2024
Applied Statistics
Name: Applied Statistics
Code: MAT13640L
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
Outcomes:
• Knowledge of the fundamental statistical principles, concepts and tools in the analysis of various experimental designs;
• Learn to validate the assumptions of different parametric approaches and look for alternatives when they are not valid;
• Learn to analyze the association and correlation involving categorical variables;
• Knowledge of the principles of a generalized linear model (GLM) in order to identify, adjust and interpret a GLM with defined response in categories.
• Knowledge to use the concepts, methods and tools to analyze multivariate data, using description and simplification techniques and identifying patterns;
Competences:
• Ability to critically select and organize information;
• Ability to apply various statistical tools in different contexts to aid decision making;
• Ability to select the correct statistical models;
• Ability to have the capacity for abstraction, selection of statistical models and critical spirit;
• Ability to work in a team.
• Knowledge of the fundamental statistical principles, concepts and tools in the analysis of various experimental designs;
• Learn to validate the assumptions of different parametric approaches and look for alternatives when they are not valid;
• Learn to analyze the association and correlation involving categorical variables;
• Knowledge of the principles of a generalized linear model (GLM) in order to identify, adjust and interpret a GLM with defined response in categories.
• Knowledge to use the concepts, methods and tools to analyze multivariate data, using description and simplification techniques and identifying patterns;
Competences:
• Ability to critically select and organize information;
• Ability to apply various statistical tools in different contexts to aid decision making;
• Ability to select the correct statistical models;
• Ability to have the capacity for abstraction, selection of statistical models and critical spirit;
• Ability to work in a team.
Contents
- Analysis of variance models: fixed effects and random effects (single and multiple factor). Multiple comparisons. Other approaches when assumptions are not verified.
- Analysis of Covariance.
- Introduction to Categorical Data Analysis. Contingency Tables. Correlation measures with at least one categorical variable. Characterization of a generalized linear model. Models with categorical response variables.
- Introduction to Principal Component Analysis.
- Introduction to Cluster Analysis.
- Analysis of Covariance.
- Introduction to Categorical Data Analysis. Contingency Tables. Correlation measures with at least one categorical variable. Characterization of a generalized linear model. Models with categorical response variables.
- Introduction to Principal Component Analysis.
- Introduction to Cluster Analysis.
Teaching Methods
The teaching sessions are theoretical-practical, combining the concepts with their application to concrete cases. The sessions include the resolution of practical exercises with the aid of statistical software, using real data whenever possible, being the students encouraged to actively participate in their resolution and / or discussion. In addition to the sessions, students are encouraged to solve practical exercises on their own in order to develop autonomy.
Two practical group works with R software is mandatory, with a report that includes analysis of some problems and recommendations.
In the continuous evaluation in addition to the practical works (weighting of 30% each) will be taken one test (40%). At each evaluation moment the minimum grade is 7.
In the evaluation b final exam, the final grade will be the one obtained in the exam.
Two practical group works with R software is mandatory, with a report that includes analysis of some problems and recommendations.
In the continuous evaluation in addition to the practical works (weighting of 30% each) will be taken one test (40%). At each evaluation moment the minimum grade is 7.
In the evaluation b final exam, the final grade will be the one obtained in the exam.
Assessment
Two tests. The final grade corresponds to the weighted average of the 2 tests grades. The student completes successfully the course if he/she obtains a) a final grade greater than or equal to 9.5 values, b) a grade greater than 7 in both tests and if he/she attends at least 75% of the classes. Otherwise he/she will have to take an exam.
or
Exam. The final grade corresponds to the grade in the exam. The student completes successfully the course if he/she obtains a final grade greater than or equal to 9.5.
In either case, assessment by two tests or by exam, the students may be summoned for an oral test to confirm the final grade.
or
Exam. The final grade corresponds to the grade in the exam. The student completes successfully the course if he/she obtains a final grade greater than or equal to 9.5.
In either case, assessment by two tests or by exam, the students may be summoned for an oral test to confirm the final grade.
Teaching Staff
- Paulo de Jesus Infante dos Santos [responsible]
Certificações
