Data analysis of the Mathematics department of Athens for the years 2000-2021

Postgraduate Thesis uoadl:3352010 83 Read counter

Unit:
Κατεύθυνση Στατιστική και Επιχειρησιακή Έρευνα
Library of the School of Science
Deposit date:
2023-09-14
Year:
2023
Author:
Koffas Nikolaos
Supervisors info:
Φώτιος Σιάννης, Επίκουρος Καθηγητής, Τμήμα Μαθηματικών, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Απόστολος Μπουρνέτας, Καθηγητής, Τμήμα Μαθηματικών, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Μαργαρίτα Καραλιοπούλου, Μέλος ΕΔΙΠ, Τμήμα Μαθηματικών, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Original Title:
Ανάλυση δεδομένων του τμήματος των Μαθηματικών της Αθήνας για τις χρονίες 2000-2021
Languages:
Greek
Translated title:
Data analysis of the Mathematics department of Athens for the years 2000-2021
Summary:
This work was conducted as a thesis of the postgraduate program in Statistics
and Operations Research of the Department of Mathematics at the National and
Kapodistrian University of Athens. Our objective was to address questions that
have been established in the academic field, using statistics as the sole impartial
criterion. We aimed to provide objective answers, thus rectifying the subjective
element that emerged from previous discussions.
The main questions that occupied our research concerned student performance.
We investigated which factors influence each student’s performance and how, as
well as students’ attitudes towards exams and their intention to participate in
them, among other questions, which now have clear answers.
The process we followed encompassed all stages of data analysis. Starting
from a completely raw dataset and after correcting any errors and inconsistencies
(which were documented for future improvement), we proceeded with its analysis
using descriptive statistics, offering an overall picture of both the dataset and the
information pertaining to the department (Chapter 3). In this chapter, we exclusively
used the computational package ”SAS Enterprise Guide” and its extensions
in the SQL language (”PROC SQL”). Subsequently, we presented more detailed
results by creating linear and non-linear models (Chapter 4). Our analyses in
Chapter 4 were based on the programming language R statistics. The theoretical
background required to generate these specific models is presented in Chapter 2.
Main subject category:
Science
Keywords:
Data analysis, Statistics, Mathematics, SAS Enterprise Guide, PROC SQL, R statistics
Index:
Yes
Number of index pages:
2
Contains images:
Yes
Number of references:
16
Number of pages:
108
File:
File access is restricted only to the intranet of UoA.

NikolaosKoffasThesis14.09.2023.pdf
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File access is restricted only to the intranet of UoA.