UNDER MAINTENANCE UNTIL 22 JANUARY 2026
UNDER MAINTENANCE UNTIL 22 JANUARY 2026
Lecture notes: (PDF) (Page) Section information and grades: (221 (GSHEET)) (222 (GSHEET))
‘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 upto Application and partly Analysis, while ECON 222 reaches upto 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 ------- from ------- to -------.
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 practice file (221 and 222) is at SD (XLSX), Confidence intervals practice file (222) is at CI (XLSX), Hypothesis testing practice file (222) is at HT (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 221 Rec 1
020 Preparing histograms using COUNTIF to describe data.xlsx 221 Rec 1
1.4 Representation of distributions
1.5 Measures of central tendency
Self study Income distribution in Türkiye
221 Handout 240214 - Mean - Variance - Covariance.pdf 221 Rec 2 Brief review of handout
030 Use of PERCENTILEs to describe data.xlsx 221 Rec 2
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 221 Rec 3
050 Describing data via histograms to reveal Global warming using COUNTIFS.xlsx 221 Rec 4
221 Hw1 due 20 October 2025 Monday 13:30 Go to Income distribution in Türkiye which was referenced above as Self study before. Examine the page including all its links and data tables. Your task is to prepare a report on 'how fair the income distribution in Türkiye is', i.e., whether it is fair; if not, what makes you to conclude it isn't. In that, first learn on your own the terms 'fair', 'just', 'equal' and 'equitable' referring to economics textbooks and/or AI of your choice. Your quantitative assessment must be based on what you've learned in Chapter 1, where your knowledge therein should enable you to learn new things you'll encounter, so, do your research. Once your analysis is done, prepare a research note (a short paper) of 1500 to 2000 words that includes all of the following: 'Title', 'Abstract (100-120 words)', 'Keywords', 'Introduction', 'Literature Review', 'Methodology (refer to Lecture Notes)', 'Empirical Analysis', 'Discussion and Conclusion', 'References (a max of 5)'. Your paper will not have an Appendix or Appendices, the number of footnotes can be a maximum of 5 and endnotes are not allowed. Use of AI in writing is not suggested; write your work on your own. Regarding the format of your paper (format elements: fonts, spacing; captions: enumeration and titling of visuals; layout elements: page structure and enumeration, etc.), briefly review the published empirical papers of the faculty members of your department and maintain the format of one of those published after 2020. If you are not from ECON or MAN, refer to ECON faculty's papers. Cite this guiding paper in your References. 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 Bayes' Theorem not included in 221 Mt1 so expect a question in 221 Mt2
2.10 Joint, marginal and conditional probabilities
2.11 Independence of Events 221 Mt1 up to here except for Bayes' Theorem
221 Rec 5 Mt1 solutions to be covered Past materials (Folder)
221 Handout 240305 - Conditional probability - Independence of events.pdf
060 Preparing Cross Tabulations using PIVOT TABLE and calculating Conditional probabilities.xlsx 221 Rec 6
070 Describing data via PIVOT TABLEs to reveal Global warming.xlsx 221 Rec 6
SD (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
221 Hw2 due 26 November 2025 Wednesday 13:30 Examine & think in-depth on the following questions: (1) What is randomness? In that, what do the terms 'random' and 'stochastic' stand for? Add other terms if you think it is necessary. (2) Is it possible to generate 'purely random' numbers? If no, why; if yes, how? (3) In many movies it is said "There is no such thing as a coincidence". How can you elaborate on this statement? Note that none of these is a query on your opinions; rather they require technically- and/or philosophically-based responses. Use of AI in researching is allowed; yet, blend it with your further self-reading. Once your analysis is done, prepare a research note (a short paper) of 2000-2400 words that includes a well-organized and well-written treatment of (1), (2), and (3). The components to be included are 'Title', 'Abstract (100-120 words)', 'Keywords', 'Introduction', 'Literature Review', 'Discussion' and 'Conclusion', 'References (a max of 5)'. Your paper will not have an Appendix or Appendices, the number of footnotes can be a maximum of 5 and endnotes are not allowed. Use of AI in writing is not suggested; write your work on your own. Regarding the format of your paper (format elements: fonts, spacing; captions: enumeration and titling of visuals; layout elements: page structure and enumeration, etc.), briefly review the published empirical papers of the faculty members of your department and maintain the format of one of those published after 2020. Cite this guiding paper in your References. Print your report to file in PDF format. Your file must be named as: 221-Hw2-11111111.pdf, where 11111111 is your ID number. Not naming your files as described will result in the rejection of your submission. Submit your 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.
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 221 Mt2 will be up to the end of 3.7
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 222 Rec 1
090 Generating random numbers using RAND and the Inverse Transformation Technique - 2.xlsx 222 Rec 1
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 221 Rec 7
130 Simulating the Negative Binomial distribution.xlsx 221 Rec 7
170 Inverse Transformation Technique for a semicircularly shaped distribution .xlsx 221 Rec 10
180 Simulating a Galton Board.xlsx 222 Rec 2 221 Rec 8
221 Rec 9 Mt2 solutions to be covered: Past materials (Folder)
& TA will provide short guidance to Inverse Transformation Technique
221 Hw3-4 Double Homework due 12 December 2025 Friday 13:30 Microsoft Excel allows us to generate values for a variety of random variables, where we typically use them for everyday calculations or well-purposed simulations. For instance we can use BINOM.INV(.) to generate values for a Binomial random variable or can use NORM.INV(.) to generate values for a Normal random variable. However, to the best of my knowledge, there is no function in Microsoft Excel to generate Poisson or Exponential random variables. Luckily, we can use the Inverse Transformation Technique to generate Exponential random values, as described in the Lecture notes and a couple of spreadsheet exercises available on the course page. Go to these materials immediately, examine and learn them. While your Hw3-4 progresses, you'll also see a brief review of those in our Lectures. Your task in Hw3-4 is to design a computation scheme to generate values for any Poisson(lambda) random variable with 'any lambda'. Your design should reside on a single worksheet of an Excel workbook; it should be capable of generating a 'user-specified number of values'; it should provide a histogram of the generated values; it should calculate the empirical Expected value and Variance of the generated random values. Once the user-specified lambda and the user-specified number of values change, practically everything on the sheet must properly adjust. A friendly warning: you are not expected to develop a VBA function in Excel, rather expected to provide a basic spreadsheet solution, i.e., don't overkill. Use of AI in the preparatory phase of homework may be alright, but not in your design and reporting. 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: 221-Hw3-4-11111111.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.
190 Simulating an asymmetric Galton Board.xlsx
200 Calculating Expected Value via simulation - Miner question.xlsx 221 Rec 11
210 Calculating Expected Value via simulation - an advanced exercise.xlsx 221 Rec 11
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 222 Rec 2
221 Hw5 due 17 December 2025 Wednesday 13:30 Create a spreadsheet model of daily customer arrivals to a shopping mall in Ankara across 365 days. Your model is supposed to allow us to generate number of arrivals 'for ordinary days', 'for discount days', 'for winter/summer days', and the like in a fully randomized fashion, i.e., when we push F9 in Microsoft Excel, all the generated random values are to refresh. Within the 'same' structure, also consider the hourly intervals from 08:00 to 18:00 each day. For the intraday arrivals, consider what time of the year it is: whether it is 'the school season or not' and the like are supposed to reflect in your design. Once you finish creating arrivals, simply introduce a Binomial(n,P) structure to generate the number of items sold each hour of each day, while P depends on the 'day of the year' and the 'hour of the day'. Perform generation of random values on one sheet and present consolidated tables and figures of your creation/simulation on another: this summary should give a good idea of arrivals and sales. To have a basic understanding of people's tendency to visit malls, you may use AI, sure not to use it in your design and reporting. Needless to say, you are supposed to pick the appropriate statistical distributions and define dependencies between parameters. 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: 221-Hw5-11111111.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.
3.9 Random variables and distributions: Moments of distributions [Optional material]
3.10 Moment generating functions [Optional material]
3.11 Random vectors [Optional material]
SD (XLSX) z (PDF, PNG) Excel (Page)
4.2 Law of large numbers theorem 221 is here 221 Fnl will be up to the end of 4.2
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4.4 Distribution of sample means
240 Sampling from a finite population using RAND-INDEX-RANK combination.xlsx 222 Rec 3
222 Hw1 due 20 October 2025 Monday 13:30 Create two towns: town A with 5000 and town B with 1000 residents (citizen, person living in town). Each town will be on a separate worksheet of the same Excel workbook. Each citizen will be represented in a row with the following attributes: incomes in town A will come from an Exponential (1/100000) and incomes in town B will come from a Normal(100000, 225000000) distribution. Each citizen in each town will support either political party X or political party Y: in town A, each citizen has an independent probability of 0.60 to vote for X; in town B, it is 0.40. In each town half of the population is women and half is men. In this Homework, there is no statistical dependence between income, political tendency and gender variables; you'll introduce these in a subsequent homework. Note that, lambda, mu, sigma-square and P (figure out which is which) must be parameters in Excel, i.e., when you change them in their designated cells, incomes, political tendencies and genders must change. While submitting your work, the given parameters must be effective on your file. Make sure you've generated the distributional graphs and a rich set of descriptive statistics: go to Chapter 1. For each distributional parameter, check and document if the experimentally obtained (empirical) parameters match the theoretical (input) ones. Use of AI is not suggested in this homework. 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.
SD (XLSX) z (PDF, PNG) Excel (Page)
5 Point estimators Not included in 222 Fnl
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 222 Rec 4 Go over the German tank problem first
SD (XLSX) CI (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
6.3 Sample size determination 222 Mt1 up to here
222 Rec 5 Mt1 solutions to be covered: Past materials (Folder)
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 222 Rec 6
320 Assessing the forecast performance via Confidence intervals.xlsx 222 Rec 7
330 Limit of Chi-square(m) by m is 1 as m tends to infinity - via simulation.xlsx
SD (XLSX) CI (XLSX) HT (XLSX) z (PDF, PNG) t (PDF, PNG) Chi2 (PDF, PNG) F (PDF) Excel (Page)
7.1 Hypothesis testing: One population
222 Hw2 due 26 November 2025 Wednesday 13:30 This is a self-learning assignment: Research and learn (1) what 'stratified sampling' is, (2) what its purpose/function is, how it is done, when it is done, (3) if there are alternative approaches to it. Use of AI in researching is allowed; yet, blend it with your further self-reading. Once your analysis is done, prepare a research note (a short paper) of 2000-2400 words that includes a well-organized and well-written treatment of (1), (2), and (3). The components to be included are 'Title', 'Abstract (100-120 words)', 'Keywords', 'Introduction', 'A Review of Methodology', 'Conclusion', 'References (a max of 5)'. Your paper will not have an Appendix or Appendices, the number of footnotes can be a maximum of 5 and endnotes are not allowed. Use of AI in writing is not suggested; write your work on your own. Regarding the format of your paper (format elements: fonts, spacing; captions: enumeration and titling of visuals; layout elements: page structure and enumeration, etc.), briefly review the published empirical papers of the faculty members of your department and maintain the format of one of those published after 2020. Cite this guiding paper in your References. Print your report to file in PDF format. Your file must be named as: 222-Hw2-11111111.pdf, where 11111111 is your ID number. Not naming your files as described will result in the rejection of your submission. Submit your 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.
222 Handout 240311 - Hypothesis testing.pdf 222 Rec 8
7.2 Hypothesis testing: Two populations
222 Handout 240312 - Two population procedures.pdf 222 Rec 8
7.3 p-value Not included in 222 Fnl
7.4 Type I and Type II errors and the Power of a hypothesis test Not included in 222 Fnl
222 Handout 240325 - p-value and Power.pdf
340 Assessing the forecast performance via Hypothesis testing.xlsx
360 ANOVA - Analysis of Variance.xlsx 222 Rec 10
SD (XLSX) CI (XLSX) HT (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 222 Mt2 will be up to the end of 8.3 except 7.3 and 7.4
222 Rec 9 Mt2 solutions to be covered: Past materials (Folder)
222 Hw3-4 Double Homework due 10 December 2025 Wednesday 13:30 Retrieve your submitted Hw1 workbook file and continue working upon it. If you have not submitted Hw1, you'll need to do it from scratch to be eligible to submit Hw3-4; yet without receiving makeup points for Hw1. In Hw3-4, we'll allow for statistical dependencies within each town. First, create two more sheets in your workbook, i.e., one for each town. The specifications are as follows: (1) Town A has 5000 and town B has 1000 residents (citizen, person living in town). Each citizen will be represented in a row with the following attributes. (2) Half of the incomes in town A will come from Exponential(1/100000)*** distribution and half from Exponential(1/50000) distribution. (3) Half of the incomes in town B will come from Normal(100000, 225000000)*** and half from a Normal(50000,25000000) distribution. (4) In each town, half of the population is women and half is men. (5) In each town, a larger portion of men will have incomes according to distributions marked with (***) for their respective towns, i.e., a larger portion of women will have incomes according to distributions not marked with (***). (5) In town A, men are more likely to support political party X; in town B it is the opposite. (6) Regardless of gender, higher income citizens are more likely to support political party Y in both towns. Using these, re-create your towns. Note that, lambda, mu, sigma-square and P (figure out which is which) must be parameters in Excel, i.e., when you change them in their designated cells, incomes, political tendencies and genders must change. On a separate 5th sheet, provide the necessary graphs and tables so that your TA can easily change the parameters of your design and observe the dependencies effortlessly. Use of AI is not suggested in this homework. 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-Hw3-4-11111111.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.
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
222 Hw5 due 17 December 2025 Wednesday 13:30 Retrieve your submitted Hw3-4 workbook file and continue working upon it. If you have not submitted Hw3-4, you'll need to do it from scratch to be eligible to submit Hw5; yet without receiving makeup points for Hw3-4. In Recitation 4, you have seen how to sample from a finite population using the RAND-INDEX-RANK formula combination in the context of the German tank problem. In this homework, you'll use it to sample from your 'new' towns created in Hw3-4. (1) Come up with your own research questions, pretending you haven't created the towns yourself, yet you see them for the first time: 3 questions will involve single population hypothesis tests while another 3 will be about comparisons between two populations, i.e., between towns or between demographic groups in the same town. (2) To obtain estimates and the related inferences, first: do random sampling without stratification, second: do a proper, not necessarily perfect, stratified sampling. (3) Conduct and conclude your hypothesis tests based on the non-stratified and stratified samples. (4) Compare your results. What do you see? Why do you see it that way? Use of AI is not suggested in this homework. 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-Hw5-11111111.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.
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 is here 222 Fnl will be up to the end of 8.10
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222 Handout 241217 - MLR model summary.pdf 222 Rec 11
8.11 Model Specification Not included in 222 Fnl
SD (XLSX) CI (XLSX) HT (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
Descriptions
8.12 Regression analysis at work Not included in 222 Fnl
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.
370 Multiple Linear Regression modeling - Analyzing demand for soft drinks.xlsx
case04.csv case04.R case04.wf1 case04.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) Not included in 222 Fnl
222 Handout 240508 - FWL theorem.pdf
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, where the STUDENTS are to be seated according to 'Section information and grades' files
ECON 221 Midterm 1: ---
ECON 221 Midterm 2: ---
ECON 221 Final: ---
Cross-check with the Faculty announcements
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ECON 222 Midterm 1: ---
ECON 222 Midterm 2: ---
ECON 222 Final: ---
Cross-check with 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: two sides of a single A4-sized or smaller sheet. Formula sheets can contain anything. Formulas can be hand-written or computer-typed.
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.