Statistics II

Code:

EIG0018

Acronym:

E II

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2013/2014 - 2S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Engineering and Industrial Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIG	119	Syllabus since 2006/2007	2	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

The aim of the courses Statistics I and II is to endow students with an integrated vision of the basic concepts and statistic techniques frequently applied. At the end of these courses, students should be able to use methods of statistic analysis autonomously in statistical decision making. Statistics II is manly focused on applying statistical inference techniques.

Learning outcomes and competences

At the end of this course unit students should be able to:

(i) define hypothesis and test them statistically;

(ii) perform different types of parametric and non-parametric tests;

(iii) perform analysis of variance;

(iv) design simple experiments;

(v) perform regression analysis;

(vi) use spreadsheets and statistical packages to apply the above mentioned techniques.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Basic spreadsheets skills.

EIG0015: All topics.

Program

TESTING HYPOTHESIS: Analysis of the basic procedures when running tests of hypothesis; Relationship between testing hypothesis and confidence intervals; most common tests concerning one or two populations: dispersion and location tests.

NONPARAMETRIC TESTS: Goodness of fit, location, randomness and association tests

ANALYSIS OF VARIANCE: Models with one or two factors with fixed, variable and mixes effects

DESIGN OF EXPERIMENTS: Introduction to the Design of Experiments; 2k Factorial Design; Fractional Factorial Design.

REFRESSION: Simple and multple linear regression models; Stepwise linear regression; Nonlinear regression, with and without transformation os variables.

Mandatory literature

Rui Campos Guimarães, José A. Sarsfield Cabral; Estatística. ISBN: 978-989-642-108-3
Jay L. Devore, Kenneth N. Berk; Modern mathematical statistics with applications. ISBN: 978-1-4614-0390-6
Thomas H. Wonnacott, Ronald J. Wonnacott; Introductory statistics. ISBN: 0-471-51733-X

Teaching methods and learning activities

The methods and techniques are introduced using systematically practical examples. The learning process is complemented with problem solving sessions supported by computer software and teamwork assignments.

Software

Folha de Cálculo
SPSS

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	70,00
Teste	10,00
Trabalho laboratorial	20,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de relatório/dissertação/tese	10,00
Estudo autónomo	60,00
Frequência das aulas	56,00
Trabalho laboratorial	36,00
Total:	162,00

Eligibility for exams

Admission criteria set according to Article 4 of General Evaluation Rules of FEUP.

Calculation formula of final grade

The final mark (CF) will be obtained by the following formula:
           CF = 0.30 TG + 0.70 EF

TG - Teamwork assignments:
- 2 small size teamwork assignments (TG1 e TG2).

EF - Final Exam
- open book exam.

To pass this course, apart from a final grade no less than 10, is required a minimum grade of 7 in the final exam.

Classification improvement

Students may improve the Final Exam (FE) mark.

The component teamwork assignments (TG) is not possible to improve.