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Linear Models (ΣΕΕ2): Διαφορά μεταξύ των αναθεωρήσεων

Linear Models (ΣΕΕ2): Διαφορά μεταξύ των αναθεωρήσεων

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Γραμμή 101: Γραμμή 101:
=== 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.

Αναθεώρηση της 16:44, 24 Αυγούστου 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 Special Background
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 The purpose of the course is:
  1. the deepening of knowledge of linear models acquired during undergraduate studies
  2. the extension of these concepts
  3. the presentation of specialized knowledge of Linear Models with applications in statistical data analysis.
General Competences
  1. Working independently
  2. Decision-making
  3. Adapting to new situations
  4. Production of free, creative and inductive thinking
  5. Synthesis of data and information, with the use of the necessary technology
  6. 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

  • Casella, G. and Berger, R.L. (2002). Statistical Inference. Duxbury Press; 2nd edition.
  • Mood A. et al. (1974). Introduction to the theory of Statistics. McGraw-Hill.
  • Roussas G. (1997). A course in Mathematical Statistics. Academic Press.
  • Hogg, R and Craig, A. (1978). Introduction to Mathematical Statistics.
  • Lehmann, E.L. and Casella, G. (1998). Theory of point estimation. Springer; 2nd edition
  • Τ. ΠΑΠΑΙΩΑΝΝΟΥ-Κ. ΦΕΡΕΝΤΙΝΟΥ: Μαθηματική Στατιστική Εκδόσεις Σταμούλη.
  • Ηλιόπουλος, Γ. (2013). Βασικές Μέθοδοι Εκτίμησης Παραμέτρων. Εκδόσεις Σταμούλη; 2η έκδοση
  • Bickel, P.J. and Doksum, K.A. (1977). Mathematical Statistics, Basic Ideas and Selected Topics, Vol. 1. Holden-Day.
  • Rohatgi, V.K. (1976). An Introduction to Probability Theory and Mathematical Statistics. John Wiley and Sons, New York.
  • Rao, C. R. (1973). Linear Statistical Inference and its Applications. Wiley: 2nd edition.
  • Lehmann, E.L. and Romano, J.P. (2005). Testing statistical hypotheses. Springer; Third edition, New York.
  • Van der Vaart (1998). Asymptotic Statistics. Cambridge University Press.
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