Linear Models (ΣΕΕ2): Διαφορά μεταξύ των αναθεωρήσεων

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[[Graduate Courses Outlines]] - [https://math.uoi.gr  Department of Mathematics]
* [[Γραμμικά Μοντέλα (ΣEE2)|Ελληνική Έκδοση]]
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=== General ===
=== General ===
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! Course Type
! Course Type
| Special Background
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Specialized general knowledge
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|-
! Prerequisite Courses
! Prerequisite Courses
Γραμμή 39: Γραμμή 42:
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|-
! Course Website (URL)
! Course Website (URL)
| -
| See [https://ecourse.uoi.gr/ eCourse], the Learning Management System maintained by the University of Ioannina.
|}
|}


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{| class="wikitable"
{| class="wikitable"
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! Learning outcomes
! Learning Outcomes
| The purpose of the course is:
|
# the deepening of knowledge of linear models acquired during undergraduate studies
By the end of the course students are expected to demonstrate:
# the extension of 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,
# the presentation of specialized knowledge of Linear Models with applications in statistical data analysis.
* 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
! General Competences
|
|
# Working independently
* Working independently
# Decision-making
* Decision-making
# Adapting to new situations
* Adapting to new situations
# Production of free, creative and inductive thinking
* Production of free, creative and inductive thinking
# Synthesis of data and information, with the use of the necessary technology
* Synthesis of data and information, with the use of the necessary technology
# Working in an interdisciplinary environment
* Working in an interdisciplinary environment
|}
|}


=== Syllabus ===
=== 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.
The General Linear Model of full Rank and its statistical properties. Multiple Regression Analysis. Hypothesis tests, diagnostic measures and residual analysis. Variable selection. Models of non full rank. Estimable functions, One and two-way analysis of variance with equal and unequal numbers per cell.


=== Teaching and Learning Methods - Evaluation ===
=== Teaching and Learning Methods - Evaluation ===
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| 39
| 39
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| Study and analysis of bibliography
| Independent study
| 78
| 70
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| Preparation of assignments and interactive teaching
| Study and analysis of bibliography, Fieldwork
| 70.5
| 78.5
|-
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| Course total  
| Course total  
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=== Attached Bibliography ===
=== Attached Bibliography ===


in Greek
<!-- In order to edit the bibliography, visit the webpage -->
# Καρακώστας, Κ. (1993). Παλινδρόμηση και Ανάλυση της Διακύμανσης. Πανεπιστήμιο Ιωαννίνων
<!-- https://wiki.math.uoi.gr/index.php/%CE%A0%CF%81%CF%8C%CF%84%CF%85%CF%80%CE%BF:MAM127-Biblio -->
# Λουκάς, Σ. (2014). Γενικό Γραμμικό Μοντέλο. Πανεπιστήμιο Ιωαννίνων


in English
{{MAM127-Biblio}}
# Draper , N. R. and Smith, H. (1981). Applied Regression Analysis. John Wiley & Sons, Inc., N.Y.
# Searle, S. R. (1971). Linear Models. John Wiley & Sons, Inc, N. Y., London
# Seber, G. A. F. (1977). Linear Regression Analysis. John Wiley & Sons, Inc, N.Y.

Τελευταία αναθεώρηση της 16:39, 15 Ιουνίου 2023

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

The General Linear Model of full Rank and its statistical properties. Multiple Regression Analysis. Hypothesis tests, diagnostic measures and residual analysis. Variable selection. Models of non full rank. Estimable functions, One and two-way analysis of variance with equal and unequal numbers per cell.

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
Independent study 70
Study and analysis of bibliography, Fieldwork 78.5
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.