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| STAT0800-1 | Models and Methods in Applied Statistics
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| Duration : | 30h Th |
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| Number of credits : |
| Master in Business Engineering, didactic approach, 1st year | | First semester | | 5 |
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| Master degree in Business Engineering, professional Focus in Financial Engineering, 1st year | | First semester | | 5 |
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| Master in Management Engineering, professional Focus, 1st year | | First semester | | 5 |
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| Master en ingénieur de gestion, à finalité spécialisée en intrapreneuriat, 1st year | | First semester | | 5 |
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| Master en ingénieur de gestion, à finalité spécialisée en Modélisation et technologie, 1st year | | First semester | | 5 |
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| Master in Business Engineering, professional Focus in Supply Chain Management, 1st year | | First semester | | 5 |
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| Master en ingénieur de gestion, à finalité spécialisée en performance management systems, 1st year | | First semester | | 5 |
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| Lecturer : | Cédric Heuchenne |
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Language(s) of instruction :
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| English language |
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Course contents :
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| In this course, the methods studied in basic statistical courses are adapted to analyzing useful applied issues in Economics and Management: variance analysis (comparison of several averages); inter-variable relation modelling (linear models); nonparametric tests. Students will also be introduced, through simple examples, to the maximum likelihood estimation method, which is particularly useful in more complex models used in Econometrics. Finally, multivariate analysis and statistical process control will be introduced, topics especially interesting for students which are oriented to quantitative methods and supply chain management. |
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Learning outcomes of the course :
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| The aim of this course is to give an overview of statistical problems met in the fields of Economics and Management.
By the end of the course, students should be able to understand and solve those problems in practice (by example, explaining variances, modelling relations between variables or using most important statistical tools to manage industrial processes). This course is also a prerequisite for quantitative courses which students will take later in their programme. |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| Basic course in probability (cumulative distribution function, density, distribution, mean, variance, usual discrete and continuous univariate laws, multivariate normal) and statistical inference (estimation , confidence intervals, hypothesis tests). Equivalent to the content of the course: Probability and statistical inference STAT1208-1 |
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Mode of delivery (face-to-face ; distance-learning) :
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| Used methodology
A1. Ex-cathedra classes: theoretical introduction and applications (quick overview of lessons of previous years, presentation of various methods, interpretation of their solutions, examples).
A1. Study and comprehension of the course material.
A2. Supervised software applications: the professor presents the software to the students during the teaching sessions, and gives exercises to them. Each student is expected to solve those exercises, aside from the teaching sessions (with the possible help from the professor).
A5. Supervised real data analysis project: the professor submits to the students a real-life problem, for which several questions need to be solved. Thanks to A1 and A2 above-mentioned, and under the supervision of the professor, each group of students gives solutions to the problem. The teacher interacts with the students during meetings, and comments each obtained result, as well as each used method. He encourages the students to choose and correctly assess their procedures, to understand the limitations of those, as well as to interpret, discuss and detail their results. The overall aim of the process is for the students to be able to propose final solutions based on a firm and accurate argumentation.
A4. Redaction of a project report: critical synthesis, relevant analysis and adequate presentation of the results are required. |
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Recommended or required readings :
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| The slides of the course, the reference books improving comprehension and the statements of exercises and project will be placed at the disposal of students (see the campus lola). |
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Assessment methods and criteria :
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| The evaluation is divided into four parts: the class performance (10%), homeworks evaluated during the oral exam, the computational project treating a "real life" problem with methods displayed during the theoretical lectures (40%) and an oral exam during January first session about the whole course (50%). |
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Contacts :
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| Alireza Faraz, HEC-ULg Management School of the University of Liège, N1, local 330, email: Alireza.Faraz@ulg.ac.be
Cédric HEUCHENNE, HEC-ULg Management School of the University of Liège, N1, local 309, email: C.Heuchenne@ulg.ac.be |
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