Computational Statistics

Recommended Prerequisites

Statistics.

Teaching Methods

Teaching is carried out within practical sessions in computer lab. In addition to classes, students will develop, in groups of 2 or 3, a computational course work covering the statistical analysis of a data set. This course work comprises the production of a written report and its oral presentation. Active participation of students in class discussions, individual and team work, and correct use of the available office hours should be strongly encouraged by the instructor.

Learning Outcomes

The main objective of this course is to prepare students to treat, analyze and retrieve information from observed statistical facts. It is intended that students apply the theoretical knowledge they have acquired in the discipline of classical Statistics and that they extend their knowledge to other topics of Statistics, that will be addressed in a more practical way.

Students will deal with real statistical data, as well as with simulated data, using the SPSS software.

The main competencies to be developed are: ability to calculate, using computational tools; knowledge of mathematical results; formulating and solving problems; design and use of mathematical models for real situations;  individual initiative; teamwork; independent learning; critical awareness.

Work Placement(s)

No

Syllabus

Exploratory data analysis. Probability distributions. Association and regression. Brief reference to sampling designs. Point and interval estimation. Parametric and nonparametric hypothesis testing. Inference in regression models. Analysis of variance.

Head Lecturer(s)

Cristina Maria Tavares Martins

Assessment Methods

Assessment
Exam (85%) ou Midterm exam (85%) + Project (15%): 100.0%

Bibliography

A. Field, Discovering Statistics Using SPSS, Sage Publications, 2005

 

D. S. Moore, G. P. McCabe, Introduction to the Practice of Statistics, W. H. Freeman and Company, 2006

 

D. Pestana, S. Velosa, Introdução à Probabilidade e à Estatística, Fundação Calouste Gulbenkian, 2002

 

M. J. Norusis, SPSS 16.0 Guide to Data Analysis, Prentice-Hall, 2008