SYLLABUS PROPOSAL
B ASIC S TATISTICS
A CADEMIC YEAR 2018/2019
SYLLABUS
NAME OF THE COURSE
FACULTY:
Faculty of Business and Economics
DEPARTMENT:
COURSE CODE:
Faculty of Business and Economics
LEVEL & STATUS:
6ECTS Credits
BE-402
Core
Undergraduate / SPRING. 2019
2h Lecture/ 1h Practice
COURSE PROFESSOR:
Hyrije Abazi-Alili
Office: 102.26
E-mail: h.abazi@seeu.edu.mk
Tel: +389 44 356 000
OFFICE HOURS:
Campus Tetovo:
Tuesday,10:00-12:00, Room 102.26
Wednesday,10:00-12:00, Room 102.26
Campus Skopje:
Monday, 13:00-14:00
LEARNING SCHEDULE:
Lecture:
Monday, 10:00-12:00
Practice:
Monday, 12:00-13:00
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NEWS AND UPDATES FOR THE ACADEMIC YEAR 2018/19
The software applied on this modue is STATA 13.
The latest version reading material (book) is ordered.
PREREQUISITES
Statistics
EQUIPMENT NEEDED
Software/Application needed: STATA 13
GOOGLE CLASSROOM
We will utilize Google Classroom as the main course management system. The
assignments, course resources, announcements, gradebook, quizzes and other items
will be used throughout the Google Classroom.
For help visit the E-Learning Center(701) or contact via email: elc@seeu.edu.mk
SEEU WEBMAIL
We will use the webmail as the main communication tool. It is mandatory to check
your SEEU email account regularly.
MYSEEU
Exam dates and other important news and events about the Faculties will be
published in the MySeeu portal. It is mandatory to check the portal regularly.
COURSE OUTLINE AND OBJECTIVES
If you complete the course successfully, you should be able to:






be familiar with the key ideas of statistics that are accessible to a student with a
moderate mathematical competence
be able to routinely apply a variety of methods for explaining, summarising and
presenting data and interpreting results clearly using appropriate diagrams, titles and
labels when required.
be able to summarise the ideas of randomness and variability, and the way in which
these link to probability theory to allow the systematic and logical collection of
statistical techniques of great practical importance in many applied areas.
have a grounding in probability theory and some grasp of the most common statistical
methods
be able to perform inference to test the significance of common measures such as
means and proportions and conduct chisquare tests of contingency tables
be able to use simple linear regression and correlation analysis and know when it is
appropriate to do so.
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LEARNING OUTCOMES
Descriptors for learning outcomes:
CATEGORY
LEARNING OUTCOMES
Knowledge and
understanding
1.
Basic concepts of
econometrics
2.
Differentiate between
different datasets
1. Run regression models in
STATA 13
2. Construct different
diagrams in STATA
13
1. Interpretation of the data
2. Intertetation of the regression
results
Applying knowledge and
understanding
Making judgment
ASSESSMENT
METHODOLOGY
1. Active participation in
class
1. Lab Work
%
10
05
1. Lab work
2. Mid-term
05
35
1.
Lab Work
05
Communications skills
1.
Logical interpretation of
the regression output
1.
Final Exam
35
Learning skills
1.
Learn the statistical
software STATA 13
1.
Lab Work
05
Total
100
CORRELATION OF THE SUBJECT WITH THE FINAL OUTPUT
EDUCATIONAL PROFILE, RESPECTIVELY: INSTITUTION OR BUSINESS
SECTOR
This is a practical module that will make the students master the understadings of
data analysis and econometric models. Showing knowledge on this sphere is an
additional input on the students profile. Both institution and business sector highly
evaluate the knowledge that supports data driven decisions.
ELEMENTS SUPPORTING THE DEVELOPMENT OF CRITICAL THINKING
The module is developed in such a way that the students advance their critical
thinking. Especially when it comes to the interpretation of the coefficients. The
students should use their theoretical knowledge and apply to criticaly interpret the
results.
ASPECT OF DIGITAL COMPETENCES
This module develops some of the studends digital competences:
- Usage of the statistical software STATA 13.
4
-
Google Classroom communication and course material.
Online Resource Center with study guide materials and data sets.
CRITERIA FOR PASSING THE EXAM
CRITERIA FOR PASSING THE EXAM DURING THE REGULAR SESSION
Master the key concepts and techniques of regression analysis, together with the
interpretation of the coefficients, tests, and models in general.
CRITERIA FOR PASSING THE EXAM AFTER THE REGULAR SESSION(MAKE UP EXAM)
Be able to provide output of the regression analysis and interpretation of the results.
CRITERIA FOR PASSING THE EXAM FOR PART-TIME STUDENTS
Master the key concepts and techniques of regression analysis, together with the
interpretation of the coefficients, tests, and models in general. The difference from the regular
students with be the practice of the STATA 13.
DETAILS OF ASSESSMENT INSTRUMENTS
TEACHING AND LEARNING METHODS
Power Poit Slides, Data files, Lab work
EXAMS
2 examinations, Mid-term and Final exam.
PROJECTS
4 Lab works
ASSIGNMENTS
Homework and Assignments
SUMMARY DESCRIPTION OF ASSESSMENT
Attendance
Participation
Homework
Assignment
Seminar work
Case study
Presentation
Essay
Lab work
Project
Midterm Exam
05
05
10
10
35
5
Final Exam
35
Total
100
RULES AND REGULATIONS
Link to student documents and guidelines
Read me carefully
BIBLIOGRAPHY
MAIN READINGS - BASIC TEXT
Title: Introduction to Econometrics
Author: Christopher Dougherty
Publisher: Oxford University Press
Year: 2011
ISBN:978-0-19-967682-8
SUPPLEMENTARY MATERIALS
Lab materials
LIBRARY "MAX VAN DER STOEL"
Title: Introduction to Econometrics
Author: Christopher Dougherty
Publisher: Oxford University Press
Year: 2011
ISBN:978-0-19-967682-8
LINKS
https://global.oup.com/uk/orc/busecon/economics/dougherty5e/student/studyguide/
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TENTATIVE SCHEDULE
WEEK MODULES
ТЕACHING & LEARNING
1
READINGS
(18.02 LECTURES
)
PRACTICAL
STUDENT WORKLOAD
2
READINGS
(25.02
LECTURES
)
3
(04.03
)
PRACTICAL
STUDENT WORKLOAD
READINGS
LECTURES
PRACTICAL
4
(11.03
)
STUDENT WORKLOAD
READINGS
LECTURES
DESCRIPTION
Syllabus
Course Syllabus
Downloading STATA 13
3h
Simple Regression Analysis
-
Simple Regression Model
-
Deriving Linear Regression Coefficients
Chapter 01 Exercises 1.1 – 1.4 (pg. 97)
3h
Simple Regression Analysis
-
Interpretation of a Regression Equation
-
Changes in the Units of Measurement
-
Goodness of Fit
Lab Work using STATA 13
- Learning the commands: sum, gen, run, etc.
- Chapter 01 Exercises 1.5-1.17 (pg. 105)
- Chapter 01 Exercises 1.19-1.26 (pg. 112)
3h
Properties of the Regression Coefficients
-
Types of Regression Model and Assumptions
for Model A
-
Random Components, Unbiasedness of the
Regression Coefficients
5
(18.03
)
PRACTICAL
STUDENT WORKLOAD
READINGS
LECTURES
Chapter 02 Exercises 2.1 – 2.7 (pg.125)
3h
Hypothesis Testing
-
Testing A Hypothesis relating to a regression
Coefficient
-
One-sides t Tests of Hypotheses Relating to
Regression Coefficients
-
Confidence Intervals for Regression
Coefficients
7
6
(25.03
)
PRACTICAL
STUDENT WORKLOAD
READINGS
LECTURES
- F Test of Goodness of Fit F
Chapter 02 Exercises 2.16 – 2.36 (pg.153)
3h
Multiple Regression Analysis

Multiple Regression with Two explanatory
Variables: Example

PRACTICAL
7
(08.04
)
STUDENT WORKLOAD
READINGS
LECTURES
Graphing a Relationship in a Multiple
Regression Model
Lab Work
Chapter 03 Exercises 3.1 – 3.8 (pg.164)
3h
Multiple Regression Analysis

Properties of the Multiple Regression
Coefficients

Precision of the Multiple Regression
Coefficients
PRACTICAL
8
(15.04
)
STUDENT WORKLOAD
READINGS
LECTURES
Lab Work
Chapter 03 Exercises 3.9 – 3.13 (pg.164)
3h
Multiple Regression Analysis

Multicollinearity

Possible Indirect Measures for Alleviating
Multicollinearity
PRACTICAL
9
(22.04
)
STUDENT WORKLOAD
READINGS
LECTURES
Lab Work
Chapter 03 Exercises 3.14 – 3.18 (pg.172)
3h
Multiple Regression Analysis

Equation

PRACTICAL
10
F Test of Goodness of Fit for the Whole
STUDENT WORKLOAD
READINGS
F Tests Relating to Groups of Explanatory
Variables
Lab work
Chapter 03 Exercises 3.19-3.21 (pg. 189)
3h
Nonlinear Models and Transformations of Variables
8
(06.05
)
LECTURES

Linearity and Nonlinearity

Elasticities and Logarithmic Models

Semilogarithmic Models
The Distubance Term in Logarithmic Models
Lab work
Chapter 04 Exercises 4.2-4.12 (pg. 214)
3h

PRACTICAL
11
(13.05
)
STUDENT WORKLOAD
READINGS
LECTURES
Nonlinear Models and Transformations of Variables

Quadratic Explanatory Variables

Interactive Explanatory Variables

Ramsey's Reset Test of Functional
Misspecification

PRACTICAL
12
(20.05
)
STUDENT WORKLOAD
READINGS
LECTURES
Nonlinear Regression
Lab work
Chapter 04 Exercises 4.13-4.18 (pg. 229)
3h
Dummy Variables

Dummy Variable Classification with two
Categories

Dummy Classification with More than Two
Categories

The Dummy Variable Trap

Slope Dummy Variables
Chow Test
Lab work
Chapter 05 Exercises 5.1 – 5.29 (pg. 229)
3h

PRACTICAL
STUDENT WORKLOAD
EXAMS AND CLINICAL TEACHING
13
MIDTERM EXAM
01.04.2019
14
CLINICAL TEACHING
22.04.2019
15
FINAL EXAM
27.05.2019
NOTE:
The exam week and clinical teaching week are flexible.
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