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Presentation
Presentation
Basic elements of probability and statistics applied to veterinary medicine.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor; Master Degree | Semestral | 4
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT478-24667
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
1. Data collection and organization. Types of variables. 2. Introduction to sampling methods. 3. Descriptive statistics. 4. Basic notions of probabilities, random variables and examples of probability distributions. 5. Law of large numbers, distribution of the means of random samples, central limit theorem and estimation of confidence intervals. Statistical inference. 6. Introduction to hypothesis testing for means and proportions. Sampling from normal distributions, with known and unknown variances. 7. Statistical tests for categorical or qualitative variables. 8. Parametric tests. 9. Non-parametric tests. 10. Association measures. Pearson and Spearman correlation. Simple linear and logistic regression models. 11. Use of the internet and online bibliographic databases to search for technical and scientific documents. 12. Use of software to solve problems with the application of concepts covered in the theoretical component.
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Objectives
Objectives
1. Use statistical methodologies to gather and summarize data in the field of veterinary sciences and proceed to do their exploratory analysis. 2. Recognize the conditions underlying the applicability of the theoretical models used in statistical analysis, distinguishing the limits of each of these models. 3. Evaluate and interpret the results obtained, using statistical inference. 4. Distinguish between cause-effect relationships and relationships of statistical association between variables. 5. Apply basic skills of research and critical reading of technical and scientific documents. 6. Use statistical analysis software for: i) word processing, construction of tables, graphs; ii) store, capture, process, analyze data using spreadsheets (Microsoft Excel®) and a statistical software package (SPSS or similar); iii) interpretation of results and technical decision making; iv) search for documentation on the internet and online bibliographic databases.
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Teaching methodologies
Teaching methodologies
Expository and interactive classes, inverted classroom, jigsaw technique and its variants, concept maps. Use of computer programs for statistical analysis: Excel and SPSS. Supervised self-learning (practical classes): throughout the semester, students will work in groups (descriptive and inferencial analysis of data), in which they will aply the knowledge acquired in theoretical and theoretical-practical classes, for a total of 12 hours. This work will be supervised by the teachers.
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References
References
- Petrie, A. & Watson, P. (2013). Statistics for veterinary and animal sciences, (3rd Ed.). West Sussex: Wiley-Blackwell. - Daniel, W.W. (2009). Biostatistics: a foundation for analysis in the health sciences, (9th Ed.). NJ: J. Wiley & Sons. - Callegari-Jacques, S.M. (2004). Bioestatística ? Princípios e aplicações, (1.ª Ed. Reimp.). Porto Alegre: Artmed. - Brase, C.H. & Brase, C.P. (2011). Understandable statistics: Concepts and methods, (10th Ed.). Boston: Cengage Learning. - Glantz, S.A. (2012). Primer of biostatistics, (7th Ed.). McGraw-Hill. - Pereira, A. & Poupa, C. (2018). Como escrever uma tese, monografia ou livro científico usando o Word, (7.ª Ed.). Edições Sílabo. - Marôco, J. (2018). Análise estatística com o SPSS Statistics, (7.ª Ed.). Report Number. - Rayat, C.S. (2018). Statistical methods in medical research. Springer Singapore. - Oliveira, A.G. (2014). Bioestatística descodificada, (2.ª Ed.). Lisboa-Porto: LIDEL.
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Assessment
Assessment
Avaliação contínua:
a) Auto-aprendizagem supervisionada (aulas práticas): os alunos farão, ao longo do semestre, um trabalho em grupo, no qual aplicarão os conhecimentos adquiridos nas aulas teóricas e teórico-práticas, num total de 12h. Este trabalho será supervisionado pelos docentes da UC.
b) Avaliação teórica: 2 frequências teóricas (50% cada). O aluno deverá ter 9,5 ou mais valores na soma das duas frequências para obter aprovação na avaliação contínua teórica.
c) Avaliação prática: 3 minitestes (25% cada) e um trabalho de grupo (25%). O aluno deverá ter 9,5 ou mais valores na soma das 4 avaliações práticas para obter aprovação na avaliação contínua prática.
Avaliação no exame final: Em caso de não aprovação ou falta não devidamente justificada à avaliação contínua.
Nota: O aluno tem que obter aprovação na componente teórica e na componente prática da UC para concluir a mesma. A nota final corresponde à média ponderada da nota final teórica com a nota final prática.
Descrição
Ponderação
Teste de avaliação teórico 1 - Estatística descritiva
50%
Teste de avaliação teórico 2 - Estatística inferencial
50%
Prática 1: Minitestes (n=3)
25% cada
Prática 2: Trabalho de grupo 25% Componente teórica 50% Componente prática 50%
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Mobility
Mobility
No
