Linear Models (ΣΕΕ2): Διαφορά μεταξύ των αναθεωρήσεων
Από Wiki Τμήματος Μαθηματικών
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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.. | |||
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! General Competences | ! General Competences | ||
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* 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 | |||
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Αναθεώρηση της 22:31, 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
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By the end of the course students are expected to demonstrate:
| |
|---|---|
| General Competences |
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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 | ||||||||||
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| Use of Information and Communications Technology | Use of ICT in communication with students | ||||||||||
| Teaching Methods |
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| 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.