the introduction, exhibition or illustration of a theoretical question, and the rest to the resolution of problems or exercises related to that issue.
- Small group interactive class: students will be given problem sets, corresponding to the contents of each of the program items. The student will try, with the help of work done in the previous section, to solve, or if necessary, fix them in the classroom, with active participation. These class will be compulsary.
- Interactive computer classes in small group: Data entry and coding (practice with EXCEL) for later use in a statistical package (R software, SPSS and its non-propietary version PSPP). These class will be compulsary and there will be a final exam.
- The tutorial hours in small groups are devoted, individually or in groups, to address uncertainties and constraints as they arise, and individual monitoring of each student.
-Repeating students of the subject that they wish, may request that their continuous assessment grade from the previous course be taken into account.
-Evaluation of skills.
-In the exam:
FM03, FM05, CI01, CI09, CI10, CP01 and CS01
-During the laboratory practices:
FM02, FM05, CI07 and CI08
-During the interactive classes:
FM04, FM05, CI09, CI10, CP01, CP02, CS01 and CS03
The score of each student will be done through continuous assessment and performance at the final examination following a calendar fixed by the Faculty. The examination will consist of problem solving.
Continuous assessment will be made by checking written controls, student participation in class and tutorial hours.
The mark of the student will be the sum of the 80% of the final exam mark and the 20% of the the continuous assessment.
For the second opportunity the same standards of assesment and the mark of continuous evaluation of the first opportunity will be kept.
Computer practices carried out and passed will be kept as passed in successive academic years.
FACE WORK IN THE CLASSROOM
Large group lectures 22
Reduced group interactive classes 10
Interactive computer lessons in a small group 9
Tutorial hours in very small groups or individualized 4
Total 45 hours of classroom work
PERSONAL WORK STUDENT / A
Individual self-study or group 45
Writing exercises, conclusions or other work 13.5
Jobs in computer 9
Total hours of personal work student at 67.5
The course devotes much time to solving exercises. Obviously, it is considered a fundamental aspect in the learning of the subject, so it is recommended:
- Try to solve the problems of the problem sheets.
- Use the literature to consolidate the knowledge and techniques for solving the problems given in the problem sheets.
- Attend the tutorial hours to solve the doubts that arise throughout the course.
– To use the web site virtual of the USC to accede to the didactical material.
PLAN DE CONTINGENCIA
En caso de que tenga que pasar a los escenarios 2 o 3,
Contenido
Se realizarían los siguientes cambios en el programa:
a) En la sección 2.3, se elimina el encabezado:
Relación entre intervalos de confianza y contrastes de hipótesis.
b) La sección 2.7 se eliminaría en su totalidad
2.7 Contrastes de bondad de ajuste: contraste chi-cuadrado de Pearson; el contraste de Kolmogorov-Smirnov; contrastes de normalidad
c) La Sección 3.5 se eliminaría en su totalidad
3.5 Otros modelos de regresión: el modelo exponencial y el modelo potencial
Metodología
a) Escenario 2: distanciamiento
1. Las clases expositivas respetarán la capacidad del aula y podrán retransmitirse en el horario fijo o grabarse para su visualización a través de los equipos de la plataforma.
b) En el caso del Escenario 3: cierre de las instalaciones,
1. Las clases expositivas serán reemplazadas por videos explicativos del material de enseñanza que ya está disponible para los estudiantes, utilizando tanto la plataforma Teams como el curso virtual.
2. Las clases interactivas serán reemplazadas por la entrega de boletines semanales de problemas. La entrega de al menos dos problemas resueltos de un boletín contará como una ayuda para la evaluación continua,
3. Las prácticas de laboratorio, en las fechas ya programadas, consistirán en la visualización de videos explicativos y la realización de un trabajo por parte del alumnado.
Evaluación
- Los controles se llevarán a cabo en línea a través del curso virtual y serán durante el horario escolar.
- La evaluación continua se realizará mediante controles escritos y telemáticos, y la entrega de boletines resueltos.
- El examen final será presencial a menos que lo autorice la Facultad. En este caso se realizará de forma electrónica (plataforma de equipos y / o curso virtual).
Al comienzo del examen, cada alumno tendrá a su disposición en el campus virtual una declaración personalizada con problemas similares a los propuestos y resueltos en la asignatura. Deberá resolverlos en el plazo que se le indicará y entregar en la tarea correspondiente del campus virtual de la asignatura una copia de las soluciones, escrita a mano, en un archivo PDF de buena calidad.
- La calificación del alumno será la suma del 60% de la nota del examen final y el 40% de la correspondiente a la evaluación continua.
- En la segunda oportunidad se mantendrán las mismas condiciones de evaluación y la nota de la evaluación continua de la primera oportunidad.
Enrique Macías Virgós
Coordinador/a- Department
- Mathematics
- Area
- Geometry and Topology
- Phone
- 881813153
- quique.macias [at] usc.es
- Category
- Professor: University Professor
Jose Carlos Diaz Ramos
- Department
- Mathematics
- Area
- Geometry and Topology
- Phone
- 881813363
- josecarlos.diaz [at] usc.es
- Category
- Professor: Temporary PhD professor
Miguel Dominguez Vazquez
- Department
- Mathematics
- Area
- Geometry and Topology
- Phone
- 881813156
- miguel.dominguez [at] usc.es
- Category
- Researcher: Ramón y Cajal
Rodrigo Mariño Villar
- Department
- Mathematics
- Area
- Geometry and Topology
- rodrigo.marino [at] rai.usc.es
- Category
- Xunta Pre-doctoral Contract
Alberto Rodriguez Vazquez
- Department
- Mathematics
- Area
- Geometry and Topology
- alberto.rodriguez.vazquez1 [at] rai.usc.es
- Category
- Ministry Pre-doctoral Contract
| Wednesday | |||
|---|---|---|---|
| 10:00-11:00 | Grupo A3/CLIS_03 | Galician | 5035 Inorganic Chemistry Seminar Room |
| 12:10-13:10 | Grupo B3/CLIS_06 | Galician | 5035 Inorganic Chemistry Seminar Room |
| 13:10-14:10 | Grupo B2/CLIS_05 | Galician | 5035 Bromatology Seminar Room |
| 18:30-19:30 | Grupo C3 /CLIS_09 | Spanish | 5035 Inorganic Chemistry Seminar Room |
| 19:30-20:30 | Grupo C2/CLIS_08 | Spanish | 5035 Bromatology Seminar Room |
| Thursday | |||
| 09:00-10:00 | Grupo A1/CLIS_01 | Galician | 5035 Animal Physiology Seminar Room |
| 13:10-14:10 | Grupo B1/CLIS_04 | Galician | 5035 Animal Physiology Seminar Room |
| 19:30-20:30 | Grupo C1/CLIS_07 | Spanish | 5035 Animal Physiology Seminar Room |
| Friday | |||
| 10:00-11:00 | Grupo A2/CLIS_02 | Galician | 5035 Bromatology Seminar Room |