Applied Quantitative Methods for Management

Code:

2EAE04

Acronym:

MQAG

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2013/2014 - 1S (of 09-09-2013 to 20-12-2013)

Active?	Yes
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible:	Master in Economics and Business Administration

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
EAE	75	Bologna Syllabus since 2012	1	-	7,5	56	202,5

Teaching language

Portuguese

Objectives

The permanent change and growing complexity of the contemporary business environment requires managers effective action. In this context, the course of Applied Quantitative Methods for Management aims to provide students with knowledge of Mathematics, Statistics and Operational Research supporting the decision-making process used in business.

The list of problems studied includes both decision problems under conditions of certainty and uncertainty. To enable analysis of realistic problems, which are typically of high dimension,  theoretical presentation will be complemented with the application of computers.

Learning outcomes and competences

After attending the curricular unit, students should be acquainted with quantitative methods and techniques to be used to provide decision support to decision makers. They should also be able to apply them to specific managerial problems.

Working method

Presencial

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

It is assumed that the student has knowledge of the subjects 0.1. Probabilities, 0.2. Random variables and their characterization, 0.3. Samples and empirical distribution function,  0.4. Important distributions, 0.5. Parametric hypothesis test , 0.6. Confidence intervals for the mean of a normal population. Slides will be available to students with a review of these issues.

Program

1. Module 1: Nonparametric tests and analysis of variance
    1.1. Tests of goodness of fit: Chi-square test and Kolmogorov-Smirnov test
    1.2. ITest of ndependence
    1.3. Signal test
    1.4. Wicoxon tes
    1.5. Location tests for two populations with paired samples
    1.6. Mann-Whitney-Wilcoxon test
    1.7. One-way analysis of variance. Methods for multiple comparisons.
    1.8. Kruskal-Wallis test
    1.9. Two-way analysis of variance. Methods for multiple comparisons.

2. Module 2: Decisions and Multi-Objective Decision Making With Uncertainty and Risk
    2.1. Multi-criteria decision making
       2.1.1. Tree Values
       2.1.2. Obtaining Decision
       2.1.3. Sensitivity Analysis
       2.1.4. Streamlining the Review Process
       2.1.5. Representation of Information
    2.2. Multi-Objective optimization
       2.2.1. Multi-Objective Linear Programming
       2.2.2. Programming for Achieving Goals
    2.3. Decision Making With Uncertainty and with Risk
        2.3.1. Choose Deterministic criteria
        2.3.2. Criteria for Selection Using Probability
        2.3.3. Determination of Probabilities
        2.3.4. Utility Function
                Determination of Utility Function
                Guaranteed Equivalent and Risk Premium
        2.3.5. Determination of Weighting Factors
        2.3.6. Value of Information
                 Perfect Information
                 Obtaining Additional Information
                 Revised Probabilities

Mandatory literature

Bento J. F. Murteira, Carlos S. Ribeiro, João A. e Silva, Carlos Pimenta; Introdução à Estatística, Escolar Editora, 2010
Webster, Allen; Estatística aplicada à administração e economia, McGraw Hill,, 2007
R. T. Clemen and T. Reilly; Making Hard Decisions, Duxbury, 2001
R. L. Keeney and H. Raiffa; Decisions with Multiple Objectives, Cambridge University Press, 2000

Complementary Bibliography

Moser, P. K.; Rationality in Action: Contemporary Approaches, Cambridge University Press, 1990
Bodily, S. E.; Modern Decision Making: A Guide to Modelling with Decision Support Systems, McGraw-Hill , 1985
Rivett, P.; Model Building for Decision Analysis, John Wiley & Sons, 1980
Holloway, C. A.; Decision Making Under Uncertainty: Models and Choices, Prentice-Hall, 1979

Teaching methods and learning activities

Theoretical presentation, complemented by illustrative examples covering a wide range of decision problems in various scenarios (deterministic, uncertainty and risk). Solving some illustrative exercises.
 

Software

Software R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

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

Eligibility for exams

DISTRIBUTED EVALUATION WITHOUT FINAL EXAM

1. There are two moments of individual assessment consisting in the realization of two tests: one corresponding to the module 1, being held at the 7th lesson, and another corresponding to the module 2, being held in 14th class.

2. The final grade is obtained by the weighted average of the two tests (50% each test).

3. To obtain approval, the final score should be at least 9.5, and a minimum score of 6 values(out of 20) in each test must been obtained.

4. Failure is considered, with a final classification of 8 values, for students who obtained a grade below 6 values in any of the tests, regardless of the value of the weighted average rating of 2 tests.

Calculation formula of final grade

CALCULATION IN DISTRIBUTED EVALUATION:
Mark of the module 1 test (Mod1)
Mark of the module 2 test  (Mod2)

Final Grade = (Mod1 + Mod2) / 2

The student must obtain a minimum score of 6 values (before rounding) in all tests. Additionally, the approval requires that the final grade is at least 9.5.