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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB352 |
| Name | Statistical Inference |
| Term | 2020-2021 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 3-0 (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 | Doç. Dr. SELMA TOKER KUTAY |
| Course Instructor |
Doç. Dr. SELMA TOKER KUTAY
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of the course is to teach how to do hypothesis testing for parameters and estimate the parameters.
Course Content
statistics and distributions, sampling and statistics,Sampling distribution function and some related statistics, sampling density function, sampling percentage, Hypothesis testing, simple and compound hypothesis, test function, Neymann-Pearson lemma , application of Neymann-Pearson lemma, Bayes tests, power functions, UMPT, p-value are the contents of this course.
Course Precondition
Yok
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Have knowledge about finding test statistics. |
| LO02 | Learn the statistics and distributions. |
| LO03 | Have knowledge about choosing estimator for the population parameters. |
| LO04 | Learn the statistical inference for parameters |
| LO05 | Have knowledge about simple and compound hypothesis |
| LO06 | Learn to apply hypothesis testis. |
| LO07 | Learn the likelihood principle and likelihood ratio test. |
| LO08 | Learn bayes confidence intervals and approximate confidence intervals. |
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 | 4 |
| PLO02 | - | Emphasize the importance of Statistics in life | 2 |
| PLO03 | - | Define basic principles and concepts in the field of Law and Economics | 1 |
| 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 | 3 |
| PLO06 | - | Utilize computer systems and softwares | 2 |
| PLO07 | - | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | 4 |
| PLO08 | - | Apply the statistical analyze methods | 2 |
| PLO09 | - | Make statistical inference(estimation, hypothesis tests etc.) | 5 |
| PLO10 | - | Generate solutions for the problems in other disciplines by using statistical techniques | 3 |
| 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 | 2 |
| PLO13 | - | Distinguish the difference between the statistical methods | 3 |
| PLO14 | - | Be aware of the interaction between the disciplines related to statistics | 3 |
| PLO15 | - | Make oral and visual presentation for the results of statistical methods | 3 |
| 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 | 2 |
| PLO18 | - | Develop scientific and ethical values in the fields of statistics-and scientific data collection | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Statistics and distributions, sampling and statistics | Review of the literature | |
| 2 | Sampling distribution function and some related statistics, sampling density function, sampling percentage | Review of the previous lesson and the literature | |
| 3 | Skor function and fisher information. Data reductions | Review of the previous lesson and the literature | |
| 4 | Completeness, likelihood principle | Review of the previous lesson and the literature | |
| 5 | Parameter estimation methods (ML, moments, OLS, Bayes) | Review of the previous lesson and the literature | |
| 6 | Hypothesis testing, simple and compound hypothesis, test function | Review of the previous lesson and the literature | |
| 7 | Likelihood ratio test | Review of the previous lesson and the literature | |
| 8 | Mid-Term Exam | General review | |
| 9 | Neymann-Pearson lemmaı , application of Neymann-Pearson lemma | Review of the previous lesson and the literature | |
| 10 | Bayes tests, power functions, UMPT, p-value | Review of the previous lesson and the literature | |
| 11 | Application of Hypothesis testing | Review of the previous lesson and the literature | |
| 12 | Application of Hypothesis testing | Review of the previous lesson and the literature | |
| 13 | Confidence intervals, point estimation | Review of the previous lesson and the literature | |
| 14 | Bayes confidence intervals, approximate confidence intervals | Review of the previous lesson and the literature | |
| 15 | Confidence intervals using Pivot | Review of the previous lesson and the literature | |
| 16 | Term Exams | General review | |
| 17 | Term Exams | General review |
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 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 114 | ||
| Total Workload / 25 (h) | 4,56 | ||
| ECTS | 5 ECTS | ||