ISLA Santarém 986

Statistics II

• ApresentaçãoPresentation

  The course deepens the analysis of data, statistical inference, and regression, focusing on the appropriate choice of methods, the formulation of problems in statistical language, and the use of software (SPSS, R) to support decision-making in contexts of uncertainty.
• ProgramaProgramme

  1. Statistical inference 1.1. Simple random sample 1.2. Punctual estimation 1.3. Basics: parameter, statistics, estimator, estimation 1.4. Properties of estimators 1.5. Some sample distributions 1.6. Interval estimation 1.7. Notion of confidence level 1.8. Confidence intervals for different parameters. Properties. 2. Hypothesis tests 2.1. Formulation of a hypothesis test 2.2. Basic concepts: null hypothesis and alternative hypothesis, error types, critical region, and p-value 2.3 Notion of significance level 2.4. Parametric tests and non-parametric tests 3. Bivariate analysis 3.1. Contingency tables 3.2. Independence tests 3.3. Association measures 3.4 Correlation 4. Linear regression 4.1. Linear regression model 4.2. Least squares method, residuals, and correlation coefficient 4.3. Linear regression assumptions 4.4. Model quality assessment 4.5. Analysis of variance 4.6 Coefficient of determination and residue analysis
• ObjectivosObjectives

  O1. Present the main concepts of statistical inference, hypothesis testing, bivariate analysis and linear regression; O2. Recognize appropriate procedures in the choice and application of statistical methods, as well as their practical suitability in decision making; O3. Perform data analysis using statistical analysis software; O4. Formulate practical problems and express practical situations using statistical language. At the end of the curricular unit students should be able to: C1. Analyze data by applying advanced statistical methodologies; C2. Express situations of uncertainty relevant to decision making using statistical language; C3. Use statistical analysis software, such as SPSS and R Commander, and interpret the outputs resulting from the application of statistical methods.
• BibliografiaBibliography

  Botelho, M., & Laureano, R. (2017). SPSS Statistics – O meu manual de consulta rápida. 3.ª Edição, Edições Sílabo. Fidell, L., Tabachnick, B. (2019). Using Multivariate Statistics. 7th Edition, Pearson. Malhotra N. (2019). Marketing Research: An Applied Orientation. 7th Edition, Pearson.  Marôco, J. (2021). Análise Estatística com o SPSS Statistics. 8.ª Edição, ReportNumber. Reis, E. et al. (2018). Estatística Aplicada 2. Volume 2, 6.ª Edição revista e aumentada, Edições Sílabo. Triola, M. et al. (2018). Biostatistics for the Biological and Health Sciences. 2nd Edition, Pearson.
• MetodologiaMethodology

  Face-to-face: 1. Expository method: the defined program content is explored. In addition, the demonstrative method is used to present concrete examples of how the content can be applied. 2. Laboratory practice: based on the Problem-Based Learning (PBL) methodology, aiming to find solutions to problems identified by students or proposed by the teacher, using generative AI and simple machine learning tools to support exploratory analysis, predictive modeling, and risk scenario simulation. Independent: 3. Guided research proposed by the teacher to consolidate the topics under study and provide input for the practical work developed by the students.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  O método de avaliação consiste desenvolvimento de um trabalho de grupo aplicado a um caso real com recurso a SPSS (50%) e uma prova escrita no final do semestre (50%). 
  A avaliação final será concretizada através de exame (100%).