Spring 2026 startup message: Lectures are beginning from the very first non-spare hour. Recitations are not beginning yet; when the time comes, your TAs will notify you. Practically everything you need will be provided on this page: examine carefully.
Lecture notes: (PDF) (Page) Section information, grades and exam seating: (221 (GSHEET)) (222 (GSHEET))
Fall 2025 Page Snapshot: (PDF)
‘Would you tell me, please, which way I ought to go from here?’ | ‘That depends a good deal on where you want to get to,’ said the Cat. | ‘I don't much care where--‘ said Alice. | ‘Then it doesn't matter which way you go,' said the Cat. | ‘--so long as I get SOMEWHERE,' Alice added as an explanation. | ‘Oh, you're sure to do that,’ said the Cat, ‘if you only walk long enough.’ | Alice’s Adventures in Wonderland, Lewis Carroll
Question: What is effective learning?
Answer: Effectiveness is the degree to which a process delivers a 'desired outcome', which is to me to see my students as high-caliber members of the professional and academic community, even more rewardingly to see my former students as people bringing positive change to the society.
Question: What is effective learning from the perspective of a student?
Answer: That depends a good deal on where you want to get to. See the previous Q/A to form your personal answer.
Question: Should I attend each class hour?
Answer: There is a strong association between performance and attendance.
Question: So, showing up in class is enough, right?
Answer: “To attend” does not only mean “to show up”, it rather has a connotation with “to come to class prepared and to participate”.
Question: Is classroom a boring environment?
Answer: No, as longs as everyone behaves with basic politeness and civility. An informal atmosphere is acceptable as long as it is orderly.
Question: I have a good memory; can I rely on it?
Answer: Memorization has some role in triggering your learning, but it should eventually be replaced with a full command of the subject matter. I usually refer to a revised version of Benjamin Bloom's work (Bloom's Taxonomy of Educational Objective) with the addition of the psycho-motor domain (as developed by Harrow, 1972) to view the cognitive domain: "
Knowledge: recognizing or recalling information (~memorization, something so much of a short-term)
Comprehension: organizing and arranging material mentally; grasping a topic; collecting together a battery of information and ideas
Application: applying the previously learned to reach an answer
Analysis: thinking critically and in depth; solving problems
Synthesis: thinking creatively to present something original; producing original communications, making predictions and solving problems
Evaluation: judging the value or validity of an idea, a solution to a problem, or an aesthetic work; offering an opinion on an issue through objective criteria, standards or personal values "
Based on this, every course has a pre-designed target complexity over the Knowledge-to-Evaluation spectrum. ECON 221 reaches mostly up to Application and partly Analysis, while ECON 222 reaches up to Synthesis and Evaluation.
Question: How long should I study?
Answer: See the previous Q/A.
Question: What gear do I need?
Answer: Each student is suggested to have a 'traditional notebook + pencil' or 'tablet with notetaking capacity' and a calculator for exams.
Question: I will never cheat. But what happens to cheaters?
Answer: Cheating ~ plagiarism is unfair and may be penalized at a minimum with failing the course and at a maximum with suspension.
Question: Is there an AI policy?
Answer: ECON 221 and ECON 222 do not employ AI in teaching. However, students may assist themselves with AI while they are studying the subject matter. The Homework descriptions will specifically inform you on whether to use AI. See also the previous Q/A.
Question: What are the exam rules? How can I learn about the exam logistics?
Answer: See the very end of this page.
Question: When is Office Hours?
Answer: Office hours are the time slots dedicated to answer the students' course-related questions and other queries; students may come to A-108 without appointment, except for the first week of classes & the final exams period. Office hours in Spring 2026 are to be held every Wednesday from 11:00 to 13:00.
Question: OK. I'm ready to go for it. Where can I find the lecture material?
Answer: PDF version of Lecture Notes is available at Lecture notes (PDF) and HTML version is at Lecture notes (Page). Also, the previous semesters' materials are available at Past materials (Folder). Also, Statistical distributions, Confidence intervals and Hypothesis testing practice materials are at cx and cx.xlsx Selected distribution tables are at z (PDF, PNG) t (PDF, PNG) Chi2 (PDF, PNG) F (PDF), and Microsoft Excel function reference is at Excel (Page). You don't need to return to this point every time, these links are repeatedly given below when needed.
Question: How are the official announcements made?
Answer: Via emails sent by the teacher and TAs through STARS/AIRS/SRS platform; also, through the section-based WhatsApp channels of TAs.
Question: What are the topics to be covered? How should I proceed now?
Answer: Simply bookmark this page in your favorite browser & use it & come to classes regularly.
Excel (Page)
1.3 Frequency
010 Microsoft Excel basics - preparing tables and entering formulas.xlsx
020 Preparing histograms using COUNTIF to describe data.xlsx
1.4 Representation of distributions
1.5 Measures of central tendency 221 is here
Self study Income distribution in Türkiye
221 Handout 240214 - Mean - Variance - Covariance.pdf
030 Use of PERCENTILEs to describe data.xlsx
1.7 Measures of association for bivariate data
1.9 Chebyshev’s theorem (Chebyshev’s inequality)
1.10 Adding and multiplying terms over an index
040 Generating random numbers - Linear Cogruential Generator - Using the digits of Pi.xlsx
050 Describing data via histograms to reveal Global warming using COUNTIFS.xlsx
221 Hw1 due DD MMM 2026 DDD HH:MM [Task Description] Print your report to file in PDF format. Finalize your Excel file (just one) used in analysis so that it will be self-explanatory and it will include everthing done. Your files must be named as: 221-Hw1-11111111.pdf and 221-Hw1-11111111.xlsx, where 11111111 is your ID number. Not naming your files as described will result in the rejection of your submission. Submit both files in the same email message as 'file attachments' (not 'cloud-based links') to your TA's email address with proper salutation and body text. Upon receiving your submission, your TA will send you a message including the word "Received". Grading: No submission=0 points, Late submission=15 points deduction. Other deductions will be announced by the TA team. Note that Homework performance makes up 20%of your semester total; so, sharing your work in any form with other students may harm your individual letter grade assessment. Unacceptably high similarity between submissions will yield a grade of 0 points as well as possible disciplinary investigation. Unless otherwise noted, these rules apply to all Homework assignments.
Excel (Page)
2.1 Modeling a Random Experiment
2.2 Properties of a probability measure: Probability postulates
2.3 Probability versus possibility
2.4 Methods of assigning probability
2.5 Counting
221 Handout 240219 - Exercises on Probability basics.pdf
2.7 Bayes’ Theorem
2.10 Joint, marginal and conditional probabilities
221 Handout 240305 - Conditional probability - Independence of events.pdf
060 Preparing Cross Tabulations using PIVOT TABLE and calculating Conditional probabilities.xlsx
070 Describing data via PIVOT TABLEs to reveal Global warming.xlsx
cx cx.xlsx z (PDF, PNG) Excel (Page)
3.1 Random Variables
3.2 Cumulative distribution function: CDF
3.3 Continuous and discrete random variables
3.4 Probability distribution functions
3.5 Expected Value
221 Handout 240426 - Exercises on PDF - CDF - Expected value.pdf
3.6 Variance and standard deviation
3.7 Random variables and distributions: Discrete probability laws
3.8 Random variables and distributions: Continuous probability laws
221 Handout 241217 - Approximations to Binomial - CT - LOLN - CLT.pdf
080 Generating random numbers using RAND and the Inverse Transformation Technique - 1.xlsx
090 Generating random numbers using RAND and the Inverse Transformation Technique - 2.xlsx
100 Solving a Bayes theorem question via Simulation.xlsx
110 Conditional probabilities Statistical Independence - an exercise on student performance.xlsx
120 Simulating the Geometric distribution.xlsx
130 Simulating the Negative Binomial distribution.xlsx
170 Inverse Transformation Technique for a semicircularly shaped distribution .xlsx
180 Simulating a Galton Board.xlsx
190 Simulating an asymmetric Galton Board.xlsx
200 Calculating Expected Value via simulation - Miner question.xlsx
210 Calculating Expected Value via simulation - an advanced exercise.xlsx
220 Estimating pandemic-epidemic probabilities for the future - an advanced exercise.xlsx
230 Poisson arrivals via Exponential interarrival times and the Law of Large Numbers theorem.xlsx
3.9 Random variables and distributions: Moments of distributions [Optional material]
3.10 Moment generating functions [Optional material]
3.11 Random vectors [Optional material]
cx cx.xlsx z (PDF, PNG) Excel (Page)
4.2 Law of large numbers theorem
4.4 Distribution of sample means
240 Sampling from a finite population using RAND-INDEX-RANK combination.xlsx
222 Hw1 due DD MMM 2026 DDD HH:MM [Task Description] Once your work is done, annotate your Excel file with text boxes & make all necessary explanations therein: what you did, how you did & why you did must all be crystal-clear. Your file (just one) must be named as: 222-Hw1-11111111.xlsx, where 11111111 is your ID number. Not naming your file as described will result in the rejection of your submission. Submit the file in an email message as a 'file attachment' (not a 'cloud-based link') to your TA's email address with proper salutation and body text. Upon receiving your submission, your TA will send you a message including the word "Received". Grading: No submission=0 points, Late submission=15 points deduction. Other deductions will be announced by the TA team. Note that Homework performance makes up 20%of your semester total; so, sharing your work in any form with other students may harm your individual letter grade assessment. Unacceptably high similarity between submissions will yield a grade of 0 points as well as possible disciplinary investigation. Unless otherwise noted, these rules apply to all Homework assignments.
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cx cx.xlsx z (PDF, PNG) Excel (Page)
5 Point estimators 222 is here
5.1 Point estimation
5.2 Least squares technique: LS
5.3 Maximum likelihood technique: ML
5.4 Method of moments technique: MM
250 Sampling via RAND-INDEX-RANK in the German tank problem.xlsx
cx cx.xlsx z (PDF, PNG) t (PDF, PNG) Chi2 (PDF, PNG) Excel (Page)
6.1 Confidence interval estimation: One population
6.2 Finite populations and correction
6.4 Confidence interval estimation: Two populations
222 Handout 240221 - Confidence interval estimation 1.pdf
222 Handout 240226 - Confidence interval estimation 2.pdf
260 Simulating the voting behavior in a small town to understand Population Proportion.xlsx
280 Confidence intervals to reveal Global warming.xlsx
320 Assessing the forecast performance via Confidence intervals.xlsx
330 Limit of Chi-square(m) by m is 1 as m tends to infinity - via simulation.xlsx
cx cx.xlsx z (PDF, PNG) t (PDF, PNG) Chi2 (PDF, PNG) Excel (Page)
7.1 Hypothesis testing: One population
222 Handout 240311 - Hypothesis testing.pdf
7.2 Hypothesis testing: Two populations
222 Handout 240312 - Two population procedures.pdf
7.3 p-value
7.4 Type I and Type II errors and the Power of a hypothesis test
222 Handout 240325 - p-value and Power.pdf
340 Assessing the forecast performance via Hypothesis testing.xlsx
360 ANOVA - Analysis of Variance.xlsx
cx cx.xlsx z (PDF, PNG) t (PDF, PNG) Chi2 (PDF, PNG) F (PDF) Excel (Page)
8.2 Transformations and functional forms
8.3 Our approach to teaching/learning
8.4 Building and estimating an Unconditional Model of Mean: A model which is a non-model
8.5 Building and estimating a Simple Linear Regression model
8.6 Building and estimating a Multiple Linear Regression model: An increase in dimensionality
8.7 Goodness of fit
222 Handout 241213 - For inference exercises.pdf
222 Handout 251222 - Christmas Special Handout.pdf
8.10 Essence of the Gauss-Markov assumptions
222 Handout 241217 - MLR model summary.pdf
8.11 Model Specification
cx cx.xlsx z (PDF, PNG) t (PDF, PNG) Chi2 (PDF, PNG) F (PDF) Excel (Page)
Some dedicated Statistical/Econometric software and other software platforms that support Statistical/Econometric computations are:
EViews Stata R & RStudio Python Matlab JASP Minitab SPSS
8.12 Regression analysis at work
222 Handout 240506 - Regression analysis at work.pdf
Case 01 State public expenditures in the US: A public finance model (Economics)
Data reference: U.S. Department of Commerce, Bureau of the Census, Government Finances in 1960, Census of Population, 1960, Census of Manufactures, 1958, Statistical Abstract of the United States, 1961. U.S. Department of Agriculture, Agricultural Statistics, 1961. U.S. Department of the Interior, Minerals Yearbook, 1960. Authorization: for educational use.
case01.csv case01.R case01.wf1 case01.xlsx
Case 02 Home prices in Albuquerque: what determines home prices? (Economics, Real estate, Business)
Data reference: Albuquerque Board of Realtors. Authorization: for educational use.
case02.csv case02.R case02.wf1 case02.xlsx
Case 03 Taste of cheese: An assessment of subjective scores (Product development, Business)
Data reference: Moore, David S., and George P. McCabe (1989). Introduction to the Practice of Statistics. Authorization: for educational use.
case03.csv case03.R case03.wf1 case03.xlsx
Case 04 Consumption of soft drinks: Practicing categorical determinants (Consumer research). Data reference: Artificial data - Eray Yucel. Authorization: for educational use.
case04.csv case04.R case04.wf1 case04.xlsx
370 Multiple Linear Regression modeling - Analyzing demand for soft drinks.xlsx
Case 05 A promotion for soda consumers: The Linear Probability Model - simple and still useful (Business). Data reference: Artificial data - Eray Yucel. Authorization: for educational use.
case05.csv case05.R case05.wf1 case05.xlsx
Case 06 A demonstration of the effect of omitted variables (Simpson’s paradox). Data reference: Artificial data - Eray Yucel. Authorization: for educational use.
case06.csv case06.R case06.wf1 case06.xlsx
8.13 Frisch-Waugh-Lovell theorem (FWL theorem)
222 Handout 240508 - FWL theorem.pdf
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Exam Rules and Logistics
STUDENTS are responsible for every topic covered in class & recitations up to and including the last lecture/recitation prior to exam. Some previous exams with solution keys are available at Past materials (Folder).
PROCTORS are expected in FEASS A-108 15 minutes before the exam's start. Regarding the exchange of posts among PROCTORS, make sure Ms. Özlem Eraslan has been informed in a timely fashion. On the exam day only, directly call +905325435888 for emergencies.
STUDENTS are not to be admitted to exam without their STUDENT ID card or a live display of their 'SRS page + Citizen ID card / Passport'.
PROCTORS: Exam dates and times are as follows; the STUDENTS are to be seated according to 'Section information, grades and exam seating' files.
ECON 221 Midterm 1: 28 February 2026 Saturday 09:30 - 11:30
ECON 221 Midterm 2: 04 April 2026 Saturday 09:30 - 11:30
ECON 221 Final: Check the Faculty announcements
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ECON 222 Midterm 1: 01 March 2026 Sunday 09:30 - 11:30
ECON 222 Midterm 2: 05 April 2026 Sunday 09:30 - 11:30
ECON 222 Final: Check the Faculty announcements
Exam duration is 75 minutes for Midterms & 105 minutes for Finals. STUDENTS are not allowed to leave the room in the first 30 minutes and not to be admitted after that.
There is no restroom allowance during the exam, except for the students with officially documented (by a Medical Committee ~ Tur: Heyet Raporu) chronic medical conditions. STUDENTS can leave only after they submit their work.
Books and notes are not allowed. Each STUDENT can use a formula sheet: the updated formula sheet policy will be announced here soon
STUDENTS' use of standalone calculators is allowed. Use of other devices is not allowed.
Exchange of calculators, stationery and formula sheets between STUDENTS is not allowed.
STUDENTS will use pencils & erasers but not ink pens or roller pens. So, no scratching etc. is to be left on exam papers.
STUDENTS are not to use the back sides of exam sheets, responses on back sides are not to be graded, as back sides are not numbered, i.e., they are not pages.
STUDENTS are not to remove the staples of exam sets under any circumstances.
STUDENTS will not ask any questions during the exam. PROCTORS will not answer STUDENTS' questions of any sort. Every single word & term in the questions have been covered in the lectures. So, when the questions from STUDENTS like "I don't know the meaning of this word" are answered by PROCTORS, the answer may coincide with the response/solution seeked by the exam question, where correspondences as such may have legal consequences.
STUDENTS are not to leave the exam room without submitting their work and signing the attendance list.
By the end of the exam, PROCTORS are to dispose the unused exam sets, are to make sure that number of people on the attendance list and the number of exam papers submitted match. Upon the submission of exam papers and attendance lists to FEASS A-108 the exam-related duties end for PROCTORS.
Exam results are to be announced in two weeks' time after the exam date. Until then, no correspondences from STUDENTS regarding the exam are expected.