Course Contents

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.

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

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.