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Multivariate Statistics

Code: EIG0059     Acronym: ESTMUL

Keywords
Classification Keyword
OFICIAL Quantitative Methods

Instance: 2018/2019 - 2S Ícone do Moodle

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
MIEGI 103 Syllabus since 2006/2007 2 - 6 56 162

Teaching Staff - Responsibilities

Teacher Responsibility
Armando Luís Ferreira Leitão
António Miguel da Fonseca Fernandes Gomes

Teaching - Hours

Lectures: 2,00
Recitations: 2,00
Type Teacher Classes Hour
Lectures Totals 1 2,00
Armando Luís Ferreira Leitão 1,00
António Miguel da Fonseca Fernandes Gomes 1,00
Recitations Totals 4 8,00
Armando Luís Ferreira Leitão 6,00
António Miguel da Fonseca Fernandes Gomes 2,00
Mais informaçõesLast updated on 2019-01-29.

Fields changed: Calculation formula of final grade, Bibliografia Obrigatória, Componentes de Avaliação e Ocupação, Programa

Teaching language

Suitable for English-speaking students

Objectives

The aim of the courses Statistics and Multivariate Statistics 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. 

Learning outcomes and competences

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

  1. perform regression analysis;
  2. perform analysis of variance;
  3. design simple experiments;
  4. perform multivariate analysis of variance;
  5. perform exploratory factor analysis;
  6. 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 and matrix calculus.

EIG0072: All topics.

Program


  • INTRODUCTION: Introduction to Multivariate Statistics. Statististics and Graphical Representations for Multivariare data.

  • FACTORIAL ANALYSIS OF VARIANCE (ANOVA): Introduction. ANOVA Model (Fixed and Random Effects, Multiple Comparisons). ANOVA Model with 2 Factors (Fixed and Random Effects, Interation between Factors, Constrasts). Extension to Additional Factors. ANOVA Assumptions.

  • DESIGN OF EXPERIMENTS: Introduction to the Design of Experiments. Randomization and Replication. Two Level Factorial Designs (Full and Fractional).

  • DATA REDUCTION TECHNIQUES: Factors and Principal Components (Graphical and Mathematical Representations). Princiapal Components Analysis. Exploratory Factorial Analysis. Factors e Principal Components Extraction (Eigenvalues and Scree Plot). Rotating Factors and Principal Components. Interpretation.

  • REGRESSION: Introduction. Multiple Linear Regression (Parameters Estimation, Inference about Parameters, Predictors Slection, Forecasts based on the Multiple Linear Regression Model, Qualitative Predictor, Collinearity). Assumptions and Residual Analysis. Linear Regression with variable Tansformations.

  • MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA): Theory and Application. Assumptions. "Follow-up" Analysis. Interpretation.


 

Mandatory literature

Rui Campos Guimarães, José A. Sarsfield Cabral; Estatística. ISBN: 978-989-642-108-3
Joseph F. Hair, Jr., ... [et al.]; Multivariate data analysis. ISBN: 978-0-13-515309-3
Armando Leitão; Factorial Experimentation (Notes available in Moodle)

Complementary Bibliography

Douglas C. Montgomery, George C. Runger; Applied Statistics and Probability for Engineers, Wiley, 2014. ISBN: 978-1-118-74412-3
S. Christian Albright, Wayne L. Winston; Business Analytics: Data Analysis and Decision Making, College Bookstore, 2011. ISBN: 9781133629603
Andy Field; Discovering Statistics using IBM SPSS Statistics, SAGE, 2013. ISBN: 978-1446249178

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

SPSS
Folhas de Cálculo

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 70,00
Trabalho prático ou de projeto 30,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Elaboração de projeto 50,00
Estudo autónomo 56,00
Frequência das aulas 56,00
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 AD + 0.70 EF

AD - Distributed Assessment:

          AD = 1/2 x TG1 + 1/2 x TG2

TG1 and TG2 - Teamwork Assignments
EF - Final Exam (openbook)

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

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