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. Anisa Isufi aisufi@epoka.edu.al , Thursday 9:30-11:30, E-306
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 International Marketing and Logistics Management (3 years)
Classroom and Meeting Time:	Monday: E-B32 ,12:40-14:30, Tuesday: E-211 , 8:40-10:30
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:	Learn the language and core concepts of probability theory. Understand the basics of statistical inference. Become an informed consumer of statistical information. Prepare for advanced coursework or practical applications.

BASIC CONCEPTS OF THE COURSE

1	Probability experiment - A probability experiment is a chance process that leads to well-defined results called outcomes. An outcome is the result of a single trial of a probability experiment.
2	Random variable - A random variable is a variable whose values are determined by chance
3	Parameter - A parameter is a characteristic or measure obtained by using all the data values from a specific population.
4	Discrete distributions describe the probabilities of outcomes for discrete random variables, which can take on a finite or countable number of values.
5	Continuous distributions describe the probabilities of outcomes for continuous random variables, which can take on any value within a given range.

COURSE OUTLINE

Week	Topics
1	Introductory Lecture, and Reference materials .
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.
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, + chapter 4 from Hansen's book: Joint probability and order statistics. We have introduced the concept of random vectors. We now generalize this concept to multiple random variables known as random vectors.
11	Chapter 4, + chapter 4 from Hansen's book: Joint probability and order statistics. We have introduced the concept of random vectors. We now generalize this concept to multiple random variables known as random vectors.
12	Chapter 6 Point Estimation. We will be dealing with descriptive statistics, and exploratory data analysis .
13	Project presentation.
14	Final exam review

Prerequisite(s):	Students should have a strong foundation in mathematics, and additionally, a basic knowledge of a programming language is expected.
Textbook(s):	Hogg, Tanis and Zimmerman "Probability and Statistical Inference". 9th edition.
Additional Literature:	2. 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 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.

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

No	Program Competencies	Cont.
Bachelor in International Marketing and Logistics Management (3 years) Program
1	Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics.	5
2	Apply key theories to practical problems within the global business context.	4
3	Demonstrate ethical, social, and legal responsibilities in organizations.	5
4	Develop an open minded-attitude through continuous learning and team-work.	4
5	Use technology to enable business growth and sustainability.	4
6	Synthesize creativity needed for marketing notion with scientific method and numerical skills, for achieving business sustainability.	4
7	Apply the concepts and structures of modern marketing in global context at private and public sectors.	4
8	Integrate the management of logistics, supply chain and in total operations with corporate goals and strategies.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	4	5
Midterm Exam(s)	1	20
Project	1	10
Quiz	10	1.5
Final Exam	1	35
	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

The statistics course has provided important principles and techniques for analyzing data, including both descriptive and inferential statistics. This understanding allows for making sense of data and drawing valuable conclusions about larger populations based on sample data.