Facultatea de Științe Economice și Gestiunea Afacerilor
Str. Teodor Mihali nr. 58-60
Cluj-Napoca, RO-400591
Tel.: 0264-41.86.52-5
Fax: 0264-41.25.70
econ@econ.ubbcluj.ro
www.econ.ubbcluj.ro
DETAILED SYLLABUS
Inferential Statistics
1. Information about the program
1.1 Higher education institution
Babeş-Bolyai University of Cluj-Napoca
1.2 Faculty
Faculty of Economics and Business Administration
1.3 Department
Department of Statistics, Forecasts, Mathematics
1.4 Field of study
Finance
1.5 Study cycle
Undergraduate studies
1.6 Specialization/Program of study Finance and Banking (FB) / Economist
2. Information about the discipline
2.1 Discipline title
Inferential Statistics
2.2 The holder of the course
Associate Professor Cristian
activities
Marius LITAN
2.3 The holder of the seminar
Associate Professor Cristian
activities
Marius LITAN
2.4 Year of study II 2.5 Semester
II
2.6 Type of
assessment
ES (i.e.,
summative
examination)
2.7
Op (i.e.,
Disciplin
optional)
e regime
3. Total time estimated (hours per semester of teaching)
3.1 Number of hours per week
3.4 Total hours of curriculum
From which: 3.2
course
From which: 3.5
42
course
3
2
3.3 seminar/laboratory
1
28
3.6 seminar/laboratory
14
Time distribution
Study after textbook, course support, bibliography and notes
Additional documentation in library, on specialized electronic platforms and on the field.
Preparing seminars/laboratories, essays, portfolios and reports.
Tutoring
Examinations
Others activities...................................
3.7 Total hours for individual
58
study
3.8 Total hours per semester
100
3.9 Number of credits
4
4. Preconditions (if necessary)
4.1 Of curriculum
4.2 Of skills
It is not the case.
It is not the case.
Hou
rs
15
15
20
4
4
5.
Conditions (if necessary)
5.1. For conducting the
It is not the case.
course
5.2. For conducting
Some of the seminars / laboratories should be held in a room with computers on
seminar/laboratory
which Excel and SPSS are installed.
6.
Specific skills acquired
Profes Acquiring the tools of descriptive statistics plays an obvious role in the development of the
sional following professional skills by the students (skills associated to the FB English line of study):
skills
• The adequate use of the concepts, theories, methods and tools specific to finance, for the
well functioning of the private or public organizations;
• Collecting, analyzing and interpreting the data and the information connected to economic
and financial problems;
• The ability to write economic and financial papers (academic, or professional reports) for
the private or public organizations.
Trans
versal
skills
7.
The courses and the seminars of descriptive statistics play a role in the development of all the
transversal skills (associated to the FB English line of study):
-
Applying the principles, the norms and the ethical values of the profession such that the
graduates are able to construct a rigorous, efficient and responsible strategy of work;
-
The ability to identify the roles and responsibilities within a team of complex tasks,
being able to insure with the rest of the teammates an efficient team work;
-
The ability to identify the opportunities for continuous professional development and
the efficient use of all the identified resources and techniques.
-
The knowledge of and the ability to apply the general principles and the theory of the
market economy.
Course objectives (arising from grid of specific skills acquired)
7.1 General objective of the
discipline
7.2 Specific objectives
8.
The general purpose of the course is to prepare the students to apply
methods and procedures of inferential statistics to any economic field,
within both the academic world and the business real world.
 Students should acquire the fundamental notions and techniques of
inferential statistics;
 Students should be able to apply the techniques of inferential
statistics in financial and economic problems;
 Students should be able to use SPSS in understanding and applying
the tools of inferential statistics in economic and financial
problems.
Contents
8.1 Course
Teaching methods
Observati
ons
Basic statistical notions
The professor gives a
- Population versus sample, parameters of a population versus
talk and encourages
sampling statistics, probability density function, distribution
discussions on the
function, histogram, empirical probability density function,
theme.
2 courses
empirical distribution function.
- Types of parameters, sample mean, sample standard deviation,
the law of large numbers and examples.
Probability distributions
- Random variables – discrete and continuous, probability function,
probability density function, (cummulative) distribution function,
the (continuous) uniform distribution, the (discrete) binomial
distribution, the normal distribution and its key properties; The professor gives a
unidimensional and multidimensional distributions; the role of talk and encourages
2 courses
the correlation in the multivariate normal distribution.
discussions on the
- The probability that a random variable takes values in a given theme.
interval, standardization of random variables, interpretation and
calculation of probabilities for the normal distribution.
- Some financial application of the distribution function (e.g. Roy’s
first safety criterion, Roy’s criterion for normal distributions, etc.)
Sampling and estimation
- Random sampling, empirical distribution function, sampling
errors and interpretation, random sampling versus stratified
sampling, time series versus cross section data, central limit
theorem and its importance;
The professor gives a
- Calculation and interpretation of the standard error of the mean talk and encourages
3 courses
estimator, point estimation and confidence intervals, desirable
discussions on the
properties of an estimator;
theme.
- The construction of confidence intervals, the calculation and
interpretation of the confidence interval in the case of the mean
estimation, calculation of the sample size, data-mining bias,
selectivity bias, survival bias, etc.
Introduction to hypothesis testing
- Steps of hypothesis testing, the choice and interpretation of the
null hypothesis and of the alternative hypothesis, one-sided and
two-sided tests, defining and interpreting statistics associated to The professor gives a
hypothesis testing, type I and II errors, confidence level and
talk and encourages
2 courses
significance level;
discussions on the
- Decision rule of a test, power of a test, relationship between
theme.
hypothesis testing and confidence intervals for standard
parameters, p-value and its relationship with the decision rule of a
test.
Hypothesis testing
- Hypothesis tests for the mean: large samples, small samples, the
case of the normal population, the case of a population that is not
normal, known versus unknown variance.
The professor gives a
- Hypothesis tests for the equality of means: for independent
talk and encourages
normal population, for dependent normal population.
4 courses
discussions on the
- Hypothesis tests for the variance: hypothesis test for the variance,
theme.
test of equality of variances for two populations normally
distributed.
- Parametric versus non-parametric tests: hypothesis test for a
proportion, tests of uniformity, normality of a distribution, etc.
The professor gives a
talk and encourages
Review of main issues with practical examples.
1 course
discussions on the
theme.
Mandatory bibliography:
 BUIGA, ANUŢA
Statistică inferenţială, curs UBB Cluj Napoca, ediţia 2009.
 LOWRY, RICHARD Concepts and applications of inferential statistics, 2010 Edition, Vassar
College.
 STOCKBURGER, DAVID W. Multivariate Statistics Concepts, Models, and Applications, 2nd
Edition, 2001, Missouri State University.
 Lecture notes.
Additional bibliography:
 Bowers, D. (1991) Statistics for Economics and Business, Macmillan Education Ltd, U.K..
 Keller, G. (2008), Statistics for Management and Economics, South- Western Cengage Learning.
 Mendenhall, W. et all (1986) Statistics for Management and Economics, PWS Publishing
Company, Boston, U.S.A..
8. 2 Seminar/laboratory
Teaching methods
The parameters of a population, sample statistics, introduction to Analysis of terms and
SPSS, calculation of some descriptive statistics in SPSS.
concepts, discussions,
case studies, solving
exercises and real data
examples, discussion of
the homework projects,
etc.
Law of large numbers and examples in SPSS.
Analysis of terms and
concepts, discussions,
The requirements of the first homework are explained to the
case studies, solving
students, with deadline the next meeting.
exercises and real data
examples, discussion of
the homework projects,
etc.
Central limit theorem and applications. Exemplifications in
Analysis of terms and
SPSS.
concepts, discussions,
case studies, solving
The requirements of the second homework are explained to the exercises and real data
students, with deadline the next meeting.
examples, discussion of
the homework projects,
etc.
The construction of confidence intervals for the mean.
Analysis of terms and
Exemplifications in SPSS. Financial and economic applications. concepts, discussions,
case studies, solving
exercises and real data
examples, discussion of
the homework projects,
etc.
Significance level and power of a test. Hypothesis testing for the Analysis of terms and
mean in SPSS. Testing the equality of two means in SPSS
concepts, discussions,
(independent samples versus dependent sample). Financial and case studies, solving
economic applications.
exercises and real data
examples, discussion of
The requirements of the third homework are explained to the
the homework projects,
students, with deadline the next meeting.
etc.
Hypothesis testing for the variance in SPSS. Testing the equality Analysis of terms and
of two variances in SPSS. Financial and economic applications. concepts, discussions,
case studies, solving
exercises and real data
examples, discussion of
the homework projects,
etc.
Observations
1 seminar /
laboratory
1 seminar /
laboratory
1 seminar /
laboratory
1 seminar /
laboratory
2 seminar /
laboratories
1 seminar /
laboratory
Bibliography:
 http://www.hmdc.harvard.edu/projects/SPSS_Tutorial/spsstut.shtml
 BUIGA, ANUŢA
Statistică inferenţială, curs UBB Cluj Napoca, ediţia 2009.
 KELLER, G. (2008), Statistics for Management and Economics, South- Western
Cengage Learning.
 Lecture notes.
 Help documents installed on SPSS.
9.
Corroboration / validation of the discipline content according to the expectations of the epistemic
community representatives, of the ones of the professional associations and also of the representative
employers of the corresponding program.
Inferential statistics is one of the main courses providing the students information on how to jointly apply
economic theory, mathematics and statistical techniques for the purpose of testing hypotheses, estimating
and forecasting economic phenomena. Therefore, it is a course of vital importance for the professional
development of any undergraduate in any economic field that needs quantitative assessments.
10. Evaluation
Type of activity 10.1 Evaluation criteria
10.2 Methods of assessment
10.4 Course
10.3 Share in
final grade
50%
The degree by which the students
Written final exam.
correctly acquired the concepts, notions
and tools of inferential statistics.
The ability to use these concepts, notions
and tools of inferential statistics in
financial and economic applications (i.e.
practical problems, real life situations,
etc.).
10.5
The degree by which the students
The assessment of the
50%
Seminar/laborat correctly acquired the concepts, notions homework projects. The
ory
and tools of inferential statistics.
assessment tries to measure the
The ability to use the statistical tools to degree by which the students
acquired the theory and the
solve case studies, real data examples,
ability to apply it in practical
real life economic and financial
examples and real life
situations.
situations. The realization of
Capacity to interpret the results of the
statistical analysis and to take economic the homework projects is
conditioning the obtaining of
or financial decisions based on these
the final grade.
results.
10.6 Minimum standard of performance
The students should prove that acquired the concepts, notions and tools of inferential statistics above a
minimal accepted level. The students should prove that have the ability to apply this knowledge to
practical problems and real life situations, above a minimal accepted level.
Date of filling
28.01.2015
Signature of the course professor
Signature of the seminar professor
Associate Professor Cristian Marius LITAN
Associate Professor Cristian Marius LITAN
Date of approval by the department
06.02.2015
Head of department’s signature
Professor Diana Andrada FILIP