Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
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
  1. To know about programming
  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
Recommended or Required Reading
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 ActivitiesQuantityWeight (%)
Midterm Examination130
Attending Lectures1430
Practice1410
Discussion610
Question-Answer610
Homework210
SUM730
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination133
Final Examination133
Attending Lectures14342
Practice14342
Discussion6530
Question-Answer6424
Homework236
SUM150