COURSE INFORMATION Course Title: STATISTICS I

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BUS 201	A	3	4	0	0	4	5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	Assoc.Prof.Dr. Nargiza Alymkulova nalymkulova@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Egla Mansi emansi@epoka.edu.al , Monday 9:00 AM
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Bachelor in Business Informatics (3 years)
Classroom and Meeting Time:	check timetable
Teaching Assistant(s) and Office Hours:	NA
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	75%
Course Description:	Statistics I: The aim of the courses is that inference making in Business. The objective of the course is to help students to understand theoretical characteristics of statistical methods and develop practical knowledge and skills to analyze the business data.
Course Objectives:	This course aims to introduce students to the foundational language and core concepts of probability theory, equipping them with essential skills for understanding the behavior of random variables and uncertainty. Students will also explore the basic principles of statistical inference, covering both Bayesian and frequentist approaches, and develop a starter statistical toolbox for practical application. By using software and simulations in R, students will not only perform data analysis but also critically evaluate the utility and limitations of these techniques. Additionally, the course prepares students to become informed consumers of statistical information, ready for advanced coursework or real-world applications in their careers.

BASIC CONCEPTS OF THE COURSE

1	Bayes Theorem
2	probability
3	continuous and discrete distribution
4	mean, variance, covariance
5	joint distribution
6	Resampling Methods
7	order statistics
8	R studio
9	random variable

COURSE OUTLINE

Week	Topics
1	Introductory Lecture, Reference materials and intro to programming
2	Chapter 1 "Probability": In this week we go into detail about basic probability theory where we will cover the properties of probability. Followed by methods of enumeration where in this section, we develop counting techniques that are useful in determining the number of outcomes associated with the events of certain random experiments. We begin with a consideration of the multiplication principle.
3	Chapter 1 "Probability": In this week we will discuss Conditional Probability and Bayes Rule. We will discuss their properties, talk about in/dependent events and mutual events. Lastly we will touch the theory on Bayes and where/how can we use it in real life or applications.
4	Chapter 2 "Discrete Distributions": Set up and work with discrete random variables. In particular, understand the Bernoulli, binomial, geometric and Poisson distributions.
5	Chapter 2 "Discrete Distributions": Set up and work with discrete random variables. In particular, understand the Bernoulli, binomial, geometric and Poisson distributions.
6	Chapter 3 "Continuous Distribution": Work with continuous random variables. In particular, know the properties of uniform, normal and exponential distributions.
7	Chapter 3 "Continuous Distribution": Work with continuous random variables. In particular, know the properties of uniform, normal and exponential distributions.
8	Chapter 3 "Continuous Distribution": Work with continuous random variables. In particular, know the properties of uniform, normal, exponential and other types of distributions.
9	Midterm
10	Chapter 4 + additional handouts + chapter 4 from Hansen's book: Joint probability and order statistics. In Chapter 2 we introduced the concept of random vectors. We now generalize this concept to multiple random variables known as random vectors. To make the distinction clear we will refer to one- dimensional random variables as univariate, two-dimensional random pairs as bivariate, and vectors of arbitrary dimension as multivariate.
11	Chapter 5 + additional handouts: Random Variables: In probability theory we studied the properties of random vectors X . In statistical theory we extend to the setting where there are a collection of such random vectors. The simplest such setting is when random vectors are mutually independent and have the same distribution. In this chapter we introduce laws of large numbers and associated continuous mapping theorem. we extend asymptotic theory to the next level and obtain asymptotic approximations to the distributions of sample averages.
12	Chapter 5 + additional handouts: Random Variables: In probability theory we studied the properties of random vectors X . In statistical theory we extend to the setting where there are a collection of such random vectors. The simplest such setting is when random vectors are mutually independent and have the same distribution. In this chapter we introduce laws of large numbers and associated continuous mapping theorem. we extend asymptotic theory to the next level and obtain asymptotic approximations to the distributions of sample averages.
13	Project Presentation
14	Project Presentation + Review

Prerequisite(s):	Students should be familiar with integral, differential, and multivariate calculus and linear matrix algebra. Also, basic knowledge of a programming language.
Textbook(s):	Hogg, Tanis and Zimmerman (2021) "Probability and Statistical Inference". 10th edition.
Additional Literature:	2. Bruce Hansen's "Introduction to Econometrics" 3. Occasional Handouts 4. Casella and Berger's "Statistical Inference"
Laboratory Work:
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Use basic counting techniques (multiplication rule, combinations, permutations) to compute probability and odds.
2	Compute conditional probabilities directly and using Bayes’ theorem, and check for independence of events.
3	Set up and work with discrete random variables. In particular, understand the Bernoulli, binomial, geometric and Poisson distributions.
4	Know what expectation and variance mean and be able to compute them.
5	Use software and simulation to do statistics (R).

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Bachelor in Business Informatics (3 years) Program
1	Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics.	2
2	Apply key theories to practical problems within the global business context.	2
3	Demonstrate ethical, social, and legal responsibilities in organizations.	1
4	Develop an open minded-attitude through continuous learning and team-work.	5
5	Integrate different skills and approaches to be used in decision making and data management.	5
6	Combine computer skills with managerial skills, in the analysis of large amounts of data.	5
7	Provide solutions to complex information technology problems.	3
8	Recognize, analyze, and suggest various types of information-communication systems/services that are encountered in everyday life and in the business world.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Project	1	30
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	4	64
Hours for off-the-classroom study (Pre-study, practice)	16	2	32
Mid-terms	1	6	6
Assignments	4		0
Final examination	1	10	10
Other	1	13	13
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER

If a student has a misbehavior report on exams then automatically that student gets zero points for that exam. The same rule goes if the projects they submit have high plagiarism