ISB351 Computational Statistics

5 ECTS - 2-2 Duration (T+A)- 5. Semester- 3 National Credit

Information

Unit FACULTY OF SCIENCE AND LETTERS
STATISTICS PR.
Code ISB351
Name Computational Statistics
Term 2020-2021 Academic Year
Semester 5. Semester
Duration (T+A) 2-2 (T-A) (17 Week)
ECTS 5 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Lisans Dersi
Type Normal
Label C Compulsory
Mode of study Uzaktan Öğretim
Catalog Information Coordinator Prof. Dr. ALİ İHSAN GENÇ
Course Instructor Prof. Dr. ALİ İHSAN GENÇ (Güz) (A Group) (Ins. in Charge)


Course Goal / Objective

This course aims that students do the statistical analyses with a computer program.

Course Content

Starting with the basics of a program, the exploratory data analysis and statistical inference methods are studied.

Course Precondition

Yok

Resources

Notes



Course Learning Outcomes

Order Course Learning Outcomes
LO01 Comprehends the basics of a statistical package, for instance R.
LO02 Plots univariate data.
LO03 Plots bivariate data.
LO04 Comprehends the properties of specific distributions.
LO05 Perfoms computer simulations.
LO06 Computes probabilities using a computer.
LO07 Finds confidence intervals.
LO08 Does the hypotheses tests.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 - Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 1
PLO02 - Emphasize the importance of Statistics in life 4
PLO03 - Define basic principles and concepts in the field of Law and Economics 0
PLO04 - Produce numeric and statistical solutions in order to overcome the problems 3
PLO05 - Use proper methods and techniques to gather and/or to arrange the data 5
PLO06 - Utilize computer systems and softwares 5
PLO07 - Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 5
PLO08 - Apply the statistical analyze methods 4
PLO09 - Make statistical inference(estimation, hypothesis tests etc.) 4
PLO10 - Generate solutions for the problems in other disciplines by using statistical techniques 4
PLO11 - Discover the visual, database and web programming techniques and posses the ability of writing programme 0
PLO12 - Construct a model and analyze it by using statistical packages 5
PLO13 - Distinguish the difference between the statistical methods 4
PLO14 - Be aware of the interaction between the disciplines related to statistics 2
PLO15 - Make oral and visual presentation for the results of statistical methods 4
PLO16 - Have capability on effective and productive work in a group and individually 3
PLO17 - Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 3
PLO18 - Develop scientific and ethical values in the fields of statistics-and scientific data collection 4


Week Plan

Week Topic Preparation Methods
1 Data types, program basics Source reading
2 Program basics Source reading
3 Univariate data, categorical data, contingency tables Source reading
4 Graphs for categorical data, barplots, pie charts Source reading
5 Summarization of a numerical data, mean, variance, mode, median Source reading
6 Numerical data plots, histogram, stem-leaf plots Source reading
7 Boxplots, standardization of data Source reading
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources
9 Simulation Source reading
10 Normal distribution and some other distributions Source reading
11 Regression and probability plots Source reading
12 Confidence intervals Source reading
13 Confidence intervals Source reading
14 Hypothesis tests Source reading
15 Hypothesis tests Source reading
16 Term Exams Review the topics discussed in the lecture notes and sources
17 Term Exams Review the topics discussed in the lecture notes and sources


Assessment (Exam) Methods and Criteria

Assessment Type Midterm / Year Impact End of Term / End of Year Impact
1. Midterm Exam 100 40
General Assessment
Midterm / Year Total 100 40
1. Final Exam - 60
Grand Total - 100


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 4 56
Out of Class Study (Preliminary Work, Practice) 14 3 42
Assesment Related Works
Homeworks, Projects, Others 1 3 3
Mid-term Exams (Written, Oral, etc.) 1 8 8
Final Exam 1 16 16
Total Workload (Hour) 125
Total Workload / 25 (h) 5,00
ECTS 5 ECTS

Update Time: 29.04.2025 02:18