Quantitative Μethods

Course outline


General Information:


School: Economics & Social Sciences

Department: Business Administration

Level of Studies: Undergraduate

Course code: 306

Semester: C

Course title: Quantitative Methods


Weekly teaching hours: 4

Type of course: mandatory/general background

Prerequisite courses: No

Language of instruction and exams: Greek

The course is offered to Erasmus students: No

Course URL:

Learning outcomes:

-     The use of regression analysis to predict the value of a dependent variable based on the value of an independent variable.

-     Evaluating the assumptions of regression analysis and what to do if they are violated.

-     Inferring the slope and correlation coefficient.

-     The development of multiple regression models.

-     The interpretation of its coefficients.

-     Determining which independent variables to include in a multiple regression model.

-     The use of categorical independent variables in a multiple regression model.

-     The description of the basic steps to be followed in each forecasting process.

-     The presentation of methodological tools for the preliminary investigation of numerical data and for checking the validity and reliability of prediction results.

-     Understanding of the most important forecasting methods.

-     The acquisition of knowledge about the general structure of Linear Programming problems and the necessary conditions for modeling a business problem in the form of Linear Programming.

-     Understanding the optimization mechanism in Linear Programming problems and identifying and interpreting the key elements of the process.

-     Understanding the process of sensitivity analysis and how to apply it to Linear Programming problems, as well as drawing key conclusions and evaluating them in decision making.

-     The perception of the main characteristics of a problem that is solved by applying the dynamic programming methodology.

-     Performing the calculations required to implement the dynamic programming algorithm.

-     Understanding the principle of sub-optimization to the extent that results obtained from the dynamic programming algorithm solution process can be interpreted.

-     The description of the main elements of the costs related to the management of the stocks and the determination of the factors that influence it.

-     The formulation of the analytical form of inventory management costs according to the operation mode of an inventory management system.

-     The explanation in business terms of the optimal inventory policy in terms of its effects on orders of raw materials, products, production schedules, etc.

-     Understanding the link between the financial optimization of inventory costs and the level of customer service.

General Skills:

-     Search, analysis and synthesis of data and information, using the necessary technologies

-     Adaptation to new situations

-     Decision making

-     Autonomous work

-     Teamwork

-     Work in an international environment

-     Work in an interdisciplinary environment

-     Generating new research ideas

-     Project planning and management

-     Respect for diversity and multiculturalism

-     Respect for the natural environment

-     Demonstration of social, professional and ethical responsibility and sensitivity to gender issues

-     Development of criticism and self-criticism

-     Promotion of free, creative and inductive thinking

-     Other skills

Moreover, the course aims to develop the following general skills:

-     Critical ability and self-criticism

-     Ability to cooperate

-     Interpersonal skills

-     Search data using the necessary technologies

Course Content:

-     Simple and multiple linear regression (hypothesis tests, residual analysis and model adequacy testing, model use for predictions),

-     Time series and forecasting (trend analysis, simple moving averages, exponential smoothing, decomposing time series with seasonality, measures evaluating forecast accuracy),

-     Graphical solution of a two-variable linear programming model – sensitivity analysis,

-     Causal dynamic programming, Causal inventory models.

Teaching & Learning Methods - Evaluation

-     Method of delivery: face-to-face

-     Use of information and communication technologies: Use of information and communication technologies in teaching, laboratory education, communication with students. Electronic communication with students, learning-process support through the “e-class” online platform.

Activities & Semester Workload

Lectures: 39

Study of references: 52

Submission of assignments: 26

Use of software: 26

Final exams: 3

Total: 146


Evaluation of students:

The Final grade of the course results from 70% of the final written exams, while the remaining 30% results from the individual assignments.

Recommended bibliography:



Εισαγωγή στη Διοικητική Επιστήμη, TaylorBernardIII

Στατιστική: Βασικές Αρχές με Έμφαση στην Οικονομία και τις Επιχειρήσεις, LevineDavid, SzabatKathryn, StephanDavid

Επιχειρησιακή Έρευνα, Παντελής, Υψηλάντης

Διοίκηση παραγωγικών συστημάτων, Δημητριάδης Σωτήριος Γ., Μιχιώτης Αθανάσιος Ν.



Hogg,R.V. and Tanis,E.A., Probability and Statistical Inference, Prentice Hall, 9th Edition, 2015.

Aczel, A. D. and Sounderpandian, J., Complete Business Statistics, McGraw – Hill & Irwin, 2012.

Doane, D., Seward, L., Applied Statistics in Business & Economics, McGraw-Hill, 7th Edition, 2021.

Hillier F. and Lieberman G., “Introduction to Operations Research”, 11th edition, New York, McGraw-Hill, 2021

JOHN A., LAWRENCE, JR. BARRY A. PASTERNACK, "Applied Management Science" A Computer-Integrated Approach for Decision Making, John Wiley and Sons, 2nd edition, 2002

JOHN A., LAWRENCE, JR.  BARRY A. PASTERNACK, “Applied Management Science: Modeling, Spreadsheet Analysis, and Communication for Decision Making”, John Wiley and Sons, 2002.

Wild R., “Production and Operations Management”, 5th edition, Cassel, 1998.