Applied Statistics

Code:

M2020

Acronym:

M2020

Level:

200

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2016/2017 - 2S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Bachelor in Mathematics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:B	0	Official Study Plan	3	-	6	56	162
L:M	54	Official Study Plan	2	-	6	56	162
L:Q	0	study plan from 2016/17	3	-	6	56	162
MI:ERS	4	Plano Oficial desde ano letivo 2014	2	-	6	56	162

Teaching language

Portuguese

Objectives

Upon completing this course, the student should:

- have a good insight of the fundamental concepts and principles of statistics, and in particular those from basic inference statistics.

- know the common inference statistical  methods and how to apply them to concrete situations;

- know the basic properties of regression linear models and be able to apply the theory to the analysis of real data, including model fitting, interpretation and forecasting;

- be able to identify and formulate a problem, to choose adequate statistical methods and to analyze and interpret in a critical way the obtained results.

It is also expected that the student acquires familiarity with the programing language and software environment R, in the framework of problems solving.

Learning outcomes and competences

Those mentioned in the above box.

Working method

Presencial

Program

Parametric estimation: point and interval estimation. Hypothesis tests. Neyman-Pearson lemma.
Hypotheses tests for a given distribution. Hypotheses tests for normality. 
Parametric hypotheses tests for one sample: test for a single mean and test for a single proportion (exact test and approximation using the normal distribution).
Parametric hypotheses tests for two samples: difference of means and difference of proportions.  
Nonparametric tests alternative to the previous parametric tests.
Hypotheses tests (parametric and non-parametric) for more than two samples of a continuous random variable. Tests for categorical data. 
Pearson and Spearman correlation analysis and correspondent hypotheses tests.
Maximum likelihood estimation. 
Linear regression models: parameter estimation by the methods of maximum likelihood and least squares, estimators properties, diagnosis and forecasting.

Mandatory literature

A. Rita Gaio; Apostamentos disponibilizados pelo docente

Complementary Bibliography

Casella George; Statistical inference. ISBN: 0-534-24312-6
Nolan Deborah; Stat labs. ISBN: 0-387-98974-9
Pestana Dinis Duarte; Introdução à probabilidade e à estatística. ISBN: 972-31-0954-9

Teaching methods and learning activities

Lectures and classes: The contents of the syllabus are presented in the lectures, illustrated with several examples. In the practical classes, exercises and related problems are solved and discussed. Several real data sets will be analyzed using the statistical software R. All resources are available for students at the unit’s web page.

 

Software

R

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	100,00
Total:	100,00

Calculation formula of final grade

The final mark will be that obtained from the final examination. Students marking 17.5 (ou of 20) or higher may be asked to do an extra examination in order to keep their mark.