Applied Analysis of Regression and Variance
| Title | ΕΦΑΡΜΟΣΜΕΝΗ ΑΝΑΛΥΣΗ ΠΑΛΙΝΔΡΟΜΗΣΗΣ ΚΑΙ ΔΙΑΣΠΟΡΑΣ / Applied Analysis of Regression and Variance |
| Code | 0530 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Common | No |
| Status | Active |
| Course ID | 40000365 |
Programme of Study: Merikīs Foítīsīs (2014-sīmera)
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | Spring | - | 5.5 |
Programme of Study: UPS of School of Mathematics (2014-today)
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses belonging to the selected specialization | 8 | 4 | 5.5 |
| Academic Year | 2013 – 2014 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 4 |
| Class ID | 40050019
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/40050019
- At the Website of the School: users.auth.gr/cmoi
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Probability Theory (especially the distributions and their properties), basics of Statistics and Linear Algebra.
Learning Outcomes
The students, taking into account experimental data, to be able to find a suitable model of the problem, recognizing which of the variables are the explanatory ones and which is the response. To be able to estimate the regression coefficients and study properties of the prediction model. To be able to choose which of the explanatory variables are most important so as to propose a restricted model. To make hypotheses tests for the parameters of the model and to estimate confidence intervals for various significance letters. In the case of several variables to be able to apply various criteria for finding the best prediction model. To be able to study the problem with the analysis of variance method, also, and to compare the two methods.
All the above to be able to perform even by hand and by computer using appropriate statistical packages.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in teams
- Work in an interdisciplinary team
Course Content (Syllabus)
Theory: Simple and multiple linear regression (estimate of parameters, confidence intervals of estimators, hypotheses tests, determination coefficient, repeated measurements). Choice of Variables (multicollinearity, restricted model, criteria of choice of the best model). Transformations of variables (Dummy variables, weighted least squares method). Analysis of variance with one and two factors (finding of ANOVA table, equivalence with regression, parameters' estimation, graphical methods), the general factorial experiment.
Laboratory: Use of statistical package R. Introduction to the basics of R and study of its possibilities for descriptive statistics and graphical illustration. The regression and analysis of variance routines of R are studied. The laboratorial courses is obligatory. Ranked only those who have followed the 70% of the laboratory courses.
Keywords
regression, analysis of variance, least squares method, dummy variables, restricted model
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 26 | ✓ | |||
| Laboratory Work | 26 | ✓ | |||
| Total | 52 |
Student Assessment
Description
70% of the rank is given in written exams, 30% is given from laboratory exercises
Student Assessment methods
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Βιβλίο [11028]: Εφαρμοσμένη στατιστική, Μπόρα - Σέντα Ε., Μωυσιάδης Χρόνης Θ.
Additional bibliography for study
Morris H. DeGroot, Mark J. Schervish. Probability and Statistics
Montgomery Douglas C.,Runger George C.(2003). Applied Statistics and Probability for Engineers
Trosset Michael W. (2008). An Introduction to Statistical Inference and Its Applications with R
Καρακώστας, ΚΞ Γραμμικά μοντέλα, Παλινδρόμηση, Ανάλυση Διακύμανσης
Φωκιανός, Κ. Θεωρία των γραμμικών μοντέλων
Last Update
03-09-2013
