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ISLA Santarém 2193

Biostatistics

Data and Technology Management in Health
  • ApresentaçãoPresentation
      
  • ProgramaProgramme
    1 Descriptive Statistics  1.1 Population, sample and individual 1.2 Frequency tables and graphical representation 1.3 Statistics - measures of location, dispersion, asymmetry and flatness 1.4Correlation and Regression 2. Hypothesis testing 2.1 Statistical hypotheses; Classification errors and significance levels 2.2 Parametric tests: t-student; t-student for two population means and for more than two populations - One-Way ANOVA. 2.3 Non-parametric tests: paired samples: Friedman, Wilcoxon and McNemar; independent samples: Mann-Whitney U and Kruskal-Wwallis H 2.4 Association and correlation tests. 3. Probability Distributions: Binomial, Poisson and Normal  
  • ObjectivosObjectives
    O1 understand the conditions underlying the applicability of the theoretical models used for statistical analysis, as well as analysing and interpreting the results obtained. O2 master the SPSS computer application to solve problems involving the concepts developed in the course. O3 acquire the ability to analyse data quantitatively and to critically assess and interpret the results of an inferential study in the fields of Health. O4. Characterise the Binomial, Poisson and Normal probability distributions and calculate probabilities from these models.
  • BibliografiaBibliography
    Marôco, João (2018). Análise Estatística com SPSS, ReportNumber. Devore, J. L., Berk, K. N., & Carlton, M. A. (2021). Modern mathematical statistics with applications. 3rd edition. New York: Springer. Gupta, B. C., Guttman, I., & Jayalath, K. P. (2020). Statistics and probability with applications for engineers and scientists. Wiley.
  • MetodologiaMethodology
    Synchronous sessions (distance): MET 1. Active methodologies, including cooperative learning, peer learning, gamification, problem-solving, modeling, and statistical simulations. In-person sessions: MET 2. Use and manipulation of statistical software, specifically Excel and SPSS, for solving proposed problems. Autonomous work: MET 3. Various educational resources (videos, links, apps, notes, exercises, and applications) will be made available on the Moodle platform. The instructor provides feedback (Tutorial Guidance - OT) on the results obtained by the student in solving the proposed problems through the Moodle platform.
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    6
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não
  • AvaliaçãoEvaluation

    Avaliação Curricular (contínua (presencial):
    Esta modalidade de avaliação é constituída por:
    AVAL 1. Portfólio de trabalhos/exercícios de aula com aplicação do SPSS. AVAL 2. Trabalho prático (relatório e projeto).
    AVAL 3. Teste final teórico/prático.
    A classificação final é calculada através da fórmula Classificação Final = 0,2*AVAL 1+0,4*AVAL 2 + 0,4 *AVAL3. O estudante é aprovado se obtiver classificação igual ou superior a 9,5 valores.

    Avaliação Final (presencial) - A: O estudante realiza o exame teórico-prático (100%) e é aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
    Época de Recurso e Época Especial (presencial) - A: O estudante realiza o exame teórico-prático (100%) e fica aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.