Course Syllabus

Course Type

Must course for undergraduate students.

Course Credits

3 local credits.

Course Prerequisites


Course Description

This course is an introductory level statistics course on introducing following concepts: sampling from a normal distribution, maximum likelihood estimation, asymptotic properties of maximum likelihood estimators, confidence intervals and pivotal quantities, testing statistical hypothesis and test statistics, optimal tests, powerful tests: power function, Neyman-Pearson theorem, likelihood ratio tests, sufficient statistics, simple linear regression and correlation, analysis of variance.

Class Schedule

Section I (CRN:24163): Mondays between 08:30-11:30 at OBL3 and
Section II (CRN:24164): Thursdays between 11:30-14:30 at D-206.

Course Logistics

This is a hybrid course. For online attendees, each week, before the classes, a Zoom meeting invitation will be sent you via your ITU email to join the class on Mondays or Thursdays. Lectures will be automatically recorded and you will have a chance re-watch the videos after the class on Ninova. Lecture materials (notes, assignments, grades etc) will be uploaded on Ninova.

Course Objectives:

This course aims to:

  1. To provide the concepts of sampling and sampling distributions.

  2. To provide the applications of point estimation, confidence interval and hypothesis testing .

  3. To provide the applications of regression analysis and analysis of variance.

Course Tentative Plan

We will closely follow the weekly schedule given below. However, weekly class schedules are subject to change depending on the progress we make as a class.

Week 1. Sampling from Normal Distribution.

Week 2. Estimation: The likelihood function and maximum likelihood estimators.

Week 3. Properties of estimators: Mean-square-error, unbiasedness, consistency.

Week 4. Fisher information and efficient estimators.

Week 5. Asymptotic properties of maximum likelihood estimators.

Week 6. Confidence intervals: (1-\(\alpha\)) confidence intervals and Pivotal quantities.

Week 7. Testing statistical hypothesis and test statistics.

Week 8. Optimal Tests: Randomized tests.

Week 9. Powerful tests: power function, Simple hypothesis, Neyman-Pearson Theorem.

Week 10. Likelihood Ratio tests. Sufficient Statistics.

Week 11. ITU Spring Break

Week 12. One-sample, Two-sample and paired tests.

Week 13. Regression and correlation.

Week 14. Regression analysis: Simple linear regression.

Week 15. One-way and two-way analysis of variance.

Student Learning Outcomes

A student who completed this course successfully is expected to:

  1. Use sampling distributions,

  2. Estimation and properties of estimators,

  3. Evaluate confidence interval, testing hypothesis,

  4. Optimal tests, powerful tests,

  5. Compute regression and correlations of data,

  6. Use variance analysis,

  7. Get familiar with Python SciPy and statsmodels libraries.

immediately following the course, and/or a few months after the course.

Textbook: All lecture materials.

Off-Campus Access to the ITU Library E-sources:

Access to library e-sources remotely is possible with a library account. Users without a library account should apply for the library registration at Library register. After setting the web configurations given at Proxy only once on your computer, you will able to have an access to ITU Library e-sources.

Course Workload

2 Homework, 1 midterm exam, and 1 final exam (see the details below).

Selected Important Dates

For the official ITU Spring 2022 academic calendar, please visit:

Here are some selected important dates in Spring 2022 semester:

February 21, 2022: First day of classes.

February 21-25, 2022: Add-drop week.

April 23, 2022: National Sovereignty and Children’s Day (Saturday).

May 1, 2022: Labor and Solidarity Day (Sunday).

May 02-06, 2022: ITU Spring Break - Ramadan Feast Holiday (Monday-Friday No classes).

May 19, 2022: Commemoration of Atatürk, Youth and Sports Day (Thursday, No classes). # Section II

June 03, 2022: Last day of classes.

June 06-19, 2022: Final exam week.

I also honor other national and religious holidays. Students, who needs flexibility on individual-based studies overlapping with these special days, can inform me.

Course Policies

Please read the information below as a reference for how this class will be conducted.

Grading Policy:

Assessment Method

Contribution to Final Grade

2 Homework each


1 midterm exam


1 final exam


Midterm date: April 20, 2022 (Wednesday) at 15:30.


  • Student studies, namely, homework and exam papers which are not written well, do not follow a proper mathematical writing language, and are hard to review, will get "0" credit for that question.

  • Please read the general advice given at:

Late Submission Policy

Students are expected to do homework assignments by themselves. Homework assignments will not be accepted after deadline. There are NO make-ups for missed homework. The students who have Covid-19 medical report during the submission periods can ask for extension only.

Final Exam Attendance Policy

At least 30 points from in-semester studies (e.g., (Midterm * 0.4 + Homework 1 * 0.1 + Homework 2 * 0.1) greater than or equal to 30).

Make-Up Exam Policy

The students who miss either midterm exam or final exam due to a health problem can take a make-up exam as long as they have a valid medical report taken on the exam day. The medical report should be handed in immediately (within two days of its expiration).

Class Attendance Policy

The students must attend at least 70% of classes and are deemed responsible to manage his/her absences.

Participation Policy

The students are expected to ask and answer questions, participate in in-class activities, and show their interest and engagement in the class.

E-mail Policy


  1. Use a proper descriptive subject line (which may consist of the course number MAT244E followed by a short phrase summarizing the subject of your e-mail).

  2. Start off your e-mail with a proper greeting, introduce yourself (give your name), then state your problem as short as possible.

  3. Finally, use a proper closing and then finish your e-mail with your first name and so on.

Feel free to send me e-mails. But be sure you that give me enough time to get back to you.


  • In the past, I have had pretty much tolerance for e-mail messages sent after business hours and at weekends. But, now, due to pandemic, I should say that I may not appreciate these e-mails anymore.

  • Lastly, e-mails asking for grade grubbing at the end of the semester are not welcomed.

Academic Honesty Policy

At every stage of the academic life, every ITU student is responsible for obeying the academic honesty policy of ITU stated below:

Equity, Diversity, and Inclusion

In this class, I am committed to cultural and individual differences and diversity as including, but not limited to, age, disability, ethnicity, gender, gender identity, language, national origin, race, religion, culture, and socioeconomic status and I acknowledge the value of differences.

Student with Special Needs

I truly care about that every student in my class feels that she/he involved in this class equally. If you are a student with special needs, please, let me know that how we can adjust the course environment, materials, and course assessment methods in accordance with your needs. Furthermore, you are also invited to contact the office of students with special needs at:

ITU Distance Education Policy

Sharing the lecture recordings or its piece with third parties is strictly forbidden. Furthermore, the recordings are subject to investigation by the authorities as needed. For that reason, be sure that you behave both orally and verbally responsibly in this virtual class. Please visit:,

for more information on distance education regulations at ITU.