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
  • Faculty of Science
  • Department of Statistics
  • Course Structure Diagram with Credits
  • Statistics I
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title İST129 - Statistics I
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 4+0
ECTS 6.0
Course Instructor(s) PROFESÖR BERNA YAZICI
Mode of Delivery FACE TO FACE
Prerequisites NONE
Courses Recomended NONE
Recommended Reading List * McClave, J.T. and Sincich, T. (2013). Statistics, Pearson Education.* Devore, J. L. (2011). Probability and Statistics for Engineering and the Sciences (Eigth edition). Cengage learning.* Johnson, R. A. and Bhattacharyya, G.K. (2009). Statistics: principles and methods (Sixth edition). John Wiley \\& Sons.* Moore, D. S., McCabe, G. P. and Craig, B. A. (2012). Introduction to the Practice of Statistics.* David R. Anderson, Dennis J. Sweeney and Thomas A. Williams, Statistics for Business and Economics, 11th Edition.* Peck, R., Olsen, C. and Devore, J. L. (2015). Introduction to statistics and data analysis. Cengage Learning.* Newbold, P., Carlson, W. and Thorne, B. (2013). Statistics for business and economics. Pearson.
Assessment methods and criteria MIDTERMPROJECTQUIZFINAL
Work Placement NONE
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Basic notions of the statistics: Population, sample, data set, experimental unit, variable.
Week - 2 Basic notions of statistics: Types of variable, scale of measurements, data collection methods.
Week - 3 Grapgical display of the data.
Week - 4 Location measures.
Week - 5 Measures for variability.
Week - 6 Quantiles of the data, interquartile range, box plot.
Week - 7 Basics of probability.
Week - 8 Conditional probability and independency.
Week - 9 Notion of discrete distribution, probability distribution, expectation and variance of a discrete distribution.
Week - 10 Binomial and Poisson distributions.
Week - 11 Notion of continuous distribution, cumulative distribution function, probability calculation for continuous distribution.
Week - 12 Standard normal distribution and z table.
Week - 13 Normal distribution and its applications.
Week - 14 Normal distribution approximation to Binomial distribution.

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Demonstration
  • Competences
  • Questoning
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Applying theoretical knowledge into practice
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 20
2.Midterm Exam 1 20
Final Exam 1 60
Toplam (%) 100
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