POLS 160 - Statistics for Social Sciences

Basic Information

Course Code:

POLS 160

Course Name:

Statistics for Social Sciences

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

4.00

Instructor:

Dr. Öğr. Üyesi SÜLEYMAN CENGİZCİ

Course Objectives

The course's main goal is to provide a thorough and self-contained description of classical or mainstream statistical theory and its applications (in the sense that the necessary probability theory is included).

Course Content

Students should learn how to perform basic data analysis (e.g., finding mean, median, standard deviation, and other descriptive statistics) as well as how to present data graphically (e.g., histograms, stem plots). They should understand the differences between population and sample, and theoretical and sample characteristics. Since it is not worthwhile to teach statistics without elements of probability theory, studying its basic notions and results is a part of the course.

Prerequisites / Corequisites

NA

Course Books / Materials / Recommended Resources

Camm et al., Business Analytics: Descriptive, Predictive, Prescriptive, 4th edition,Cengage, 2020.S. C. Albright, Business Analytics: Data Analysis and Decision Making, 7th edition,Cengage, 2020.

Learning Outcomes

Code	Description
LO1	Students should understand what probability space, a random event, and the probability of an event are.
LO2	They should know how to calculate the probabilities of complex events, solve elementary combinatorial problems, and use the probability and Bayes formulas.
LO3	The students should have a firm grasp of what a random variable is and how it is distributed.

Weekly Course Content

Week	Content
1	Introduction to Business Analytics
2	Mathematical preliminaries
3	General Purposes of Statistics
4	Introduction to Statistics
5	Numerical descriptive measures - I
6	Numerical descriptive measures - II
7	A general review & exercises
8	Midterm
9	Introduction to probability - I (Discrete)
10	Introduction to probability - II (Continuous)
11	Hypothesis testing - I
12	Hypothesis testing - II
13	Introduction to linear regression analysis
14	A general review & exercises
15	A general review & exercises

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	15	3.00	45.00
Practice	10	1.00	10.00
Pre-Class Individual Study	15	3.00	45.00
Midterm Exam/Preparation	1	8.00	8.00
Final Exam/Preparation	1	8.00	8.00
Total Workload (Hours)			116
ECTS Credit (Workload / 25)			4

Assessment

#	Assessment Type	Contribution (%)
1	Midterm Exam	%35
2	Homework	%15
3	Final Exam	%50
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Lecture (expository teaching), interactive discussion	Listening and taking notes.	Standard classroom technologies, multimedia tools (projector, computer, digital presentations)
2	Problem Solving	* Analyzing physical and physiological problems using problem-solving techniques and developing appropriate solutions.
3	Homework	Solving and analyzing homework questions using a computer and Excel.	Computer

Academic Integrity and Artificial Intelligence

Violations of scholastic honesty include, but are not limited to cheating, plagiarizing, fabricating information or citations, facilitating acts of dishonesty by others, having unauthorized possession of examinations, submitting work of another person or work previously used without informing the instructor, or tampering with the academic work of other students. Any form of scholastic dishonesty is a serious academic violation and will result in disciplinary action. Regarding the use of generative artificial intelligence (GenAI) tools in this course, the provisions of the 'Ethical Guide on the Use of Generative Artificial Intelligence in Scientific Research and Publication Activities of Higher Education Institutions (2024)' published by the Council of Higher Education (YÖK) shall apply.