quantitative methods

190 COURSE DESCRIPTIONS: QUANTITAVE METHODS

14.9 QUANTITATIVE METHODS

30A00210 Mathematics and Statistics for Managers (6 cr)

Responsible teacher:

N.N.

Status of the Course:

Compulsory course

Level of the Course:

Fundamentals of Business Knowledge

Teaching period:

I-II

Learning Outcomes:

Learn to understand and use basic mathematical and statistical tools in solving and model-ing problems in economics and business. Provide skills for reading literature in economics and management science.

Content:

Mathematics: basic concepts of functions, vectors, matrices, systems of linear equations, linear pro-gramming. Statistics: population and sample, graphical data analysis, descriptive statistics, in-troduction to regression analysis, random num-bers.

Assessment Methods and Criteria:

1. Lectures in mathematics 21 h and lectures in statistics 21 h.

2. Exercises 12 h in mathematics and 12 h in statistics.

3. The course can be completed by doing the exercises (20% of the final grade) and passing two midterm exams (80% of the final grade), or by passing the final exam.

Literature:

Knut Sydsæter and Peter Hammond (2008) Es-sential mathematics for economic analysis 3rd edition. ; Pace, L. (2010) Statistical Analysis Using Excel 2007.

Study Material:

Complementary/alternative readings: Simon, C.P. & Blume, L.: Mathematics for Economists

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/30A00210/

Prerequisites

High school mathematics

Evaluation:

0-5

Registration for Courses:

Via WebOodi

Language of Instruction:

English

Further Information:

Only one of the courses 30A00110 Matematiikkaa ja tilastotiedettä liikkeenjohtajille and 30A00210 Mathematics and statistics for managers can be included in the Bachelor‟s degree.

30A00410 Quantitative Business Analysis (6 cr)


Dr. Pekka Malo and N.N.


Compulsory course


Fundamentals of Business Knowledge

Learning Outcomes:

To improve mathematical and statistical skills for problem solving, and to create theoretical founda-tions for further studies and understanding eco-nomic reference texts. This course is a continuum for Mathematics and Statistics for Managers (30A00210).

Content:

Mathematics: derivative and partial derivative, integral calculus, foundations of unconstrained optimization and constrained optimization. Statis-tics: inference based on probability, conditional probability and Bayes formula. Random variable and its distribution, expectation, variance and standard deviation, decision trees, descriptive statistics, confidence intervals and hypothesis testing.


1. Lectures in mathematics 21 h, Dr. Pekka Malo. and lectures in statistics 21 h, N.N.

2. Exercises in mathematics 12 h, N.N.. and exercises in statistics 12 h, N.N.

3. The course can be completed by doing the exercises (20% of the final grade) and passing two midterm exams (80% of the final grade) or by passing the final exam.

Literature:

COURSE DESCRIPTIONS: QUANTITAVE METHODS 191

Knut Sydsæter and Peter Hammond (2008) Es-sential mathematics for economic analysis 3rd edition. ; Pace, L. (2010) Statistical Analysis Using Excel 2007.

Study Material:

Complementary/alternative readings: Simon, C.P. & Blume, L.: Mathematics for Economists, W.W. Norton & Co, 1994.

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/30A00410/

Prerequisites:

30A00210 Mathematics and statistics for manag-ers

Evaluation:

0-5


Via WebOodi


English


Only one of the courses 30A00310 Kvantitatii-vinen analyysi taloustieteissä and 30A00410 Quantitative business analysis can be included in the Bachelor‟s degree.

30C00100 Statistical Analysis (6 cr)


Jan-Otto Malmberg


BT BSc program, common program studies B.Sc., finance (Taloushallinto)


Intermediate

Teaching period:

III - IV

Workload:

- Lectures 40 h - Exercises 20 h - Independent work 102 h

Total 162 h

Learning Outcomes:

The course will provide students with additional knowledge in statistical theory and techniques.

Content:

Probability, conditional probability, decision trees, probability distributions, estimation, hy-pothesis testing. Introduction to regression analy-sis, analysis of variance.


1. Lectures 40 h, Jan-Otto Malmberg. 2. Exercises 20 h, N.N. 3. The course can be completed by doing the

exercises (20% of the final grade) and passing two midterm exams (80% of the final grade) or by passing the final exam.

Literature:

Levine, David M. (2006) Business statistics a first course. ISBN 0131971018

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/30C00100/

Prerequisites:

Compulsory courses in quantitative methods

Evaluation:

0-5


Via WebOodi


English

30C00200 Econometrics (6 cr)


Professor Timo Kuosmanen


B.Sc., business technology, specialization studies B.Sc., economics program B.Sc., finance (Taloushallinto)


Intermediate

Teaching period:

III

Workload:

- Lectures 40 h - Exercises 20 h - Independent work 102 h - Total 162 h

Learning Outcomes:


The main objective of the course is to obtain a basic understanding of the econometric method. The course focuses on least squares estimation and related statistical inferences. The assumptions of least squares estimation will be critically inves-tigated, and possible ways to alleviate the assump-tions will be explored.

Content:

Linear regression model and its assumptions, least squares estimation, tests of parameters and linear restrictions, endogeneity and instrumental variables, heteroskedasticity and autocorrelation. Introduction to maximum likelihood estimation of discrete choice models and limited dependent variables. Introduction to time series and panel data models. All topics are examined by means of economic examples with real empirical data.


1. Lectures 40 h, Timo Kuosmanen 2. Exercises and demonstrations 20 h, Antti

Saastamoinen 3. Grading is based on exercises (30%) and the

final exam (70%).

Literature:

Dougherty, Christopher (2007) Introduction to econometrics. ISBN 9780199280964

Course Homepage:


Prerequisites:


Evaluation:

0-5


Via WebOodi


English

30C00300 Mathematical Methods for Economists (6 cr)


N.N


BSc, BT program, specialization studies BSc, economics program


Intermediate

Teaching period:

III - IV

Workload:

Contact hours 62 h and autonomous work 100 h

Learning Outcomes:

To improve mathematical skills acquired in the basic courses

Content:

The course concentrates on mathematical meth-ods useful in economics and business. The topics covered are essentials of set theory, matrix alge-bra, multivariate calculus, optimization, introduc-tion to dynamics: difference and differential equa-tions. The applications of the course will cover different areas in economics and management, finance, business technology and logistics. Com-puters are used to solve some of the problems.


1. 40 h lectures + 22 h exercises + 20 h optional instruction sessions.

2. The course can be completed by doing exer-cises (20% of the final grade) and by pssing two midterm exams (80% of the final grade) or by passing the final exam.

Literature:

Simon, Carl P. and Blume, Lawrence (1994) Mathematics for Economists. ISBN 0393957330 ; Sydsaeter K., Hammond P., Seierstad A., Strom A. (2008) Further mathematics for economic analy-sis, 2nd edition. ; Sydsäter, K. & Hammond, P (2008) Essential Mathematics for Economic Analysis, 3rd edition.

Study Material:

Lecture notes

Course Homepage:


Prerequisites:


Evaluation:

0-5


Via WebOodi


English

30C00400 Tools for Business Decisions 2 (6 cr)



N.N


Bachelor‟s program in Business Technology, common program studies


Intermediate studies

Teaching period:

IV

Workload:

- Lectures 42 h - Independent work 120 h

Learning Outcomes:

To familiarize the students with useful analytical tools applied to practical business decisions.

Content:

Decision making under uncertainty, utility theory, multiple criteria decision making, waiting line models, simulation, with cases. Modeling with spreadsheets and add-inns.


1. Lectures and demonstrations 42h 2. Exercises/cases and final exam

Literature:

Anderson, D.R., Sweeney, D.J., and Williams, T.A. (2010) An Introduction to Management Science. ISBN 978-1439043271

Study Material:

PP-slides, handouts, cases/exercises, textbook

Course Homepage:


Evaluation:

0-5


Via WebOodi


English

30E00150 Applied Optimization and Methodologies (6 cr)


Kalyanmoy Deb


Elective course. This course is intended both for master's and doctoral students.


Advanced

Teaching period:

Summer 2012

Workload:

- lectures 42 h - individual work 120 h

Learning Outcomes:

The objective of the course is to acquaint students with optimization methods for single and multiple objective problems. Both classical and evolution-ary algorithms for optimization are discussed.

Content:

Introduction to optimality conditions, classical optimization methods, constraint handling basics, introduction to evolutionary optimization (EO), cases studies from real-world problems, custom-ized EO, uncertainty based EO, robust and relia-bility based EO, multi-objective EO, innovation through optimization, and decision-making.


1. Lectures and participation (10 %) 2. Exercises (20 %) 3. Project (20 %) 4. Exam (50 %)

Literature:

1. Bazaraa, M. S., Sherali, H. D. and Shetty, C. M. (2004). Nonlinear programming: Theorey and algorithms. Wliey: London. 2. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley: London.

Study Material:

Lecture slides, handouts, exercises, textbook.

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/30E00150/

Evaluation:

0-5


Via WebOodi


English


30E00400 Simulation (6 cr)


prof. Tomi Seppälä


M.Sc.(Econ), Information and Service Manage-ment program, elective course of advanced spe-cialization studies.


Advanced

Teaching period:

I

Workload:

- Lectures 40 h - Exercises 20 h - Project 50 h - Independent work 52 h

Learning Outcomes:

To acquaint the student with modeling using sim-ulation techniques which can be usedto support management decision making, especially in fi-nance, operations management, and logistics; to develop expertise in using simulation models with computers and related software,especially Excel; to give you experience in analyzing results and making decisions through assigned homework exercises and case analyses.

Content:

Introduction to simulation models, simulation in Excel, random numbers, methods to simulate random events, managerial applications of risk analysis, Wiener process, valuation of stocks and options, system simulation, forecasting, advanced simulation techniques.


1. Lectures 40 h 2. Exercises 20 h 3. The grade consists of final exam (50%), exer-

cises (20%) and project work (30%).

Literature:

Evans, J.R. & Olson, D.L. (2002) Introduction to simulation and risk analysis. ; Vose, D. (2000) Risk analysis: a quantitative guide. ; Ross, Shel-don M. (2006) Simulation. ISBN 9780125980630

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/30E00400/ https://noppa.aalto.fi/noppa/kurssi/30C00400/

Prerequisites:

Undergraduate mathematics, statistics and prob-ability, as well as Excel skills. More specifically, basic knowledge in matrix algebra, differential and integral calculus, statistical analysis and probability distributions are essential.

Evaluation:

0-5


Via WebOodi


English


Lectures and exercises in period I. Project work and project presentations in period II.

30E00500 Quantitative Empirical Research (6 cr)


Prof. Pekka Korhonen and Dr. Pekka Malo


Common Scientific Doctoral Studies Master‟s degree, Minor studies in Quantitative Methods


Advanced

Teaching period:

IV

Workload:

- Lectures 40 h - Exercises 20 h - Independent work 102 h

Learning Outcomes:

The objective of the course is to enable the stu-dents to use quantitative data analysis techniques in business and economic research. The course will provide the students with a set of tools useful in empirical research.

Content:

Basic concepts, screening data, and visualizing multivariate observations are discussed. Further-more, the course will define and introduce a set of statistical multivariate methods and explain when their use is appropriate and how they are related to each other. Some of the methods covered are linear regression, logistic regression analysis,


principal component analysis, factor analysis, analysis of variance, and cluster analysis. Method-ological aspects and interpretation of analysis are also explained. Excel and SAS programs will be used in exercises and demonstrations during the course.


1. Preliminary assignments 2. Lectures 42 h 3. Exercises 18 h 4. Grading is based on a final exam (80% of the

grade) and exercises (20% of the grade)

Literature:

Sharma, Subhash (1996) Applied multivariate techniques. ISBN 0-471-31064-6 ; Hair J.H., Tat-ham R.L., Anderson R.E.A., Black W. (1998) Mul-tivariate data analysis. ISBN 0138948585

Study Material:

Other material announced by the lecturers.

Course Homepage:


Prerequisites:

Basic knowledge in mathematics and statistics is assumed. Moreover, basic knowledge in linear algebra and statistical analysis are recommended.

Evaluation:

0-5


Via Weboodi


English

30E00700 Advanced Statistical Methods (6 cr)


Prof. Tomi Seppälä


M.Sc.(Econ), Information and Service Manage-ment program, elective course of advanced spe-cialization studies. M.Sc.(Econ), finance program, advanced speciali-zation studies.


Advanced

Teaching period:

III

Workload:

- Lectures 40 h - Exercises 20 h - Project 50 h - Independent work 52 h

Learning Outcomes:

To expand and deepen the student‟s knowledge of and ability to use regression and time series anal-ysis with applications to business, finance and economics.

Content:

Topics in linear models and Time Series analysis: special estimation methods of regression models, ARMA models, forecasting, cointegration, ARCH and GARCH models. The content may change from year to year.


1. Lectures 40 h, professor Tomi Seppälä 2. Exercises 20 h, Tomi Seppälä 3. The grade consists of a final exam (50%), ex-

ercises (20%) and a project (30%).

Literature:

Brooks, Chris (2008) Introductory econometrics for finance, 2nd edition. ISBN 0-521-79018-2 ; Verbeek, Marno (2004) Guide to modern econo-metrics. ISBN 978-0-470-85773-1 ; Enders, Wal-ter (2003) Applied Econometric Time Series, 2nd Edition. ISBN 978-0471230656

Course Homepage:


Prerequisites:

Compulsory statistics and mathematics courses in the Bachelor‟s program, as well as at least one of the following: introduction to econometrics, sta-tistical analysis or simulation. It is important that student knows the basics of statistical testing and regression analysis prior to attending the course. Other prior courses in quantitative areas are use-ful, too.

Evaluation:

0-5


Via Weboodi


English



Lectures and exercises in period III. Project work and project presentation in period IV

57C99901 Bachelor’s Thesis (10 cr)


Assistant Professor, Ph.D. (econ.) Johanna Bragge (with Merja Halme and Markku Kuula)


B.Sc. (Econ.), Business Technology program.


Compulsory program course.

Teaching period:

I-II (Autumn) and III-IV (Spring)

Workload:

- Independent scientific work: 267 h - searching and reading literature - formulating a research question - (doing empirical analyses, gathering research

data) - writing the thesis

Learning Outcomes:

The objective is to learn and practice independent scientific thinking by setting up research ques-tions and studying a specific topic. Students learn to get to know references, to find literature, and to apply it for resolving a research question. The thesis will be a structured, scientific report.

Content:

The Bachelor‟s Thesis is carried out together with Bachelor‟s Thesis Seminar (57C99902)


Grading: 85% of the grade is based on the thesis and 15% is based on participation in the thesis seminar.

Course Homepage:


Evaluation:

0-5


Register to 57C99902 Bachelor‟s Thesis Seminar via Weboodi.


English

57C99902 Bachelor’s Thesis Seminar (2 cr)


Assistant Professor, Ph.D. (econ.) Johanna Bragge (with Merja Halme and Markku Kuula)




Compulsory program course

Teaching period:


Workload:

- introductory lectures 8 h - seminar sessions 14 h - preparing for lectures 6 h - preparing for seminar sessions 12 h - doing assignments (research plan, research

profiling assignment) 13 h

Learning Outcomes:

The objective is to learn and practice independent scientific work.

Content:

In the seminar, students get guidance in their research for Bachelor‟s Thesis. During the Thesis seminar, students will prepare a research plan, conduct a research profiling assignment, report the progress of their Thesis, present and defend their research results, act twice as an opponent, and participate actively in seminar discussions.


3. Introductory lectures and seminar sessions 28 h

4. Requirements for passing the seminar: com-pleting the research profiling assignment, pre-senting own thesis at the seminar, acting twice as an opponent, attending to 70% of the semi-nar sessions

(Grading: Pass/Fail)

Course Homepage:


Evaluation:

Pass/Fail


via WebOodi.



English

57C99903 Maturity Test (0 op)


Thesis supervisor




Compulsory program course.

Teaching period:


Learning Outcomes:

After finalizing the Bachelor‟s Thesis, students carry out a proficiency test in mother tongue. The objective is to show that a student can write an essay in her or his mother tongue and that s/he masters the basic concepts in her or his thesis. In general, the test is written in Finnish, but non-Finnish speakers are granted an exemption to write the test in English.

Content:

The Maturity Test is taken at the end of the Bache-lor‟s Thesis Seminar (57C99902) and Bachelor‟s Thesis (57C99901). The test can be taken in con-nection with any official exam, and the date is agreed upon with the supervisor once the thesis is written.

Assessment, Methods and Criteria:

Exam 4 h

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/57C99902

Evaluation:

Pass/Fail


Register first to 57C99902 Bachelor‟s Thesis Sem-inar via WebOodi. Agree the date for the Profi-ciency Test with the supervising teacher (after the thesis is written).

57E00500 Business Intelligence (6 cr)


Oana Velcu


M.Sc.(Econ), Information and Service Manage-ment program, common advanced course


Specialization course

Teaching period:

12th March 2012 - 11th May (IV Spring)

Workload:

- Lectures 28 hours - Preparing for lectures 56 hours - Case studies 4.5 hours - Preparing for case studies 14 hours - Preparing for exercises 10 hours - Computer labs 4.5 hours - Preparing for computer labs 9 hours - Preparing for exam 30 hours - Exam 4 hours

Learning Outcomes:

After the course, the students will understand the connection between the analytical tools that can be used on real data in companies and the strate-gic decision-making. The students will have ana-lytical skills that enable them to work with the data, to understand it and turn it into intelligence for particular decisional contexts.

Content:

The course seeks to provide a balanced approach to BI by enabling insight into the building blocks of BI, such as data management, querying, analyt-ic tools, quantitative modeling, and reporting, with the purpose to enable students acquire theo-retical and practical understanding of decision-making and problem solving.


4. Exam 60% 5. Case study essays 20% 6. Exercises 20%

Literature:

Turban, Efraim, Sharda R., and Delen, D. (2011) Decision support and business intelligence sys-tems 9th ed.. ISBN 0132453231

Study Material:

Other research papers and material provided by lecturers.

Course Homepage:


Evaluation:

0-5



Via WebOodi


English

57E99901 Master’s Thesis (30 cr)


M.Sc.(Econ), Information and Service Manage-ment program, compulsory course.


Advanced

Learning Outcomes:

The objective is to practice independent scientific thinking by setting up research questions and studying a specific research topic. The thesis will be a structured, scientific report.

Course Homepage:


Evaluation:

0-5


The Master‟s Thesis needs to be carried out to-gether with the Master‟s Thesis Seminar (57E99902).

57E99902 Master’s Thesis Seminar (0 cr)


Matti Rossi (coordinates)




Advanced

Teaching period:

I - IV

Workload:

- 6 h presentations - 4 h critique - 20 h seminars

Learning Outcomes:

The objective is to practice independent scientific thinking by setting up research questions and studying a specific research topic. The thesis will be a structured, scientific report.

Content:

In the seminar, students get guidance in their research for Master‟s Thesis. There are lecturers on methods and theories of study in IS, MS and OR.

Assessment Me Course Homepage:


thods and Criteria:

Lectures, presentations and critique of other stu-dents work. If necessary, students will be orga-nized into smaller groups supervised by professors from the department of Information and Service Economy.

Course Homepage:

https://noppa.aalto.fi/noppa/kurssi/57E99902

Evaluation:

hyväksytty/hylätty


Via WebOodi


English

57E99903 Maturity Test (0 op)




Advanced

Learning Outcomes:

After finalizing the Master‟s Thesis, students carry out a proficiency test in Mother Tongue. The ob-jective is to show a student can write an essay in her or his mother tongue and that she or he knows the basic concepts in her or his Thesis.

Evaluation:

hyväksytty/hylätty

quantitative methods

Economy & Finance