Statistics II

Code:

EIG0018

Acronym:

E II

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2015/2016 - 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:

1. perform analysis of variance;
2. design simple experiments;
3. perform regression analysis;
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.

EIG0015: All topics.

Program

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


• ANALYSIS OF VARIANCE (ANOVA): ANOVA Model with 1 Factor (Fixed and Random Effects, Constrasts). ANOVA Model with 2 Factors (Fixed and Random Effects, Interation between Factors, Constrasts). Extension to Aditional Factors. ANOVA Assumptions. Non-Parametric ANOVA (Kruskal-Wallis, Friedman).


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


• REGRESSION: Simple Linear Regression (Parameters Estimation, Inference about Parameters, Forecasts based on the Simple Linear Regression Model, Collinearity). 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. Non-Linear Regression. Linear Regression with variable Tansformations.


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


• EXPLORATORY FACTORIAL ANALYSIS: Factors and Principal Components (Graphical and Mathematical Representations). Factorial Analysis. Princiapal Components Analysis. Análise Fatorial vs Análise de Componentes Principais. Factors e Principal Components Extraction (Eigenvalues and Scree Plot). Rotating Factors and Principal Components. Interpretation.

 

Mandatory literature

Andy Field; Discovering Statistics using IBM SPSS Statistics, SAGE, 2013. ISBN: 978-1446249178
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

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

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	20,00
Trabalho laboratorial	10,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	66,00
Frequência das aulas	56,00
Trabalho laboratorial	40,00
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 = 1/3 x MT1 + 1/3 x MT2 + 1/3 x TG

MT1 e MT2 - Quizzes

TG - Teamwork Assignment

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.

Classification improvement

Students may improve the Final Exam (EF) and Quizzes (MT1 and MT2) marks.

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