Linear Models (ΣΕΕ2): Διαφορά μεταξύ των αναθεωρήσεων

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By the end of the course students are expected to demonstrate:
By the end of the course students are expected to demonstrate:
* A strong foundation in simple linear, multiple regression and in the one- and two-way analysis of variance as well as in extending these concepts,
* A strong foundation in simple linear, multiple regression and in the one- and two-way analysis of variance as well as in extending these concepts,

Αναθεώρηση της 22:32, 27 Οκτωβρίου 2022

Graduate Courses Outlines - Department of Mathematics

General

School School of Science
Academic Unit Department of Mathematics
Level of Studies Graduate
Course Code ΣΣΕ2
Semester 1
Course Title Linear Models
Independent Teaching Activities Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type

Specialized general knowledge

Prerequisite Courses -
Language of Instruction and Examinations Greek
Is the Course Offered to Erasmus Students Yes (in English, reading Course)
Course Website (URL) See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning Outcomes

By the end of the course students are expected to demonstrate:

  • A strong foundation in simple linear, multiple regression and in the one- and two-way analysis of variance as well as in extending these concepts,
  • Deep knowledge of the main assumptions of the general linear model and their implications when violated,
  • How to conduct diagnostics and correct for the violation of the assumptions of the general linear model,
  • How to interpret various coefficients and in general how to analyze data with linear models,
  • How to deal with multicollinearity effects, missing data e.t.c..
General Competences
  • Working independently
  • Decision-making
  • Adapting to new situations
  • Production of free, creative and inductive thinking
  • Synthesis of data and information, with the use of the necessary technology
  • Working in an interdisciplinary environment

Syllabus

General Linear Model of full Rank, Multiple Regression Analysis, Analysis of residuals-Diagnostics, Selection of Variables, Two way analysis of variance with equal and unequal numbers per cell. Models of non full rank.

Teaching and Learning Methods - Evaluation

Delivery Face-to-face
Use of Information and Communications Technology Use of ICT in communication with students
Teaching Methods
Activity Semester Workload
Lectures 39
Study and analysis of bibliography 78
Preparation of assignments and interactive teaching 70.5
Course total 187.5
Student Performance Evaluation Final written exam in Greek (in case of Erasmus students in English).

Attached Bibliography

  • Καρακώστας, Κ. (2002). Γραμμικά Μοντέλα: Παλινδρόμηση και Ανάλυση Διακύμανσης. Πανεπιστήμιο Ιωαννίνων.
  • Λουκάς, Σ. (2014).  Γενικό Γραμμικό Μοντέλο. Πανεπιστήμιο Ιωαννίνων.
  • Οικονόμου, Π. και Καρώνη, Χ. (2010). Στατιστικά Μοντέλα Παλινδρόμησης, Εκδόσεις Συμεών.
  • Draper, N.R. and H. Smith, (1998). Applied Regression Analysis, Third Edition, Wiley,
  • Searle, S.R., (1997). Linear Models, Wiley Classics Library, Wiley,
  • Seber, G.A.F. and A.J. Lee, (2003). Linear Regression Analysis, 2nd Edition, Wiley.