Course outline

School

Economics & Social Sciences

Department

Business Administration

Level of Studies

Undergraduate

Course code

306

Semester

 3^ο

Course title

Quantitative Methods

INDEPENDENT TEACHING ACTIVITIES

Weekly teaching hours
-

ECTS

4

5

Type of course

Prerequisite course

Language of instruction and exams

Greek

The course is offered to Erasmus students

Course URL

https://eclass.uth.gr/courses/DE_U_118/

Learning outcomes

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• 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

• 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 METHOD

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. eclass.

TEACHING ORGANIZATION

STUDENT EVALUATION

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

Greek

1. • 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.

English

• • Hogg,R.V. and Tanis,E.A., Probability and Statistical Inference, Prentice Hall, 9^th Edition, 2015.
  • Aczel, A. D. and Sounderpandian, J., Complete Business Statistics, McGraw – Hill & Irwin, 2012.

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

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

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

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

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