2023
Statistical Modeling
Name: Statistical Modeling
Code: MAT13598D
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: B-learning
Sustainable Development Goals
Learning Goals
Objectives:
It is intended to strengthen the basic training in Probability and Statistics with the complement of fundamental knowledge of probability theory and statistical inference theory.
Skills:
To know how to use different concepts and fundamental statistical tools in the statistical inference and their application areas.
To know how to use different estimation methods.
To know the general statistical theory of hypothesis testing.
To know the basic probabilistic models to modelling and data analysis.
It is intended to strengthen the basic training in Probability and Statistics with the complement of fundamental knowledge of probability theory and statistical inference theory.
Skills:
To know how to use different concepts and fundamental statistical tools in the statistical inference and their application areas.
To know how to use different estimation methods.
To know the general statistical theory of hypothesis testing.
To know the basic probabilistic models to modelling and data analysis.
Contents
Unidimensional and multidimensional random variables.
Moment generating function, probability generating function and characteristic function. Laplace transformations.
Main probability distributions.
Rando variables and random vectors transformations.
Stochastic convergences , central limit theorem and law of large numbers.
Pint estimation. Estimation methods and properties of estimators. BLUE and UMVUE estimators. Crámer-Rao Lower Band.
Interval estimation.
Hypothesis tests. Likelihood ratio tests. Neyman-Pearson's lemma.
Regression analysis, analysis of variance and analysis of covariance.
Moment generating function, probability generating function and characteristic function. Laplace transformations.
Main probability distributions.
Rando variables and random vectors transformations.
Stochastic convergences , central limit theorem and law of large numbers.
Pint estimation. Estimation methods and properties of estimators. BLUE and UMVUE estimators. Crámer-Rao Lower Band.
Interval estimation.
Hypothesis tests. Likelihood ratio tests. Neyman-Pearson's lemma.
Regression analysis, analysis of variance and analysis of covariance.
Teaching Methods
The curricular unit is organized in theoretical-practical classes. The classes are plenary and are based on the deduction, understanding and interpretation of various statistical techniques always fostering a critical attitude and scientific rigor in students. Introduction of theoretical concepts using application examples covering various areas.
The evaluation is recommended to be continuous through the exercise of evaluation works with resolution of exercises, incentivizing the bibliographic research, the study by diverse sources and the acquisition of the theoretical concepts through a dedicated study.
If the average of the papers submitted for continuous assessment is not equal to or higher than 10, the student takes a final exam.
The evaluation is recommended to be continuous through the exercise of evaluation works with resolution of exercises, incentivizing the bibliographic research, the study by diverse sources and the acquisition of the theoretical concepts through a dedicated study.
If the average of the papers submitted for continuous assessment is not equal to or higher than 10, the student takes a final exam.
Teaching Staff
- Luís Miguel Lindinho da Cunha Mendes Grilo [responsible]
