Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
4BUS 204Business Statistics II3+0+03505.01.2022

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Business Administration (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course Ders, öğrencilere temel gerçekleri tam olarak anlama ve istatistikte ortak yöntemleri işletme alanında karar vermede matematiksel bir destek aracı olarak uygulama becerisi kazandırmayı amaçlamaktadır.
Course Content Bölme noktaları ve oranları, Chebyshev'in eşitsizliği, örnekleme ve örnekleme dağılımları, merkezi limit teoremi, güven aralıkları ve konum için hipotez testleri ? varyasyon ve orantı parametreleri - ANOVA, çoklu numuneler için hipotez testleri, hayır
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Görkem Erdoğan
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Basic Business Statistics, Mark L. Berenson, David M. Levine, Timothy C. Krehbiel, ISBN-10: 0132168383, ISBN-13: 9780132168380 - Business Statistics, David F. Groebner, Patrick W. Shannon, Phillip C. Fry, Kent D. Smith, ISBN-10: 0136121012, ISBN-13: 9780136121015 - Statistics for Business and Economics, David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, ISBN-10: 0538481641, ISBN-13: 9780538481649


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Assignment 1 % 10
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 16 3 48
Assignments 1 30 30
Mid-terms 1 30 30
Final examination 1 40 40
Total Work Load   Number of ECTS Credits 5 148

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Average student; - Correctly uses the language of mathematics
2 Is always aware of the validity of the carried out analysis
3 Is able to prove the mathematical basis of the carried out analysis
4 Estimates population parameters based on sample information by using theoretical probability distributions
5 Applies statistical tests about population parameters correctly
6 Correctly interprets the outcomes of analysis and comments on the validity, reliability and uncertainty of these outcomes

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Dividing ratios and points, Chebyshev?s Inequality
2 Sampling and sampling methods
3 Sampling distributions and Central Limit Theorem
4 Normal distribution and its properties
5 Confidence intervals and hypothesis testing for means ? One sample
6 Confidence intervals and hypothesis testing for variances ? One sample
7 Mid term exam
8 Confidence intervals and hypothesis testing for means ? Two samples
9 Confidence intervals and hypothesis testing for variances ? Two samples
10 Type I and Type II errors and Power of the test
11 Tests for multiple samples, ANOVA
12 Goodness of fit tests and Chisquare tests
13 Nonparametric tests
14 Nonparametric tests
15 Recitation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 2 3 3 3 2 2 2 2 4 5 4
C1 4 2 2 1 1 1 2 2 3 1 4
C2 2 1 3 3 3 4 2 4 1 2 2
C3 3 3 2 1 2 4 3 2 4 5 3
C4 2 2 3 2 3 1 1 3 2 3 1
C5 3 1 1 2 1 2 1 2 1 1 4
C6 2 5 3 3 2 2 4 3 1 2

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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