EPOKA UNIVERSITY
FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
DEPARTMENT OF BUSINESS ADMINISTRATION
COURSE SYLLABUS
20232024 ACADEMIC YEAR
COURSE INFORMATIONCourse 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)  NA 
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:  M.Sc. Dafina Muda dshehi@epoka.edu.al , Friday 9:3012:00 
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:  NA 
Teaching Assistant(s) 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:  Tuesday 08:4010:30 and Wednesday 11:40 13:30 
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:  The major objective of this course is to increase the student data analysis skills using different statistical tools, both from descriptive and inferential statistics. 
BASIC CONCEPTS OF THE COURSE

1  Variable  A variable is a characteristic or attribute that can assume different values. 
2  Population  A population consists of all subjects (human or otherwise) that are being studied 
3  Inferential statistics  Inferential statistics consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions. 
4  Frequency distribution  A frequency distribution is the organization of raw data in table form, using classes and frequencies. 
5  Parameter  A parameter is a characteristic or measure obtained by using all the data values from a specific population. 
6  Probability experiment  A probability experiment is a chance process that leads to welldefined results called outcomes. An outcome is the result of a single trial of a probability experiment. 
7  Histogram  Histogram a graph that displays the data by using vertical bars of various heights to represent the frequencies of a distribution. 
8  Random variable  A random variable is a variable whose values are determined by chance. 
9  Normal distribution If a random variable has a probability distribution whose graph is continuous, bellshaped, and symmetric, it is called a normal distribution. The graph is called a normal distribution curve. 
10  Central limit theorem central limit theorem a theorem that states that as the sample size increases, the shape of the distribution of the sample means taken from the population with mean 
COURSE OUTLINE

Week  Topics 
1  Introduction to syllabus 
2  Chapter 1. The nature of probability and statistics. 1.1Descriptive and Inferential Statistics pg 15. 1.2 Variables and Types of Data pg 68 1.3Data Collection and Sampling Techniques pg 913 1.4 Observational and Experimental Studies pg.1316 
3  Chapter 2. Frequency distribution and graphs. 2.1 Organizing Data Pg.3650 
4  Chapter 2. Frequency distribution and graphs. 2.2 Histograms, Frequency Polygons, and Ogives Pg.5168 2.3 Other Types of Graphs Pg.68101 
5  Chapter 3 . Data description. 3.3Measures of central tendency Pg. 104123 3.4 Measures of Variation Pg.123142 
6  Chapter 3. Data description. 3.4 Measures position. Pg.142162 3.5 Exploratory Data Analysis Pg.162180 
7  Chapter 4. Probability and counting rules 4.1 Sample Spaces and Probability Pg.182199 4.2 The Addition Rules for Probability Pg.199211 
8  Midterm 
9  Chapter 4. Probability and counting rules 4.3 The Multiplication Rules and Conditional Probability Pg.211224 4.4 Counting Rules Pg.224237 4.5Probability and Counting Rules Pg.237250 
10  Chapter 5. Discrete Probability Distributions. 5.1 Probability Distributions Pg. 252259 5.2. Mean, Variance, Standard Deviation, and Expectation Pg.259270 
11  Chapter 5. Discrete Probability Distributions 5.3 Binomial distribution Pg. 270283 5.4 Other types of probability distribution Pg.270295 
12  Chapter 6. The normal distribution 6.1 Normal distribution Pg300316 6.2 Applications of normal distribution Pg. 316330 
13  Chapter 6. The normal distribution 6.3 The Central Limit Theorem Pg. 331340 
14  Project presentation and exam review 
Prerequisite(s):  NA 
Textbook(s):  Allan G. Bluman (2017) Elementary Statistics , 10th ed., McGraw Hill 
Additional Literature:  NA 
Laboratory Work:  Yes 
Computer Usage:  Excel 
Others:  No 
COURSE LEARNING OUTCOMES

1  Descriptive statistics 
2  Inferential statistics 
3  Types of probabilities 
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.  4 
4  Develop an open mindedattitude through continuous learning and teamwork.  4 
5  Use technology to enable business growth and sustainability.  5 
6  Synthesize creativity needed for marketing notion with scientific method and numerical skills, for achieving business sustainability.  3 
7  Apply the concepts and structures of modern marketing in global context at private and public sectors.  3 
8  Integrate the management of logistics, supply chain and in total operations with corporate goals and strategies.  3 
COURSE EVALUATION METHOD

Method  Quantity  Percentage 
Midterm Exam(s) 
1

35

Project 
1

15

Final Exam 
1

40

Attendance 
10


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  3  48 
Hours for offtheclassroom study (Prestudy, practice)  2  20  40 
Midterms  1  15  15 
Assignments  1  7  7 
Final examination  1  15  15 
Other  0  
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 fundamental principles and techniques of statistical analysis, include descriptive and inferential statistics. We have learned visualizing the data and decide the best statistical way to interpret and analyse them, take decisions, and make predictions. We have explored the concept of statistical inference, which allows us to draw conclusions about populations based on sample data. By mastering these concepts and techniques, we are able to make sense of data, draw meaningful conclusions about populations based on sample data. 