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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 | |
| 2 | Random numbers generation, inverse transform method | Source reading | |
| 3 | Accept-reject method, transformation method | Source reading | |
| 4 | Multiple plots | Source reading | |
| 5 | Contour plots | Source reading | |
| 6 | Monte Carlo integration | Source reading | |
| 7 | Variance reduction | Source reading | |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Monte Carlo methods in statistical inference | Source reading | |
| 10 | Bootstrap and jackknife | Source reading | |
| 11 | Permutation tests | Source reading | |
| 12 | MCMC methods | Source reading | |
| 13 | Probability density estimation | Source reading | |
| 14 | Maximum likelihood method | Source reading | |
| 15 | EM algorithm | 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 |
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 | ||