Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

# Description of Individual Course Units

 Course Unit Code Course Unit Title Type of Course Year Semester ECTS BİS613 R Programming and Statistical Data Analysis 927001 1 1 6
Level of Course Unit
Second Cycle
Objectives of the Course
Giving information about R program. Using packages for basic statistical methods, getting graphics, creating random data sets for simulation studies, hypothesis tests, linear regression analysis and statistical inferenceshics facilities are learned in the lecture also.
Name of Lecturer(s)
Learning Outcomes
2. Statistical analysis with R
3. Drawing graphics with R
4. Changing codes for different type of analysis
5. Getting new functions by writing codes
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
1. W.N. Veneables, D.M. Smithand the R Development Core Team, An Introduction to R, R project, 2008
2. Michael J. Crawley, The R Book, John Wiley & Sons Inc., 2007.
3. Joaquim P.Marques de Sa, Applied Statistics Using SPSS, STATISTICA, MATLAB and R, Springer Berlin Heidelberg, New York,2007.
4. Arslan, İ. R ile İstatistiksel Programlama, Pusula Yayıncılık, Ankara, 2015
5. An Introduction to R, Notes on R: A Programming Environment for Data Analysis and Graphics
Version 2.15.0 (2012-03-30).
Planned Learning Activities and Teaching Methods
Language of Instruction
Turkish
Work Placement(s)
None
Course Contents
Installing R program, installing main packages, principals of programming language, data management, transferring data form other programs, writing new functions on R, descriptive statistics, graphics, creating random data sets, hypothesis testing, linear regression and practices
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 1. Installing R and main packages 2. Principals of programming language – 1 (Vectors and Matrix) 3. Principals of programming language – 2 (Lists and tables) 4. Management of data sets and transferring form other programs 5. Creating random data sets on R 6. Using functions on R 7. Descriptive analysis on R 8. Introduction to graphics on R 9. Standard and advanced graphic functions 10. Theoretical distributions and creating data 11. Hypothesis testing for independent samples 12. Hypothesis testing for paired samples 13. Linear regression analysis on R 14. Practices
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight (%) Midterm Examination 1 30 Attending Lectures 14 30 Practice 14 10 Discussion 6 10 Question-Answer 6 10 Homework 2 10 SUM 730 End Of Term (or Year) Learning Activities Quantity Weight (%) Final Examination 1 100 SUM 100 Term (or Year) Learning Activities 40 End Of Term (or Year) Learning Activities 60 SUM 100