Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
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  • Department of Physical Education and Sports
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  • Course Structure Diagram with Credits
  • Statistics I
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title İST543 - Statistics I
Course Type Required Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ UMUT SEZER
Mode of Delivery Face to face
Prerequisites
Courses Recomended
Required or Recommended Resources Ott, R. L., and M. Longnecker. An introduction to statistical methods and data analysis; Hogg, R. V., Tanis, E. A., & Zimmerman, D. L. Probability and statistical inference; Navarro, D., & Foxcroft, D. Learning Statistics with JAMOVI: a tutorial for beginners in statistical analysis; Hoyle, R. H. (Ed.). Handbook of structural equation modeling; Büyüköztürk, Ş., Çokluk, Ö., & Köklü, N. Sosyal bilimler için istatistik; Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. Multivariate analysis; Güven, R., Tekindal, M. A. Temel istatistik yöntemler: SPSS ve JAMOVI paket program uygulamaları; Başol, G. (2019). Araştırmacılar için istatistik. Ankara: Pegem Akademi.
Recommended Reading List Charles Wheelan - Çıplak İstatistik, Ian Hacking - Olasılık ve Tümevarım Mantığına Giriş, Alex Reinhart - Statistics Done Wrong.
Assessment methods and criteria Midterm, Final
Work Placement
Sustainability Development Goals Quality Education

Content

Weeks Topics
Week - 1 Statistics and Statistical Thinking
Week - 2 Probability
Week - 3 Hypotheses, p-value, confidence intervals, alpha, and beta
Week - 4 Distributions and measures of central tendency
Week - 5 Data types
Week - 6 Variance, Covariance, and Correlation
Week - 7 T-test, ANOVA, MANOVA
Week - 8 Non-parametric tests
Week - 9 Regression analysis
Week - 10 Factor analysis
Week - 11 Factor analysis
Week - 12 Structural Equation Model
Week - 13 Application and practice
Week - 14 Application and practice

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Drill - Practise
  • Brain Storming
  • Report Preparation and/or Presentation
  • Competences
  • Productive
  • True to core values
  • Rational
  • Questoning
  • Entrepreneur
  • Creative
  • Follow ethical and moral rules
  • Effective use of Turkish
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Information Management
  • To work autonomously
  • Elementary computing skills
  • Decision making

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Quiz 1 20
Final Exam 1 50
Toplam (%) 100
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