Undergraduate
Faculty of Economic and Administrative Sciences
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Course CodeSemester Course Name LE/RC/LA Course Type Language of Instruction ECTS
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Course Goals
 Statistics as a discipline provides a wide variety of methods to assist in data analysis and decision making. Descriptive statistics focus on the collection, summarization and characterization of set of data,Inferential statistics estimate a characteristic of a set of data. 

In general, business managers need to be knowledgeable abouth statistics in order to understand how 
properly present and desciribe information

Draw conclusion abouth large populations based only on information obtained from sample

Improve process

Obtain reliable forcasts
Prerequisite(s)
Corequisite(s)
Special Requisite(s)
Instructor(s)
Course Assistant(s)
Schedule
Office Hour(s)
Teaching Methods and Techniques
Principle Sources
Other Sources
Course Schedules
Week Contents Learning Methods
1. Week Why a Manager needs to know about statistics ,Why Data are needed? Sources of data , Types of data Lectures, computer lab., power-point presentations
2. Week Organizing numerical data, Tables and charts for numerical and categorical data, Using Microsoft Excel for Tables and Charts Lectures, computer lab., power-point presentations
3. Week The Mean , Median , Mod and other measures of central tendency Lectures, computer lab., power-point presentations
4. Week The Standart Deviation and other measures of dispersion, Using Microsoft Excel For Descriptive Statistics Lectures, computer lab., power-point presentations
5. Week Probability and its Postulates,exercises Lectures, computer lab., power-point presentations
6. Week Bayes’ Theorem , Probability distributions Lectures, computer lab., power-point presentations
7. Week Discrete Random Variables, Binomal Distribution Lectures, computer lab., power-point presentations
8. Week MIDTERM EXAM EXAM
9. Week Poisson Distribution Lectures, computer lab., power-point presentations
10. Week Continuous Random Variables and Normal Distribution Lectures, computer lab., power-point presentations
11. Week Normal Distribution and applications Lectures, computer lab., power-point presentations
12. Week Sampling Theory Lectures, computer lab., power-point presentations
13. Week Confidence Interval, Exercises Lectures, computer lab., power-point presentations
14. Week FINAL EXAM EXAM
15. Week
16. Week
17. Week
Assessments
Evaluation tools Quantity Weight(%)
Midterm(s) 1 40
Quizzes 1 10
Final Exam 1 50


Program Outcomes
PO-1Define the concept and types of entrepreneurship in historical development within the framework of entrepreneurship theory.
PO-2Develop awareness about ways to improve personal and corporate innovation and creativity
PO-3Distinguish the different aspects of SME management and its problems from SME management and its problems
PO-4Design a business plan to start a new business
PO-5Assess the institutionalization process of newly established businesses
PO-6Employ the information and skill that is related to entrepreneurship in the career life and apply it to the workplace environment.
PO-7Explain new business in social environment with social capital and communication competence
PO-8Interpret knowledge of innovation and the importance of innovation that learned during education life with up-to-date information and adopt it to business life
PO-9Identify how to reach entrepreneurship supports thanks to the basic and up-to-date information gained on entrepreneurship and estimates about the change and paradigm shifts in the entrepreneurship ecosystem.
PO-10Compose the conceptual and cognitive knowledge with expert knowledge required by business life
Learning Outcomes
LO-1Gain application skills of solving business problems
LO-2Research, practice and determine how to use the field analysis on theoretical and practical knowledge and skills gained in the field of business administration
LO-3Use of statistical tools in making forward-looking estimates
LO-4Able to obtain a good statistical knowledge of technical analysis
LO-5Being an accoutred systems analyst
Course Assessment Matrix:
Program Outcomes - Learning Outcomes Matrix
 
LO 1
LO 2
LO 3
LO 4
LO 5
LO 6
LO 7
LO 8
LO 9
LO 10
LO 11
LO 12
LO 13
LO 14
LO 15
LO 16
LO 17
LO 18
LO 19
LO 20
LO 21
LO 22
LO 23
LO 24
LO 25
LO 26
LO 27
LO 28
LO 29
LO 30
LO 31
LO 32
LO 33
LO 34
LO 35
LO 36
LO 37
LO 38
LO 39
LO 40
LO 41
LO 42
LO 43
LO 44
LO 45
LO 46
LO 47
LO 48
LO 49
LO 50