ISB561 Statistical Computing

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code ISB561
Name Statistical Computing
Term 2023-2024 Academic Year
Term Fall
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. ALİ İHSAN GENÇ
Course Instructor
1


Course Goal / Objective

The aim of this course is to teach students how to do the statistical analyses based on computing tools.

Course Content

Basics of R programming, random number generating from distributions, weak law of large numbers, approximate integral, Monte Carlo methods, bootstrap and jackknife methods

Course Precondition

None

Resources

Statistical Computing with R, Maria L. Rizzo, First Edition (Chapman and Hall/CRC The R Series), 2007.

Notes

Using R for Introductory Statistics, John Verzani, Chapman and Hall/ CRC, Boca Raton, 2005.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Generates a random number from a given distribution.
LO02 Demonstrates Monte Carlo methods in statistical inference.
LO03 Computes integrals approximately.
LO04 Computes confidence intervals approximately.
LO05 Uses of the MCMC methods in point estimation.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Have in-depth theoretical and practical knowledge about Probability and Statistics 5
PLO02 Bilgi - Kuramsal, Olgusal They have the knowledge to make doctoral plans in the field of statistics. 3
PLO03 Bilgi - Kuramsal, Olgusal Has comprehensive knowledge about analysis and modeling methods used in statistics. 4
PLO04 Bilgi - Kuramsal, Olgusal Has comprehensive knowledge of methods used in statistics. 4
PLO05 Bilgi - Kuramsal, Olgusal Make scientific research on Mathematics, Probability and Statistics.
PLO06 Bilgi - Kuramsal, Olgusal Indicates statistical problems, develops methods to solve. 3
PLO07 Bilgi - Kuramsal, Olgusal Apply innovative methods to analyze statistical problems. 2
PLO08 Bilgi - Kuramsal, Olgusal Designs and applies the problems faced in the field of analytical modeling and experimental researches. 2
PLO09 Bilgi - Kuramsal, Olgusal Access to information and do research about the source.
PLO10 Bilgi - Kuramsal, Olgusal Develops solution approaches in complex situations and takes responsibility.
PLO11 Bilgi - Kuramsal, Olgusal Has the confidence to take responsibility.
PLO12 Beceriler - Bilişsel, Uygulamalı They demonstrate being aware of the new and developing practices.
PLO13 Beceriler - Bilişsel, Uygulamalı He/She constantly renews himself/herself in statistics and related fields.
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Communicate in Turkish and English verbally and in writing.
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Transmits the processes and results of their studies clearly in written and oral form in national and international environments.
PLO16 Yetkinlikler - Öğrenme Yetkinliği It considers the social, scientific and ethical values ​​in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. 4
PLO17 Yetkinlikler - Öğrenme Yetkinliği Uses the hardware and software required for statistical applications. 5


Week Plan

Week Topic Preparation Methods
1 R system, R functions, script files, packages and graphics Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
2 Random numbers generation, inverse transform method Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
3 Accept-reject method, transformation method Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
4 Multiple plots Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
5 Contour plots Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Monte Carlo integration Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 Variance reduction Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Ödev
9 Monte Carlo methods in statistical inference Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
10 Bootstrap and jackknife Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
11 Permutation tests Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
12 MCMC methods Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
13 Probability density estimation Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
14 Maximum likelihood method Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
15 EM algorithm Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
16 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Ödev
17 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Ödev


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 09.05.2023 09:02