# Statistical Modeling and Inference

## Objectives

We want the student to obtain a solid knowledge in areas of inference and statistical modelization which, usually, are not taught at the undergraduate level. Emphasis is placed in aspects related with statistical modelization presented in a more general context than just only the linear model setting, in such a way that the student may acquire a wider view of the modelization and inference process, which is essential for everyone that is willing to make solid applications in their future professional life. The association between modelization and inference wants to give this more embracing view and the possibility of applications in several areas, since Generalized and Mixed linear models, taught in this course, allow for a wide range of applications of statistical inference in many areas.

## General characterization

10818

6.0

Weekly - 4

Total - 56

Português

### Prerequisites

Are pre-requisites for a good performance and understanding of the topics in this course:

• a good knowledge of the matters related with the couses Statistics and Probability I and II;
• a relatively good knowledge of matricial Algebra;
• a predisposition, by the students, to relate the topics taught in this course with topics they were taught in other previous courses in their undergraduate courses, namely topics on estimation and testing of hypotheses.

Available soon

### Teaching method

 Classes are taught in a theoretical-practical regime, initially having an expository part followed by the resolution of exercises on the taught topics. The evaluation of the course will be done through the realization of 2 midterms, with a weight of 30% each for the final grade, and the resolution of 2 sets of problems, each one of them with a weight of 20% for the final grade.

### Evaluation method

The evaluation of the course will be done through the realization of 2 midterms, with a weight of 30% each for the final grade, and the resolution of 2 sets of problems, each one of them with a weight of 20% for the final grade.

## Subject matter

1. Review of fundamental concepts about point and interval estimation
2. The Exponential family of distributions
• The Exponential family of 1 and several parameters: fundamental concepts and results
• Distributions in the Exponential family
• Estimation in the Exponential family
3. Generalized Linear Models
• Error distributions as members of the Exponential family