Multivariate Statistics

Code:

PRDEIG014

Acronym:

ESTM

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2011/2012 - 1S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Doctoral Program 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
PRDEIG	4	Syllabus since 2007/08	1	-	6	42	162

Teaching language

English

Objectives

To give first-year PhD students a broad, but simultaneously in-depth, overview of probability and statistics relevant to analysis and research in management and public policy.
More specifically, it is expected to endow the students with skills to:
- summarize data;
- build probabilistic models;
- build statistical models;
- understand and apply a variety of statistical methods and research designs.

Program

- Descriptive statistics
- Probability. Joint, marginal and conditional probability. Bayes rule.
- Random variables. Functions of random variables. Joint, marginal and conditional distributions.
- Some specific probability distributions.
- Samples and random sampling. Statistics as estimators. Point estimation. Interval estimation.
- Hypothesis testing.
- Simple linear regression. Multiple linear regression.
- Least squares regression. Finite-sample properties of the least squares estimator.
- Hypothesis tests and model selection.
- Binary variables.
- Diagnosing and fixing problems.

Mandatory literature

Dimitri P. Bertsekas, John N. Tsitsiklis; Introduction to Probability, 2nd Edition, Athena Scientific, 2008. ISBN: 978-1886529236
William H. Greene; Econometric Analysis, 7th Edition, Pearson Education, 2011. ISBN: 978-0273753568
David Hildebrand, R. Lyman Ott, J. Brian Gray; Basic Statistical Ideas for Managers, 2nd Edition, Thomson South-Western, 2004. ISBN: 978-0534378059

Teaching methods and learning activities

The course will be organized around lectures, complemented by autonomous work, which will be organized around group assignments that take place in off class periods. A strong interaction and participation of students, leading to a real active learning environment, will be sought in the lectures, by resorting to differentiated learning strategies, namely involving the analysis of small cases.

Software

SPSS
Microsoft Excel

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	39,00
	Total:	-	0,00

Calculation formula of final grade

Grades will be based on:
- four assignments – 60% of final grade;
- final exam – 35% of final grade;
- class participation – 5% of final grade.