Department of Informatics and Telematics

Statistics

ΜΥ04 - Statistics

General Information

School: Digital Technology

Department: Informatics and Telematics

Level: Undergraduate

Course Title: Statistics

Course id: ΜΥ04

Type: Background Course 

Semester: 3

Teaching and Examination Language: Greek

Is the course offered in Erasmus: Yes

Course web-page: http://eclass.hua.gr/courses/DIT142/

Activities

Lectures (Theory): 3,0

Lab lectures: 2,0

ECTS credits: 5,0

Learning Outcomes

The objective of the course is to be well prepared for problem-solving involving statistics in the rest of your courses, as well as gaining an understanding of the role of statistics in your daily life.

General Skills

Search, analysis and synthesis of data and information
Adaptation in new conditions
Decision Making
Independent work
Work at an interdisciplinary framework
Formulation of new research ideas
Promoting reasoning and self improvement
Promoting free, creative and deductive reasoning

Course Content

1.        Elements of Descriptive Statistics: Population, Samples, Random Samples, Descriptive Measures, Frequency and Relative Frequency Tables, Plots of Empirical Frequency Distributions
2.        Applications of Descriptive Statistics relative to Science of informatics and Telematics,  lab exercises with R language
3.        Statistical Inference: Point Estimation, Confidence Intervals  of population parameters
4.        Statistical Inference: Hypothesis Testing of population parameters
5.        Applications of Statistical Inference relative to Science of informatics and Telematics,  lab exercises with  R language
6.        Correlation and Linear Regression
7.        Analysis of Variance
8.        Applications of Linear Regression and Analysis of Variance   relative to Science of informatics and Telematics,  lab exercises with R language
9.         - Independence Test
10.         - Goodness of Fit Test
11.         - Homogeneity Test
Applications of  relative to  - Homogeneity Test,  lab exercises with  R language

Learning and Teaching Methods - Evaluation

Teaching methods: face-to-face

Use of ICT: 

Use of R language. Support the learning process through the electronic platform e-class

Course Organization

 

Activity

Semester work load

Lectures

39,0

Lab exercises

26,0

Individual of group projects

 

Lab report preparation

 

Thesis 

 

Independent Study

60,0

Total

125

Assessment

The evaluation of each student will be made with writing exams and/or interim written exams.

Literature

  •       A. Kyriakoussis (2000). Statistical Methods, Symmetria Editions, Athens.
    ●        S. Loukas (2003), Statistics. Kritiki Editions.
    ●        M. Filippakis (2017). Statistical Methods and Regression Analysis for New Technologies. Tsotras Edistions.