Course Details

ADVANCED STATISTICS AND DATA ANALYSIS

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
2	DSBE521	ADVANCED STATISTICS AND DATA ANALYSIS	3+0+0	3	7,5

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	DATA SCIENCE
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Introducing hypothesis testing, estimation and inference. Introducing time-series and panel data model as well as the issue of heteroscedasticity. Showing how to analyze and test economic theory with empirical data.
Course Content	Test economic hypotheses and check significance properties. Construct a novel econometric model and test overall performance of the model. Calculate simple and partial correlations between variables within a model. Choose appropriate functional form in regression models. Construct a novel time-series and/or panel data econometric model and test overall performance of the model. Understand strengths and limitations of the methods covered in the course
Course Methods and Techniques	Practical applications is based on R and Pyton softwares
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Asist Prof.Dr. Eda Ustaoglu eda.ustaoglu@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	1) Wooldridge. J.M. (2020). Introductory Econometrics: A Modern Approach. 7th Edition. Cengage Learning. ISBN-13: 978-1-3375-5886-0
	Course notes were prepared using the textbook "Wooldridge. J.M. (2020). Introductory Econometrics: A Modern Approach. 7th Edition. Cengage Learning. ISBN-13: 978-1-3375-5886-0"

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Course Category

Mathematics and Basic Sciences	%50
Social Sciences	%50

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Araştırma Ödevi	1	10	10
Tartışma	1	28	28
Sınıf İçi Aktivitesi	1	42	42
Proje	1	20	20
Okuma	1	45	45
Rapor	1	20	20
Ders dışı çalışma	1	50	50
Derse Devam	1	10	10
Total Work Load			Number of ECTS Credits 7,5 225

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Test economic hypotheses and check significance properties.
2	Construct a novel econometric model and test overall performance of the model.
3	Calculate simple and partial correlations between variables within a model.
4	Choose appropriate functional form in regression models.
5	Construct a novel time-series and/or panel data econometric model and test overall performance of the model.
6	Construct a novel time-series and/or panel data econometric model and test overall performance of the model.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to the course
2	Introduction to the course
3	The Simple Regression Model
4	Multiple Regression Analysis: Estimation
5	Multiple Regression Analysis: Inference
6	Multiple Regression Analysis: Asymptotics
7	LFW
8	Review and Mid Term Exam
9	Multiple Regression Analysis: Further Issues
10	Basic regression analysis with time series data
11	Basic regression analysis with time series data
12	Serial correlation and heteroscedasticity in time series regressions
13	Pooling cross sections across time: Simple panel data methods
14	Pooling cross sections across time: Simple panel data methods

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=75109&lang=en