2024
Categorical Data Analysis
Name: Categorical Data Analysis
Code: MAT13608M
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
The learning outcomes are:
• To know how to analyze the association and correlation involving categorical variables;
• To know the principles of a generalized linear model in order to identify, adjust and interpret a model of this type;
• To know and apply the basic principles of modeling with this type of models.;
• To know how to critically interpret the results obtained from the statistical software.
The skills developed are:
• Ability to critically and autonomously know how to construct and analyze different generalized linear models and to apply these methodologies in their professional activity;
• To acquire the basic principles of statistical modeling and to know the main modeling phases of a generalized linear model;
• Ability to interpret problems for longitudinal data;
• Ability to research and understand related literature in order to apply to other models for categorical data;
• Ability to use R for categorical data analysis.
• To know how to analyze the association and correlation involving categorical variables;
• To know the principles of a generalized linear model in order to identify, adjust and interpret a model of this type;
• To know and apply the basic principles of modeling with this type of models.;
• To know how to critically interpret the results obtained from the statistical software.
The skills developed are:
• Ability to critically and autonomously know how to construct and analyze different generalized linear models and to apply these methodologies in their professional activity;
• To acquire the basic principles of statistical modeling and to know the main modeling phases of a generalized linear model;
• Ability to interpret problems for longitudinal data;
• Ability to research and understand related literature in order to apply to other models for categorical data;
• Ability to use R for categorical data analysis.
Contents
• Contingency Tables and association and correlation measures with categorical variables.
• Generalized linear models: characterization, link functions, statistical modelling, assumptions, residual analysis, validation and inference.
• Discrete models: logit, probit, log-log, ordinal, Multinomial, Poisson, Negative Binomial, Inverse-Gaussian and Gama.
• Generalized additive models (GAM).
• Generalized Estimating Equations (GEE) and Generalized Linear Mixed Models (GLMM).
• Introduction to zero inflated models (ZIF).
• Generalized linear models: characterization, link functions, statistical modelling, assumptions, residual analysis, validation and inference.
• Discrete models: logit, probit, log-log, ordinal, Multinomial, Poisson, Negative Binomial, Inverse-Gaussian and Gama.
• Generalized additive models (GAM).
• Generalized Estimating Equations (GEE) and Generalized Linear Mixed Models (GLMM).
• Introduction to zero inflated models (ZIF).
Teaching Methods
Theoretical-practical lessons combining the concepts with their application to real data from different areas, making students aware of the importance of the exposed subject. The sessions include modelling and data analysis with the help of statistical software. Students actively participating in their resolution and / or discussion. In addition students are encouraged to solve practical exercises on their own in order to develop autonomy.
Focus on modeling, critical interpretation and data analysis using outputs from the software used.
Evaluation:
In the continuous evaluation regime, two compulsory works will be carried out, each counting 50% of the final grade.
If not approved in continuous assessment, the student takes an exam and individual assignments are no longer required, but will have a weight of 25% in the final grade.
Focus on modeling, critical interpretation and data analysis using outputs from the software used.
Evaluation:
In the continuous evaluation regime, two compulsory works will be carried out, each counting 50% of the final grade.
If not approved in continuous assessment, the student takes an exam and individual assignments are no longer required, but will have a weight of 25% in the final grade.
Assessment
In continuous assessment, students carry out two works (50% each). The final grade is the result of the arithmetic average between the two works.
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
- Paulo de Jesus Infante dos Santos [responsible]
Certificações
