# Statistics II

Course ID:
ECO_122N
Semester:
Year of Study:
Category:
For Erasmus Students:
Yes

## Learning Outcomes

By the end of this course the student will be able to:

• Have basic knowledge of theoretical probability distributions which constitute an essential methodological tool.
• Understand and be able to apply essential statistical inference. This implies that students should develop critical thinking on decision making.
• Understand and apply basic regression analysis to decision making

By the end of this course the student will have developed the following skills:

• Ability to exhibit knowledge and understanding of the essential facts, concepts, theories and applications which are related to statistical inference and regression analysis.
• Ability to adopt and apply methodology for solving problems in the fields of statistical inference and regression analysis.
• Ability to use computational techniques in the aforementioned fields.
• Ability to interact with experts in statistics.

## Course Contents

• Theory:
• Large sample statistical inference. Sampling distributions. Means, difference between two means, proportions, difference between two proportions. Confidence intervals. Statistical testing. Small sample statistical inference. Student's t probability distribution. Means, difference between two means, paired difference test, proportions, difference between two proportions. Inferences about a population variance. The χ2 probability distribution. Comparing two population variances. The F probability distribution. Introduction to simple and multiple regressions. The method of least-squares. Testing the utility of a model. Model building. Elements of time-series analysis.

## Teaching Activities

Lectures (4 hours per week) and Tutorials (2 hour per week)

## Teaching Organization

 Activity Semester workload Lectures (4 hours per week x 13 weeks) 52 hours Tutorials (2 hour per week x 13 weeks) - solving of representative problems 26 hours Hours for private study 122 Total number of hours for the Course (25 hours of work-load per ECTS credit) 200 hours (total student work-load)

## Assessment

The overall course grade is the sum of

b) 20 percent of the mid-term exam grade

### Course Info

Teaching Hours:
6 hours per week
ECTS Credits:
8.00
Teaching Credits:
5.00
Weight:
2.00
Language:
Teaching Method:
General Competences:
Indicative Prerequisites:

### Current Tutors

Instructor:

#### Polymenis Athanasios

Assistant Professor
Field of Expertise:
Statistics, Mathematics, Econometrics
Organic Unit / Lab:
Laboratory of Quantitative Economics and Information Systems
E-mail:
Telephone:
Office Hours:
Tuesday 16.00-15.00
Wednesday 10.00-12.00