Applied Statistics

Code:

2MADSAD04

Acronym:

EA

Keywords
Classification	Keyword
OFICIAL	Statistics

Instance: 2015/2016 - 1S

Active?	Yes
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible:	Master in Modeling, Data Analysis and Decision Support Systems

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MADSAD	39	Bologna Official Syllabus	1	-	7,5	56	202,5
ME	3	Bologna Syllabus	1	-	7,5	56	202,5

Teaching language

Suitable for English-speaking students

Objectives

The purpose of Applied Statistics is to provide the student statistical analysis techniques applied to some areas. It is intended to provide the tools necessary for inferential approach through non-parametric hypothesis testing as a complement to parametric hypothesis testing, known by students. It also provides some   simulation and sampling techniques, as well as some methods and techniques related to statistical quality control.

Learning outcomes and competences

The skills that the student intends to acquire are:
- Correct use of the most appropriate hypothesis test (parametric or non-parametric) and to distinguish clearly its application and conclusions that can be drawn;
- Use the main simulation methods in statistics.
- Choice of the sampling process more accurate, or possible to
- Use statistical control of quality and know how to take the most appropriate conclusions;

Working method

Presencial

Program

Module 1 Non-parametric tests
1. Fitting tests
1.1 Chi-Square
1.2 Kolmogorov-Smirnov
1.3 Adequacy of the statistical model
1.3.1. Graphical method (QQ plot)
2. Localization testing
2.1 Test of the signs
2.2 Wilcoxon
2.3 Mann - Whitney - Wilcoxon
3. Analysis of variance
3.1 With a factor
3.1.1 ANOVA Model
3.1.2 Kruskal - Wallis
3.2 With two factors
3.2.1 ANOVA Model
3.2.2 Friedman Test

Simulation Module 2
1. statistical models
2. Generation of random variables
2.1. Introduction: Motivation
2.2. univariate distributions
2.2.1. Discrete random variables
2.2.2. Continuous random variables
2.3. multivariate distributions
3. Integration by Monte Carlo method


Module 3 Sampling
1. introduction
2. Sampling methods empirical or non probabilistic
2.1. Quota method
2.2. Purposive sampling method
2.2.1. Method of individuals-type
2.2.2. Snowball sampling method
2.3. Convenience sampling method
2.4. Random-route (random itineraries)
3. Probabilistic sampling methods
3.1. Simple random sampling
3.2. Stratified random sampling
3.3. Sampling in bunches

Module 4 Statistical Quality Control
1. introduction
1.1. Quality settings. fundamental objective
1.2. Usual statistical techniques in quality
2. Statistical Process Control Production
2.1. Brief introduction to control charts
2.2. Policies sampling FSI and VSI
2.3. Parameters associated with the "performance" of letters FSI and VSI
2.4. Shewhart control charts
2.4.1 Control Charts usual for quantitative variables
2.4.2 Control Charts usual for qualitative variables
2.5 CUSUM and EWMA control charts.
3. Control Acceptance and Reception
3.1. Sampling plans single and double. Operating characteristic curve
3.2. Rectification of lots. Average quality of output. Average number of articles inspected
3.3. Producer's risk and consumer

Mandatory literature

Bento Murteira, Carlos Silva Ribeiro, João Andrade e Silva, Carlso Pimenta; Introdução à Estatística, Escolar editora, 2010
Guimarães, R. C. e Cabral, J. A.; Estatística, McGraw-Hill, 1997
Fishman, G. S.; Monte Carlo. Concepts, Algorithms and Applications, Springer, 1996
Ross, S. M.; Simulation, Academic press, (DM 62-443), 1997
Ivette Gomes, Fernanda Figueiredo e Maria Isabel Barão; Controlo Estatístico da Qualidade, Edições SPE, 2010
Vic Barnett; Sample Survey – Principles and Methods, 3rd edition, Hodder and Arnold, 2003

Complementary Bibliography

Conover,W. J.; Practical nonparametric statistics, John Wiley, 1999
Kleijnen, J. e Van Groenendaal, W.; Simulation: A statistical perspective, J. Wiley & Sons, 1992
Ripley, B. D.; Stochastic Simulation John Wiley & Sons, 1987
Bratley, P., Fox, B.L. e Schrage, L.E. ; A Guide to Simulation, Springer-Verlag, 1987
Hjorth, J.S.U. ; Computer Intensive Statistical Methods. Validation, Model Selection and Bootstrap, Chapman & Hall, 1994
Montgomery, D.C.; Introduction to Statistical Quality Control, John Wiley and Sons, 1997
Reis, Elizabeth, Vicente, Paula e Ferrão, Fátima. Sondagens. ; A amostragem como factor decisivo de qualidade. Edições Sílabo, 1998

Teaching methods and learning activities

Theoretical and practical classes.
Support of R software.

Software

R (http://www.r-project.org)

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	100,00
Total:	100,00

Eligibility for exams

The final classification of the distributed evaluation corresponds to the simple average of the marks obtained in the two tests (minimum mark in each test: 8.0)

Calculation formula of final grade

Average grade of the 2 tests.

Test 1 with all the contents of Modules 1 and 2.

Test 2 will take place in the day of the exam (normal season). The students will be evaluated on all the contents of Modules 3 and 4. 


Each test has a maximum score of 20.

Students can do the exam with all the contents of the 4 Modules (The classification of the Test 1 is forgotten)

Examinations or Special Assignments

 

Classification improvement