ECON 2115 - Statistics I - Course Information

Basic Information

Course Code:

ECON 2115

Course Name:

Statistics I

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

4.00

Instructor:

Dr. Öğr. Üyesi FIRAT YILMAZ

Course Objectives

The aim of this course is to enable students to understand fundamental statistical concepts and methods and to apply these methods to the analysis of economic and social data. The course covers types of data, descriptive statistics, probability, random variables, probability distributions, sampling, estimation, confidence intervals, hypothesis testing, correlation, and simple linear regression. It aims to develop students' ability to organize, summarize, analyze, and interpret data and statistical findings in economic and social contexts.

Course Content

Introduction to statistics; types of data and levels of measurement; classification and presentation of data; frequency distributions, tables, and graphs; measures of central tendency; measures of dispersion and variability; basic probability concepts and probability rules; conditional probability and Bayes' approach; random variables; discrete and continuous probability distributions; binomial and normal distributions; sampling and sampling distributions; point estimation and confidence intervals; fundamental principles of hypothesis testing; tests for a single population mean and proportion; comparison of two populations; introduction to correlation and simple linear regression; statistical analysis and interpretation of economic and social data.

Prerequisites / Corequisites

None

Course Books / Materials / Recommended Resources

Newbold, P., Carlson, W. L. & Thorne, B., Statistics for Business and Economics, Pearson.

Learning Outcomes

Code	Description
LO1	Ability to explain fundamental statistical concepts and types of data
LO2	Ability to calculate and interpret descriptive statistics
LO3	Ability to apply probability and distribution concepts
LO4	Ability to use the logic of sampling, estimation, and hypothesis testing
LO5	Ability to evaluate statistical results in the context of social sciences

Weekly Course Content

Week	Content
1	Introduction to statistics; the role of statistics in economics and social sciences, fundamental concepts, population, and sample
2	Types of data and levels of measurement; qualitative and quantitative data, cross-sectional and time-series data
3	Organization and presentation of data; frequency distributions, tables, histograms, and other graphical displays
4	Measures of central tendency; arithmetic mean, median, mode, and interpretation of economic data
5	Measures of variability; variance, standard deviation, coefficient of variation, and evaluation of the shape of distributions
6	Introduction to probability; fundamental probability concepts, addition and multiplication rules, and economic applications
7	Conditional probability, independence, and Bayes' approach
8	Midterm Examination
9	Random variables and probability distributions; expected value and variance
10	Discrete probability distributions; binomial distribution and basic applications
11	Continuous probability distributions; normal distribution, standard normal distribution, and z-scores
12	Sampling methods, sampling distributions, and the Central Limit Theorem
13	Point estimation and confidence intervals; estimation of population means and proportions
14	Introduction to hypothesis testing; null and alternative hypotheses, significance level, p-value, and statistical decision making
15	Introduction to correlation and simple linear regression; integrated statistical application and evaluation using economic and social data

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	15	3.00	45.00
Pre-Class Individual Study	15	4.00	60.00
Midterm Exam/Preparation	1	15.00	15.00
Final Exam/Preparation	1	30.00	30.00
Total Workload (Hours)			150
ECTS Credit (Workload / 25)			5

Assessment

#	Assessment Type	Contribution (%)
1	Midterm Exam	%40
2	Final Exam	%60
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11
PO-12

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)

Academic Integrity and Artificial Intelligence

Students are expected to comply with the principles of academic integrity in all examinations, coursework, and other academic activities. Information, data, tables, figures, and ideas obtained from external sources must be properly acknowledged. Plagiarism, manipulation of data or results, completing work on behalf of another student, and unauthorized collaboration are considered violations of academic integrity. Artificial intelligence tools may be used, when permitted by the course instructor, to clarify statistical concepts, generate practice examples, or explore data-analysis methods. Students are responsible for verifying the accuracy of AI-generated results. AI tools may not replace students' own calculations, analyses, or interpretations, and their use must comply with the guidelines established by the course instructor.

Sustainable Development Goals