Statistics II

Recommended Prerequisites

Mathematics I, Mathematics II, Statistics I

Teaching Methods

Theoretical-practical classes: in each topic, exposition of introductory questions and of practical examples, motivating theoretical generalizations. Exposition of general theoretical results, supported by theoretical schemes' projection. Problem solving by students, accompanied by teachers. Statistical software is used for some applications.

Learning Outcomes

General objetives:

Understanding the notion of probabilistic model. Basic notions of statistical inference. Linear regression model (lrm); estimation and inference on the parameters of the lrm. Application of these concepts to simple economic and business problems.

Specific objectives:

Common discrete and continuous probabilistic models. Point estimação (methods of moments and of maximum likelihood), confidence intervals and hypotheses tests, namely under Gaussian models. Understanding the main asymptotic results (Law of Large Numbers and Central Limit Theorem) and their application to statistical inference. Application of some of these concepts to the lrm.

Use of software in solving simple exercises. Use of these concepts in practical economic and business problems.

Work Placement(s)

No

Syllabus

1 Common probabilistic models

1.1 Discrete distributions

1.2 Continuous distributions

2 Sampling

2.1 Sampling schemes

2.2 Sample mean and variance

2.3 Central Limit Theorem

3 Parametric estimation

3.1 Point estimation

3.2 Interval estimation

4 Parametric hypotheses tests

5 Linear regression

5.1 Conditional distribution. Conditional expectation.

5.2 Linear regression model

5.3 Ordinary least squares method

5.4 Statistical inference

Head Lecturer(s)

José Maria Ruas Murteira

Assessment Methods

Evaluation
Periodic or by final exam as given in the course information.: 100.0%

Bibliography

Murteira, B., C. Silva Ribeiro, J. Andrade e Silva, C. Pimenta (2015), Introdução à Estatística, Escolar Editora.