ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 102 Hours of tutorials: 2 Expository Class: 12 Interactive Classroom: 34 Total: 150
Use languages Spanish, Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Faculty of Sciences
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
Introducing to students to the classical statistical techniques of data analysis in research. It is intended that the students acquire skills that enable them to identify situations in which it is possible and necessary a statistical analysis of the data. As well as know used the R program results and interpret the outputs.
Topic 1.- Basic techniques of statistical inference.
Checking the dataset: missing values, outliers, normality, homoscedasticity. Estimation by confidence intervals. Estimation by hypothesis test.
Topic 2.- Design of experiments and analysis of results.
Designs with one factor. Analysis of variance. Multiple comparisons. Designs with two or more factors (models without replication and with replication). Repeated measures.
Topic 3.- Non-parametric methods and analysis of categorical data.
Non-parametric tests for completely randomized designs and block designs. The chi-square test in contingency tables and proportions.
Topic 4.- Regression Models.
Correlation. Simple linear regression. Inference in model estimation and prediction. Non linear regression. Multiple regression. Analysis of collinearity. Methods of diagnosis.
Topic 5.- Models with qualitative response.
Binary logistic regression models. Estimation and confidence intervals. Prediction. ROC curve.
Topic 6.- Classic techniques of multivariate analysis.
Dependency and interdependence techniques for data analysis. Principal component analysis, correspondence analysis and cluster analysis.
Practical lectures: DATA ANALYSIS WITH R.
- Working with R for researching.
- Data frames and reading data into R. Descriptive statistics. Comparison of groups. Visualizing data.
- Parametric and non-parametric methods of inference.
- Analysis of count data.
- Regression models and logistic regression.
- Classic multivariate methods with R.
Basic bibliography
-ALDÁS, J.; URIEL, E. (2017): Análisis multivariante aplicado con R. Paraninfo.
-ALKARKHI, A. F. M.; ALQARAGHULI, W. A. A. (2019). Easy Statistics for Food Science with R. Academic Press.
-ÁLVAREZ CÁCERES, R. (2007). Estadística aplicada a las Ciencias de la Salud. Díaz de Santos.
-DANIEL, W. W. (2006). Bioestadística: base para el análisis de las ciencias de la salud. Limusa Wiley coop.
-EKSTROM,C. T.; SORENSEN, H. (2011). Statistical Data Analysis for the Life Sciences. CRC Press.
-LALANNE, C. ; MESBAH, M. (2016). Biostatistics and Computer-based Analysis of Health Data using R. ISTE Press Ltd and Elsevier Ltd. eBook ISBN: 9780081011751. URL: https://iacobus.usc.gal/permalink/34CISUG_USC/1od8vts/cdi_askewsholts_v…. Last accessed: May 8, 2024.
-MARTÍNEZ GONZÁLEZ, M. A. (ed.) (2006). Bioestadística amigable. Díaz de Santos.
-RIUS, F.; BARÓN, F. J. (2008). Bioestadística. Thomson-Paraninfo.
-SAMUELS, M. L. et all (2012). Fundamentos de Estadística para las Ciencias de la Vida. Pearson.
-SARABIA ALEGRÍA, J.M; PRIETO MENDOZA, F. E JORDÁ GIL, V. (2018). Prácticas de estadística con R. Pirámide.
-ZAR, J.H. (2010). Biostatistical Analysis. Pearson.
Complementary bibliography
-EVERITT, B. S.; HOTHORN, T. (2010). A Handbook of statistical analysis Using R. Chapman & Hall RC.
-JOHNSON, D. E. (2000). Métodos Multivariados aplicados al análisis de datos. Internacional Thomson Ed.
-LOGAN, M. (2010). Biostatistical design and analysis using R: a practical guide. Wiley-Blackwell.
-MAINDONALD, J.; BRAUN, W. J. (2010). Data Analysis and Graphics Using R. An Example Based Approach. Cambridge.
-PARDO, A.; SAN MARTÍN, R. (2010). Análisis de datos en ciencias sociales y de la salud II. Editorial Síntesis.
-PEÑA SÁNCHEZ DE RIVERA, D. (2002). Análisis de datos multivariantes. Mc Graw Hill.
-QUINN, G. P., KEOUGH, M. J. (2002). Experimental Design and Data Analysis for Biologists. Cambridge University Press.
-RIAL BOUBETA, A.; VARELA MALLOU, J. (2008). Estadística práctica para investigación en Ciencias de la Salud. E d. Netbiblo.
General competences
CG6: Being able to design and development of healthier foods.
CG7: Developing skills and abilities in the statistical analysis and treatment of physical, chemical, microbiological and sensory data of food.
CG11: Acquiring training to develop research activity, being able to formulate hypotheses, collect and interpret information for problem solving following the scientific method, and understanding the importance and limitations of scientific thinking in aspects related to nutrition, Security and food technology.
Basic competences
CB7: Students should be able to apply acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
CB9: That the students know how to communicate their conclusions and the latest knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way.
Transversal competences
CT1: Ability of analysis and synthesis.
CT2: Ability to organize and plan.
CT3: Ability to work in a team.
CT5: Ability to use information and communication technologies.
CT7: Ability to solve problems.
CT8: Ability to make decisions.
CT10: Capacity for critical reasoning and argumentation, and self-critical ability.
CT12: Ability to use information in a foreign language.
CT14: Ability to apply knowledge to practice.
Specific competences
CE1: Knowing the methodology of statistical techniques and sample designs, and be able to formulate statistical hypotheses in problems related to nutrition, safety and food technology.
CE2: Being able to interpret the results of statistical analyzes related to nutrition, food and health aspects.
In the face-to-face classes there will be a brief presentation of the statistical methodology to be addressed, developing it through applications to practical cases. All classes will be in the computer room for students to follow the examples from their initial approach, setting the objectives, conveniently organizing the database, obtaining the results with the computer and interpreting the output provided by the computer program . These classes will use scripts that will be available to students on the USC virtual campus before each session in the computer room.
The face-to-face activities are completed with tutorials, exams and revision.
In the non-contact activities, the students will carry out individual works that will consist of the reasoned problem solving related to the contents of each topic. The USC Virtual Campus will be used to collect the proposed works and deliver the solutions.
The evaluation will be made based on the following sections.
Section 1. The examination of the subject, with a weight of 60%. This test will address questions about statistical methods and reasoned problem solving. There are two opportunities to be held on the official dates set by the center. Competences to evaluate: CG7, CG11, CB7, CT1, CT5, CT7, CT14, CE1, CE2.
Section 2. The activities for the evaluation of the continuous monitoring of the expository and interactive classes. They will be face-to-face and non-face-to-face activities and will have a 40% assessment in the final grade. Competences to evaluate: CG6, CG7, CG11, CB7, CB9, CT1, CT2, CT3, CT5, CT7, CT8, CT10, CT12, CT14, CE1, CE2.
For the assessment based on the two previous sections to be applicable, a minimum grade of 4 out of 10 will be required in section 1 of the assessment. If that minimum is not reached in section 1, the maximum final grade of the subject may not exceed 4.5 out of 10.
Students who do not pass the subject in the first opportunity will have to do a new evaluation test in section 1 on the second opportunity. The note of the first opportunity in section 2 is saved for the second opportunity.
In cases of fraudulent performance of tasks or tests, the provisions of the “Performance Assessment Regulations will apply of the students and the review of the grades ”.
ECTS = 6
Lectures: 12 hours. Estimated hours of non - presential work: 18.
Interactive contact classes: 34 hours. Estimated hours of non - presential work: 54.
Tutorials: 2 hours.
Individual and / or group work. 15 estimated hours of non-face-to-face work.
Examination and revision: 3 hours face-to-face.
Total contact hours: 51.
Estimated total hours of study and personal work: 99.
- Attendance and active participation in the classes.
- Carry out the scheduled activities within the established deadlines.
- Follow up of the readings that are proposed and consult the bibliography.
- Use of the tutorials, both in the hours assigned to them and through the USC-Virtual.
- In the exams, justify all the answers and use the appropriate statistical notation.
In this matter two languages will be used: Galician and Spanish.
Maria De Las Nieves Muñoz Ferreiro
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 982824058
- nieves.munoz [at] usc.es
- Category
- Professor: Collaborator
| Monday | |||
|---|---|---|---|
| 16:00-19:00 | Grupo /CLIL_01 | Spanish | COMPUTER CLASSROOM 2 |
| 01.10.2025 10:00-14:00 | Grupo /CLE_01 | COMPUTER CLASSROOM 1 |
| 06.11.2025 16:00-20:00 | Grupo /CLE_01 | COMPUTER CLASSROOM 1 |