2024
Regression and Classification
Name: Regression and Classification
Code: MAT14350L
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
The objectives of this UC are to provide students with the fundamental theoretical concepts for:
Know how to analyze the correlation involving quantitative variables;
Formulate, adjust and interpret a general linear model;
Use multivariate techniques of dependence and interdependence in multivariate data analysis to reduce dimensionality and segment observations.;
Critically interpret the results obtained from the statistical software.
At the end of this UC students are expected to have acquired the following skills:
Autonomously construct and analyze the methods studied in this UC;
Properly apply the various statistical tools addressed in this UC;
Interpret the results by appealing to the critical spirit;
Individual and team work capacity;
Carry out statistical analyses using computer software.
Know how to analyze the correlation involving quantitative variables;
Formulate, adjust and interpret a general linear model;
Use multivariate techniques of dependence and interdependence in multivariate data analysis to reduce dimensionality and segment observations.;
Critically interpret the results obtained from the statistical software.
At the end of this UC students are expected to have acquired the following skills:
Autonomously construct and analyze the methods studied in this UC;
Properly apply the various statistical tools addressed in this UC;
Interpret the results by appealing to the critical spirit;
Individual and team work capacity;
Carry out statistical analyses using computer software.
Contents
Correlation and regression: Simple linear regression and Nonlinear regression
Multiple linear regression
Classification analysis: hierarchical and non-hierarchical
Discriminant analysis
Multinomial regression
Multiple linear regression
Classification analysis: hierarchical and non-hierarchical
Discriminant analysis
Multinomial regression
Teaching Methods
The classes will be taught using projections of support material, complemented with the exhibition of the materials on the board. Special emphasis will be placed on illustrating theoretical content with application examples related to the course area. Classes include the resolution of exercises using statistical programs, and the autonomous work of students is encouraged.
Assessment
In continuous assessment, students carry out 2 individual or group works (50%) and one test (50%). The minimum grade is equal to 8 values in each of the evaluation moments and to obtain approval in this assessment regime, the student must have a final grade (result of the weighted average between the two works and the test) equal to or greater than 10 values.
The final assessment regime consists of a written exam in the regular period and a written exam in the appeal period.
The student is Approved when the final classification equals or exceeds 10 values.
The final assessment regime consists of a written exam in the regular period and a written exam in the appeal period.
The student is Approved when the final classification equals or exceeds 10 values.
Teaching Staff
- Anabela Cristina Cavaco Ferreira Afonso [responsible]
Certificações
