Lecture 1 v3.pdf

ECON-UH 2020 - Data Analysis
Spring 2022
Lecture 1: Course Logistics
Betul Arda
arda@nyu.edu
Jan 25, 2022

Outline
• What is data analysis?
• Why and how data science became popular
• Doesn’t data just speak for itself?!
• Course logistics:
• Attendance and participation
• Topics and schedule
• The textbooks and other learning resources
• Data resources
• Office hours
• Assignments and group projects
• Exams
• Academic honesty
2
25 January 2022 Lecture 1: Course logistics

What is data analysis?
25 January 2022 Lecture 1: Course logistics 3
Data analysis is extracting information from data.
We will learn how to organize and analyze data and interpret the
results (information).
The difference between inference and speculation is data.

Questions we can investigate with suitable data
Effects of a newly
implemented traffic
law on traffic accident
fatalities.
Bank loans and risk
assessments – can you
predict if a loan will
get accepted or not?
Inventory prediction
before holiday sales.
The effects of Brexit on UK
economy. The effect of COVID19
on gas prices.
Any other examples?

Some examples from https://www.analyticsinhr.com/blog/advanced-data-analysis-techniques-applied-to-people-analytics/

Data does not speak for itself!
A common
misconception: if
we have enough
data and a good
statistical
software, we can
learn everything
we need from that
data. WRONG!
A first simple
look at data
can be
misleading.
One can miss
underlying
phenomena
that are not
readily visible.
One can
mistake
artifacts for
meaningful
relationships.
One can run
the wrong
tests and/or
misinterpret
the results
and make
wrong
inferences.

Why data analysis?
The short and oversimplified answer:
In an argument, would you rather have evidence or opinion?

How it became so popular – the exponential growth in computing power.
http://content.time.com/time/interactive/0,31813,2048601,00.html

Why data analysis?
Why and how did data analysis become popular?
• We will never run out of data.
• The Internet! The ease of access to global data.
• Many questions to be answered with readily available or
collectible data.
• Data collection is also easier than ever.
Data is “renewable”:

Why data analysis?
Why and how did data analysis become popular?
• Time and money.
• The cost of educated decisions vs speculation.
• Time cost.
• Irreplaceable resources? Any ideas?
Limited and/or irreplaceable resources:

Why data analysis?
Still not convinced?
• The careers you want to pursue will most likely require some level of data analysis
knowledge.
• My children learned about mean, median, quartiles, histograms and the concept of
probability distributions as a part of his school curriculum in 3rd grade. By the time
these kids reach college age in 10 years, they’ll already be ahead of your current
position. Kids are learning coding and statistics at young ages. You must stay
relevant!*
• *This is an anecdotal evidence and an opinion. My argument that you will need data analysis skills
shouldn’t be very convincing without any data and supporting evidence. Keep on reading 
You will be left behind otherwise.

Some evidence
• This is just one study on the demand for data analysis skills. Do your own research.
• American Statistical Association: New Report Highlights Growing Demand for Data Science,
Analytics Talent. https://www.amstat.org/ASA/News/New-Report-Highlights-Growing-Demand-for-Data-Science-Analytics-
Talent.aspx
• Jobs of the Future: Data Analysis Skills: https://www.shrm.org/hr-today/trends-and-forecasting/research-and-
surveys/Pages/data-analysis-skills.aspx
Why organizations do
not use big data:
•51% lack of knowledge/
expertise
•30% not enough data
collected/available

Objectives: What will you learn?
The ability to answer questions with evidence
A good data analysis is never dumping raw data
into a software and seeing what comes out.
It is finding the right methods and properly
interpreting the results.
The ability to ask (meaningful) questions
We will turn real-world problems into
mathematical models.
Questions before and after the
analyses.
This class will teach you critical thinking in a quantitative way.

Recitations
• Sunday,
• REC1: 3:45pm-5:00pm (A2-012),
• REC2: 2:20pm-3:35pm (A2-012),
• REC3: Fri 7:55am-9:10am (A2-007),
• REC4: Fri 9:20am-10:35am (A2-007).
• The recitations will help tremendously.
• The instructor (Jon) will cover some Stata and stats basics next week.
• He will go over extra examples and you will practice on data.
• Practice will help you in exams, projects, later in other classes and
your career.

Assignments
• Practice is essential when learning data analysis.
• The assignments will help with thorough comprehension of the
material.
• Read the questions carefully and be mindful of the analyses you
are carrying out.
• Explain your steps and the results so you and I both know what
you are doing and why you are doing it.
• Recitations will help with practicing so it will be easier
completing the assignments.

Assignment reports
• There is an example report on Brightspace!
• Add comments to your code. Explain everything.
• Your code should run all at once.
• This is important as we won’t keep selecting and running partial
code to see if it’s working partially.
• When adding a comment, make sure to follow proper comment
notation so it does not stop your code from running as a whole.
• Clearly label all your plots, axes, etc.

Assignment reports
• Type your assignments. NO handwriting, please.
• Create a PDF with all your answers, plots, explanations, etc.
ONLY PDF. No docx.
• Code in a separate .do file.
• Do not add code into your report, and vice versa.
• Submit to Brightspace.
• Be mindful of math notations (subscripts, superscripts, etc.)
• No X1 if it’s 𝑥𝑥1.
• No theta^2 if it’s 𝜃𝜃2
.

Assignment reports
• Label your code as: LastnameFirstInitial_HW1.do (ArdaB_HW1.do)
• Label your pdf as: LastnameFirstInitial_HW1.pdf (ArdaB_HW1.pdf)
• Label your data as: LastnameFirstInitial_HW1.dta (ArdaB_HW1.dta)
• Label your log as: LastnameFirstInitial_HW1.log (ArdaB_HW1.dta)
• We receive about 40-50 reports per week per semester. With each report, we also
have the code, the data and the log. Multiply it with 10 – we’ll have 10 HWs this
semester. Can you imagine the mess if you don’t label your work properly?
• Submit the data as is. This might seem unnecessary, but it’ll help us confirm that the
problems in your results are not due to data download problems.
• Submit your log file too.

Assignment reports
• The first homework will be posted today (January 25).
• Due January 31, Monday, 11:59pm, sharp.
• 10 points will be subtracted for each additional hour starting
midnight.
• Ex: submitted Feb 1, 12:00:01 am (basically right after midnight)
𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 ≤ 90, 1:30am 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 ≤ 80, etc.
• If your circumstances have proved it difficult to finish the HW on
time that week, let me know and ask for an extension before the
last minute.

Assignment reports
• Over the years, I and the instructors of this course have been
providing students with code that would help them not just
for this course but upcoming econometrics courses as well.
DO NOT ever present those code snippets as your own.
• For example, if you ever use a code snippet of Emir Hidovic or
Jonathan Rogers from recitations, cite them in the upcoming
econometrics courses (and elsewhere). It is their intellectual
property. The professors and instructors will recognize their own
code.

Academic honesty
• There are many ways to write a piece of code that does the
same thing.
• Write your own code. Do NOT copy someone else’s. Seriously, don’t
risk your whole academic future.
• Write your own reports in your own words.
• You aren’t allowed to use “homework” websites, solution
manuals, etc. If you can find it online, so can we.
• If you are stuck, ask for help from me or Jon.
• No posting course materials elsewhere. These are considered
intellectual property.
• Lecture notes, assignments, exams, solutions, recitation notes,
codes, meeting videos, etc. are all considered intellectual property.

The software
• Follow the instructions you received in your email to
download Stata 17.
• Earlier versions of Stata are fine to use for this course, but you may
need some of the newer features for the next econometrics course.
• Why Stata? Not R or any other free software?
• The short answer: easier to use (more user-friendly) and still very
versatile.
• This is not a coding class. The less time you spend trying to figure
out how to code something, the more time you’ll have for data
analysis topics.

The software
• Why Stata? Not R or any other free software?
• User-friendly, interactive interface.
• Lets us focus on learning data analysis without trying to figure out a
more complicated software.
• Comes with detailed help files full of real examples.
• Most more advanced econometrics classes at NYUAD require Stata
due to similar reasons.
• Commonly used in social sciences research.
• Comparison: https://www.r-bloggers.com/whats-the-best-statistical-software-a-comparison-
of-r-python-sas-spss-and-stata/

The software
• There is a third section of this class using R.
• Other statistical software/programming languages you may
want to explore: R, Python, Java, C++, C#, SAS, SPSS, MATLAB,
Mathematica…
• Once you learn to code in one language, you will find it easier to learn
any other language compared to starting blind.
• Python is probably the most popular programming language for
machine learning today.
• But we won’t go into machine learning topics this semester.

The textbooks
• Cross-sectional analysis:
• Introductory Econometrics: A Modern Approach. Wooldridge. 7th ed.
• Focuses on why and how to in general.
• Many examples to strengthen your understanding of the concepts.
• Introductory level. Easy to read. We won’t go over all the proofs.
• Time-series analysis:
• Introduction to Time Series Using Stata. Becketti.
• Doesn’t include many proofs anyway. Many examples.
• Why not Wooldridge for time-series: because it overcomplicates the
time-series topics. Becketti is easier to read and learn TS from.

Data: the books’ resources – Wooldridge
Wooldridge data
• Manual download:
https://www.cengage.com/aise/economi
cs/wooldridge_3e_datasets/statafiles.zip
• You can also use Stata’s bcuse
(findit bcuse) and install.
• The data list:
http://fmwww.bc.edu/ec-
p/data/wooldridge/datasets.list.html

Data: the books’ resources – ITSUS
• Download the data from the book’s website:
• https://www.stata-press.com/data/itsus.html
• Check out the errata for the list of typos:
• https://www.stata-press.com/books/errata/itsus.html
• Install directly into Stata:
net from “http://www.stata-press.com/data/itsus/”
net get itsus_files
net get itsus_data
net install itsus_files

Data: public databases
• Always cite your source!
• Data, papers, code, etc.
https://scholar.google.com
• Failing to do so is called
plagiarism! Don’t be that
person.

• Always cite your source!
• Data, papers, code, etc.
• You can install the
Google Scholar button in
your browser for ease of
access to several citation
formats.

• You can use many data types
in Stata:
• The data set does not have to
be in Stata format (.dta)
• You can import other text
formats such as csv, txt, xlsx,
etc.

Data: FRED
• Federal
Reserve of
Economics
Data (FRED)
https://fred.stlouisfed.org/

Data: FRED
• Federal Reserve of Economics
Data (FRED) –
• Check out the video about how
to use freduse
• Install freduse either with the
link or the code
findit freduse
ssc install freduse

Data: FRED
• Federal Reserve of
Economics Data (FRED) –
• Use the search box to
search for data used in
class or assignments
• Try GDP US.
• Load it directly into Stata.

Data: FRED
• You’ll need the variable
names.
• Variable name for Real GDP
of the US: GDPC1
• Stata 17 allows you to
directly search in the
program.
• For versions earlier than
15, you’ll need to peruse
the website.

Data: FRED
• Direct access in Stata
• File > Import > Federal
Reserve Economic Data
(FRED)
• You can search for
keywords, add dates and
import the data directly
into Stata.
• You still have to add the
code in your assignments!
• e.g. freduse GDPC1

Data: Yahoo! Finance
• Data can be
downloaded from the
website or directly
into Stata using
fetchyahooquotes

Yahoo! Finance
• Use the code below to
install:
net install http://researchata.com/stata/203/fetchyahooquotes.pkg, force

Data: Yahoo! Finance
• Alphabet Inc. (GOOG)
clear all
fetchyahooquotes GOOGLE, freq(m) start(1jan2007) end(31dec2018)
line adjclose_GOOG date
• Once installed, try this code to check if it’s
working properly:

Exams
• Exams will be similar to the homework assignments:
• Analyzing the data sets assigned to you.
• Interpreting the results.
• Making and discussing the inferences.
• Midterm will be during lecture time
• Tentative: March 10. Open-book. On paper.
• Final exam date and time will be determined by the registrar.
• Cumulative. Double check the date and time closer to the exam
date. In the computer lab.

Group projects
• Data does not speak for itself!
• Even after you possibly carried out all the analyses under the sun
and found every bit of information it may contain. This is assuming
you chose the correct methods.
• You still have to find a neat and efficient way to present all
your analyses and inferences.
• Working on a project will give you experience in presenting
your work in front of an audience.
• You will also provide feedback for other projects assigned to
you.

Group projects
• Groups of 3-4. You can work with people in the other section.
• You will:
• Come up with a research question.
• Find relevant (publicly available) data.
• Write a proposal on how you are planning to tackle your research
question. Present your proposal in 8 minutes in class.
• Tackle your research question using the methods we learned,
analyze the data. Present your results in 10 minutes in class. This
will be the finished project.
• Write a final report.

Group projects
• Submissions: Project proposal (1st and 2nd drafts), updated
project report with results, final report.
• Due dates on the syllabus.
• About the research question: find something interesting.
• Every year I see at least 2 projects about same old topics. Be original. If you
repeat any topic that has been done before (i.e., if older cohorts gave you a
topic “idea”), I’ll ask for originality.
• This is a good time to think about your capstone. Try a few things now so you
can improve those or move on to new things for your capstone.

Group projects
• About the group project data:
• Start looking for the data the moment you have a research
question idea.
• Every year at least a couple groups realize that they can’t access to
data relevant to their topic.
• Either the data isn’t publicly available or simply does not exist.
• Or the data is publicly available, but it is too messy and complicated to
work on. E.g., requires extensive clean up, deciphering the variables, etc.
• You are allowed to change your topic and your research question, but you
can’t get the wasted time back. Find and check the data before it is too
late.

Group projects
• You can work with any publicly available data.
• Except for data created for teaching purposes like for books or software.
E.g., Stata datasets cited in the user manuals, or the datasets included in
the Wooldridge (or any other book) may be altered to fit the teaching
narrative.
• There are many publicly available trusted data resources.
• Do your own research to find them. It is a part of the learning experience.
• Ex: interested in global warming? NASA might have the data you're
looking for. COVID-19? Check out WHO. Macroeconomics data? FRED has
it covered.

Group projects
• Work with financial data at your own risk.
• NYUAD has access to the WRDS database via the library computers, but our
course hasn’t been given personal (home) access since it is not a finance course.
Rethink your decision to work with financial data if you have no previous
experience (deep personal interest, previous courses, etc.) with finance theory
and accounting terms. The instructor will not be able to help you deciphering the
WRDS database.
• Want to collect your own survey data? Consider these first:
• Have you checked the regulations? Do you have the IRB approval?
• Do you know how to properly do this? (population specification, sample frame,
non-responses, nonleading words, mutually exclusive categories, etc.)
• Are you sure you can collect a large enough sample on time?

Attendance and participation
Data analysis is a VAST area.
We will focus on practice and only
enough theory to make sense of
what we are doing and why.
But we cannot completely ignore
theory since practice won’t make
sense otherwise.
This is a lecture class.
Take notes!
You will need your notes along
with the slides and the books.
Your learning experience will
enhance significantly if you pay
attention to the lectures.

• This course is about your level as long as you put in the
necessary effort.
Pay attention to the lectures.
Attend
Ask and answer questions.
Participate
Study the material.
Read
Do the weekly homework assignments*.
Do
*This font on the slides indicates code you can type into Stata. (Yes, even this!)

• Read the assigned material as we cover them so you can participate in
class.
• Beyond your grade, your learning experience highly depends on how much
attention you pay to the lectures. You’ll have an easier time navigating the
topics throughout the semester.
• Do not hesitate to give a “wrong” answer to my questions during lectures.*
*except in exams, assignments and project presentations, of course.
• If you won’t be able to attend a lecture, let me know beforehand with
your reasoning.
• If can’t let me know before, definitely let me know after.
• We can discuss how to make sure you’re not behind.

• As per the university’s decision, the lectures will remain online
at least until February 4.
• Cameras will stay on for the duration of the lectures.
• This is comparable to physically showing up to a class.
• You are expected to attend lectures on campus once the
university reverts to face-to-face teaching.

• Participation means asking and answering questions.
• The class discussions will help you discover the areas you didn’t fully
understand.
• It will give me the ability to reinforce those areas.
• It will also earn you 10% of your grade. You can’t get an A if you are not
participating. A starts at 93.
• Do not hesitate to give a “wrong” answer to my questions during lectures.*
These are a part of the learning process.
• We will use the chat window on Zoom to ask and answer questions.
• You can raise your hand (button on Zoom) or directly write questions in the
chat. I’ll keep track of that area while teaching.
*obviously, except exams and project presentations, etc.

Participation grade rubric
• 50 for attending all lectures + 50 for actively answering questions and participating in the
discussions
• 100 – student does not miss a lecture without an excuse and consistently participates in the
discussion 15+ active participation (out of 22 lectures)
• 90 – student attends all lectures and participates sometimes but not always (10+ active)
• 85 – student attends but participates only a few times out of 22 lectures (5+ active)
• 80 – attends but never participates (<5 active participation)
• 75 – student attends the lectures but consistently late and/or preoccupied but 5+ participation
• 70 - student misses several (3+) lectures without an excuse or never participates
• 65 - student misses several (3+) lectures without an excuse and never participates
• 50 – 4-6 missing lectures
• 30 – 7+ missing lectures
• 0 – 10+ missing lectures

• Please attend the section you are registered in.
• Unless you have permission from me. Let me know beforehand.
• This is to ensure virtual class manageability and the sanitization
requirements when we go back to face-to-face teaching.
• Please be present on time.
• It won’t be easy to allow people in from the waiting room after the
first few minutes.
• I’ll be online a few minutes early if you want to ask any questions or
discuss a topic.
• I start lectures at exactly 9am and 10:25am in-class too.

Schedule
• First few classes: a fast review of necessary statistical
concepts and Stata code.
• You have already covered these topics in previous classes. This
will only be to refresh your memory.
• These will be the building blocks to understanding the rest of the
class and data analysis in general.
• Pay close attention to what you can easily remember and what
you need to review on your own.
• Technically, I will assume you already know these concepts, but a
refresher never hurts.
• Ask me if you need additional resources for recap.

Schedule
• First part of the semester: cross-sectional data
• Simple linear regression
• Multiple linear regression
• Common mistakes and how to avoid them
• Common problems and possible solutions
• Multiple regression analysis with qualitative information
• Binary (dummy) variables
• Logit and probit models
• Instrumental variables
• Will take about ~60 − 70% of the semester (8-9 weeks)

Schedule
• Second part of the semester: time-series data
• Components of time-series data
• Basic regression analysis with time-series data
• Time-series filters
• Parametric time-series models
• Common problems and possible solutions
• Forecasting
• A brief intro to panel data
• Will take ~30 − 40% of the semester (4-5 weeks)
• Most methods covered in the first 8 weeks will be applicable to time-series
and panel data. The second part will also include methods which can only be
applied to time-series or panel data.

Office hours
• Monday, 8:30am-10:00am or by appointment.
• Always on Zoom. Links on Brightspace.
• You can join in any time until 9:50am. You don’t have to arrive at 8:30
and stay the whole time. Stop by, ask your question and either stay and
listen to other people’s questions or leave. No obligation either way.
• Please do not hesitate to ask for help. I’m always happy to assist you
about this class. If you do not ask, I cannot help you.
• If you are having difficulty following the material because of gaps in
your background, let me know so I can recommend extra resources.
• If you would like to dive deeper into these topics, let me know so I can
assign you extra optional reading material.

Grading
Requirement Percentage
Weekly homework assignments 20%
Group project presentation and report 20%
Participation 10%
Midterm exam 20%
Final exam 30%

Before you go…
• Things to do before Thursday:
• Read Wooldridge Chapter 1, and Appendices A and B
• Install Stata.
• Download Wooldridge’s data sets.
• Download ITSUS data sets.
• Install freduse.
• Complete the installation and try the codes on the slides. If they don’t work, it
means you didn’t install it properly.
• Peruse FRED’s website.
• Install fetchyahooquotes.
• Try the code included on the slides.
• Be mindful of the time zones. Everything is listed in GST (GMT+4).

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