quantitative methods (2)

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.
two midterm exams (80% of the final grade)
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.
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
- 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
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
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
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:
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
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
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
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
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)
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

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