UNM Stat 428/528: Advanced Data Analysis II (ADA2)
Table of Contents
Goal
Learn to produce beautiful (R Markdown) and reproducible (quarto) reports with informative plots (ggplot2) and tables (kable) by writing code (R, tidyverse, Rstudio) to answer questions using fundamental statistical methods (multiple regression, analysis of covariance, logistic regression, and multivariate methods), which you’ll be proud to present (poster).
Content
Roadmap
Here’s your roadmap for the semester! Each week, follow the general process outlined below:- The class maintains a Tuesday/Thursday schedule.
- Each Tuesday and Thursday:
- Enjoy reading the assigned chapter, using Video lectures to supplement the reading.
- If available, experiment with Applets to develop intuition and work through Tutorials to practice R coding with data.
- Complete the homework assignments in the form of exercises and worksheets.
- Tuesday assignments are due Friday by 11:50 PM
- Thursday assignments are due Monday by 11:50 PM
- The table below has a row for each Tuesday and Thursday.
Resources
- UNM Canvas for completing quizzes and for submitting worksheet assignments (evaluated by TA within 1 week).
- After uploading a pdf assignment, verify with a preview of the file.
- Book (online and free)
- ADA: Statistical Acumen: Advanced Data Analysis by Erik Erhardt.
- Note that the (historical) chapter numbers referred to in the table and assignments below differ from the (new) book chapter numbers. The chapter names are the same, and I hope this won’t cause much confusion.
- Videos
- Lectures: YouTube Video playlist (try 1.5 speed, then pause as needed). (Videos based on Spring 2016 notes.)
- Assignments: YouTube Video playlist (walk-through of each assignment)
Pre-course to-dos
Did you receive a registration error for Spring 2025? Send me an email with the following answers:- What registration error did you get (copy/paste is best)?
- What is your UNM ID?
- What is your Math/Stat background (that is, do you have the prerequisites)?
Software, R
Using R (through the RStudio IDE)
R will be used for all homework assignments. You can use R by downloading R onto your own computer. R is freely available at http://www.r-project.org/ and is already installed on many college computers. Additionally, you are required to install RStudio and turn in all R assignments using Quarto (RMarkdown). http://rstudio.org/. (You can use the LaTeX compiler at: https://yihui.name/tinytex/)Installing software and packages (Step 0)
Before our first “class” (Mon 1/20/25), please read through the following actions and install the required software on your computer. Video- Install:
- Install R packages.
- Follow these instructions: R packages. (Ignore warning about rtools or any packages unavailable.)
- In RStudio, open Packages tab, click on “Update”, Select All, Install Updates (“No” to restart, “No” to compile from source).
- Make sure the erikmisc package works by printing the logo in the Console:
library(erikmisc)erikmisc_logo()
- Set up your computer
- RStudio disable notebook
- Operating system to be more friendly to programming.
type="binary" option.
Learning R, self-study
- R for beginners
- A fantastic ggplot2 tutorial
- Incredibly helpful cheatsheets from RStudio.
Timetable
| Date | Class | Topic | Reading, Video, Quiz | class Worksheet, Data |
|---|---|---|---|---|
| 01/20 | 00 | Install software |
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| 01/21 | 01 | 01 R statistical software and review
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| 01/23 | 02 |
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| 01/28 | 03 | 02 Introduction to Multiple Linear Regression
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| 01/30 | 04 | |||
| 02/04 | 05 | 03 A Taste of Model Selection for Multiple Linear Regression
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| 02/06 | 06 | 04 Experimental Design: One- and Two-Factor Designs
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| 02/11 | 07 | 05 Paired Experiments and Randomized Block Designs
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| 02/13 | 08 | |||
| 02/18 | 09 | |||
| 02/20 | 10 | |||
| 02/25 | 11 | 06 Discussion of Observational Studies
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| 02/27 | 12 | 07 Analysis of Covariance: Comparing Regression Lines
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| 03/04 | 13 | 08 Polynomial Regression
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| 03/06 | 14 | 09 Response Models with Factors and Predictors
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| 03/11 | 15 | 10 Model Selection for Multiple Regression
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| 03/13 | 16 | |||
| 03/18 | Spring Break | |||
| 03/20 | Spring Break | |||
| 03/25 | 17 | 11 Logistic Regression
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| 03/27 | 18 | |||
| 04/01 | 19 | 12 An Introduction to Multivariate Methods
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| 04/03 | 20 | PCA, continued | ||
| 04/08 | 21 | 14 Cluster Analysis
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| 04/10 | 22 | |||
| 04/15 | 23 | 16 Discriminant Analysis
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| 04/17 | 24 | |||
| 04/22 | 25 | 13+11+17 PCA and logistic regression classification
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| 04/24 | 26 | |||
| 04/29 | 27 | 10 Model Selection for Multiple Regression, revisited: ATUS data subset and model selection
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| 05/01 | 28 | MS Stat Qual exam, you can do it! | ||
| 05/06 | 29 | |||
| 05/08 | 30 |
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| 05/13 | FINALS WEEK | (no final) | Congratulations on a great semester! |
(I reserve the right to continue to improve the materials throughout the semester.)
Syllabus
- Description: A continuation of 427/527 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminant analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression.
- Prerequisite: Stat 427/527 (ADA1)
- Semesters offered: Spring
- Lecture: Stat 428, CRN 33933; 528.001, 33935; Online MAX Arranged
- Email: Please include “ADA2” in the subject line of all emails; please do not send messages via UNM Canvas.
Instructors
- Professor
- Erik Erhardt <erike@stat.unm.edu>, he/him
- Teaching Assistants
- Mingyue Liu <mingyueliu@unm.edu>, she/her
- Collin Evans <coevans@unm.edu>, he/him
Office hours
See email “ADA2, Stat 428/528, Announcements” for Zoom links and instructions. UNM Authentication instructions.- Before attending an office hour, please email the instructor above to let us know when you’ll be attending
- Email example: “ADA2 Office Hours Tue 10 AM: I’ll be there to ask about X, Y, and Z”.
- We will certainly be at our office hours if you let us know that you’re coming. However, we reserve the right to cancel an office hour last minute without notification if no one has let us know they will be attending. This is a way of respecting everyone’s time so we can each be effective in our lives.
- We are also available by appointment by email if these many hours do not work for your schedule.
| Time | Mon | Tue | Wed | Thu | Fri | Sat | Sun |
| 8 AM | MYL | MYL | MYL | ||||
| 9 AM | MYL | EE | MYL | EE | MYL | ||
| 10 AM | EE | EE | |||||
| 11 AM | EE | EE | |||||
| 12 PM | CE | CE | CE | CE | CE | ||
| 1 PM | CE | MYL | EE | MYL | CE | ||
| 2 PM | CE | MYL | EE (end 2:30) | MYL | CE | ||
| 3 PM | CE | ||||||
| 4 PM | |||||||
| 5 PM | |||||||
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| 7 PM | |||||||
| 8 PM | |||||||
| 9 PM |
Student learning outcomes
Similar to ADA1, but at a higher level.Assessment
- You are adults; I will respect and treat you as such. Do not request special treatment unless you have serious unavoidable circumstances. Asking me or a TA to accept late assignments is unbecoming. You are responsible for your own success (with plenty of our eager support to that end), so start early, ask for help early in Zoom office hours, make the necessary time to complete the work, and turn in work on time that you’re proud of.
- Quizzes. Purpose: to assess reading and video comprehension and assure you’re prepared to actively participate in worksheet activities with minimal lecture. (About 12, 15% of final grade.) Most weeks plan for 1-2 hours reading and video, 20-minute quiz. Quizzes are not timed, they can be taken twice, and the higher of the two scores is used as the grade.
- Quiz solutions can be viewed after the due date in UNM Canvas.
- Worksheet assignments. Purpose: to struggle and find success in class with the concepts and skills. (About 21, 83% of final grade.)
- Course surveys are due at the end of the course (EvalKit). (1 or 2, 2% of final grade.)
- No late assignments. The purpose of the drop policy is to have an automatic mechanism for well-meaning students who miss a due date/time by a few minutes or legitimately have important personal business that conflicts with effort on an assignment to reduce making exceptions for late work with the professor. Such deal making is exhausting and unbecoming of mature students. Roughly speaking, the lowest 2-weeks worth of assignments are dropped, so your lowest 1-2 quizzes assignments and 2-3 worksheet assignments are not included in the calculation of your grade (this could include a worksheet assignment that spanned a full week). Large two-week long, 20-point assignments are not eligible to be dropped. Spring 2025: 2 quizzes; 3 assignments excluding Class 14, 23, 25, 27, and 28.
- Final grade may include a small buffer at the discretion of the instructor. For example, final grade could be the total points earned adjusted none or a little for graduate students and a little more for undergraduate students. That is [Final Grade] = 1 – (1 – [Points Earned])/a, where a = 1.25 for undergraduate students. This increases your grade is slightly higher than you earned, and does more so for those with lower grades. Here’s R code to see how grades would adjust for a given value of “adjustment”:
adjustment = 1.25
tibble::tibble(
original = seq(0, 1, by = 0.05)
, adjusted = 1 - ((1 - original) / adjustment)
, difference = adjusted - original
) |>
print(n=Inf)
- All assignments in this class are electronic, submitted to UNM Canvas. For all submissions: (1) In Quarto, render qmd file to HTML, (2) Open HTML file in your internet browser, (3) Print HTML to pdf file, (4) Submit pdf to UNM Canvas. Always view your submission in Canvas to verify that the grader will also be able to view your assignment!
- Browser choice: Chrome is the best browser choice. On a Mac, Safari adds “.txt” to Quarto files when downloaded, and Firefox sometimes fails on upload of a pdf to UNM Canvas.
- Rubrics guide assessment (and self-assessment) of homework, code, projects, exams, and presentations. Each assignment will have its own specific rubric.
- The use of R and Quarto are required for the course. This will include all of the R code for the assignment with the part of the problem it addresses in a fixed-width font and syntax highlighting. You will weave your code with prose narrations of your work and solutions.
Collaboration and citation
- For homework, I encourage you to work together. Please discuss the data, code, and problems with one another, but do your own exploration and write-up. We expect everyone to hand in substantially different homework, and we will enforce this under the honor code. The small benefit you might get from plagiarism is not worth the severe penalty (of lost trust, being reported to the dean, no points for the assignment, etc.).
- As in life, please use any resources available to you. Projects and some homework will explicitly encourage you to use resources on the internet, but showing extra initiative will always be appreciated. You may find R programming tough at first, so feel free to discuss your problems with other classmates or meet with or email questions to the TAs or me.
- I encourage you to use the ideas of others, but make them your own, giving credit. For projects have a formal bibliography, for homework cite casually, and for code simply copy the URL into your code as a comment (which is doubly helpful to you for finding the resource again).
Statements
COVID-19 Health and Awareness
UNM is a mask friendly, but not a mask required, community. If you are experiencing COVID-19 symptoms, please do not come to class. If you do need to stay home, please communicate with me at my email; I can work with you to provide alternatives for course participation and completion. Let me, an advisor, or another UNM staff member know that you need support so that we can connect you to the right resources. Please be aware that UNM will publish information on websites and email about any changes to our public health status and community response. Support: Student Health and Counseling (SHAC) at (505) 277-3136. If you are having active respiratory symptoms (e.g., fever, cough, sore throat, etc.) AND need testing for COVID-19; OR If you recently tested positive and may need oral treatment, call SHAC. LoboRESPECT Advocacy Center (505) 277-2911 can offer help with contacting faculty and managing challenges that impact your UNM experience.Accessibility and Privacy
UNM is committed to providing courses that are inclusive and accessible for all participants. As your instructor, it is my objective to facilitate an accessible classroom setting, in which students have full access and opportunity. If you are experiencing physical or academic barriers, or concerns related to mental health, physical health, and/or COVID-19, please consult with me after class, via email/phone, or during office hours. You are also encouraged to contact the Accessibility Resource Center at arcsrvs@unm.edu or by phone 277-3506.
Below are accessibility and privacy statements for the tools we will be using in this course. If you have questions or concerns about any of these, please contact me.
- UNM Canvas: Accessibility Statement – Privacy Statement
Kaltura: Accessibility Statement – Privacy StatementMicrosoft Office: Accessibility Statement – Privacy Statement- Adobe Acrobat: Accessibility Statement – Privacy Statement
- Google Products (including YouTube): Accessibility Statement – Privacy Statement
- Zoom: Accessibility Statement – Privacy Statement
Credit-hours
This is a three credit-hour course delivered in an entirely asynchronous online modality over 16 weeks during the Spring 2023 semester. Please plan for a minimum of 9 hours per week to learn course materials and complete assignments. Support: Resources to support study skills and time management are available through Student Learning Support at the Center for Teaching and Learning.Title IX statement
Our classroom and our university should always be spaces of mutual respect, kindness, and support, without fear of discrimination, harassment, or violence. Should you ever need assistance or have concerns about incidents that violate this principle, please access the resources available to you on campus. Please note that, because UNM faculty, TAs, and GAs are considered “responsible employees” any disclosure of gender discrimination (including sexual harassment, sexual misconduct, and sexual violence) made to a faculty member, TA, or GA must be reported by that faculty member, TA, or GA to the university’s Title IX coordinator. For more information on the campus policy regarding sexual misconduct and reporting, please see: https://policy.unm.edu/university-policies/2000/2740.html. Support: LoboRESPECT Advocacy Center, the Women’s Resource Center, and the LGBTQ Resource Center all offer confidential services.Citizenship and/or Immigration Status
All students are welcome in this class regardless of citizenship, residency, or immigration status. Your professor will respect your privacy if you choose to disclose your status. As for all students in the class, family emergency-related absences are normally excused with reasonable notice to the professor, as noted in the attendance guidelines above. UNM as an institution has made a core commitment to the success of all our students, including members of our undocumented community. The Administration’s welcome is found on our website: http://undocumented.unm.edu/.Land Acknowledgement
Founded in 1889, the University of New Mexico sits on the traditional homelands of the Pueblo of Sandia. The original peoples of New Mexico Pueblo, Navajo, and Apache since time immemorial, have deep connections to the land and have made significant contributions to the broader community statewide. We honor the land itself and those who remain stewards of this land throughout the generations and also acknowledge our committed relationship to Indigenous peoples. We gratefully recognize our history. Faculty Resource: Information provided by UNM’s Division for Equity and Inclusion can support building an inclusive classroom, https://diverse.unm.edu/education-and-resources/programs/index.html.Respectful and Responsible Learning
We all have shared responsibility for ensuring that learning occurs safely, honestly, and equitably. Submitting material as your own work that has been generated on a website, in a publication, by an artificial intelligence algorithm, by another person, or by breaking the rules of an assignment constitutes academic dishonesty. It is a student code of conduct violation that can lead to a disciplinary procedure. Please ask me for help in finding the resources you need to be successful in this course. I can help you use study resources responsibly and effectively. Off-campus paper writing services, problem-checkers and services, websites, and AIs can produce incorrect or misleading results. Learning the course material depends on completing and submitting your own work. UNM preserves and protects the integrity of the academic community through multiple policies including policies on student grievances (Faculty Handbook D175 and D176), academic dishonesty (FH D100), and respectful campus (FH CO9). These are in the Student Pathfinder (https://pathfinder.unm.edu) and the Faculty Handbook (https://handbook.unm.edu). Support: Many students have found that time management workshops or work with peer tutors can help them meet their goals. These and are other resources are available through Student Learning Support at the Center for Teaching and Learning.Connecting to Campus and Finding Support
UNM has many resources and centers to help you thrive, including opportunities to get involved, mental health resources, academic support such as tutoring, resource centers for people like you, free food at Lobo Food Pantry, and jobs on campus. Your advisor, staff at the resource centers and Dean of Students, and I can help you find the right opportunities for you.Support in Receiving Help
Students who ask for help are successful students. I encourage students to be familiar with services and policies that can help them navigate UNM successfully. Many services exist to help you succeed academically, such as peer tutoring at CAPS and http://mentalhealth.unm.edu. There are plenty of ways to find your place and your pack at UNM: see the “student guide” tab on my.unm, students.unm.edu, or ask me for information about the right resource center or person to contact.Doing the Right Thing
UNM has policies to preserve and protect you and the academic community available in the Student Pathfinder as well as in the Faculty Handbook. These include policies on student grievances D175 (undergraduates) and D176 (graduate and professional students), academic dishonesty (D100), and respectful campus (CO9). Please ask for help in understanding and avoiding plagiarism (passing the work or words of others off as your own work or words) or other forms of academic dishonesty. Doing something dishonest in a class or on an assignment can lead to serious academic consequences. Come talk with me about your concerns or needs for academic flexibility or talk with support staff at one of our student resource centers before you do something that may endanger your career.Our Classroom
We’re doing this because:- We want you to be empowered with statistics.
- We believe everyone should get out of this course with awesome skills
- Real-time feedback promotes efficient learning
GAISE Connections
Our six recommendations include the following:- Emphasize statistical literacy and develop statistical thinking
- Use real data
- Stress conceptual understanding, rather than mere knowledge of procedures
- Foster active learning in the classroom
- Use technology for developing conceptual understanding and analyzing data
- Use assessments to improve and evaluate student learning
Learning without thought is labor lost. What I hear, I forget. What I see, I remember. What I do, I understand. – Confucius
Archive
Passion Driven Statistics (PDS) data
- Install PDS package.
- AddHealthW1 Sampling Design, Codebook, RData.
- AddHealthW4 Sampling Design, Codebook, RData.
- NESARC Sampling Design, Codebook, RData.
- OutlookOnLife Sampling Design, Codebook, RData.
- GapMinder Sampling Design, Codebook, RData.
Asking smart questions
- “Smart Questions” guide (note “hackers build things, crackers break them”)
- Follow this Rubric when emailing a question:
- Send a new email for each new question. Use “Reply” to continue a conversation on a question (do not start a new email, again).
- Include “ADA2” as the first word of the subject line in new emails (if replying, use reply), with the rest of the subject indicating the assignment and type of problem.
- Begin the email with a short question summary (that is, don’t bury your question in the middle of the third paragraph). Then, begin the detail of your question in the second paragraph.
- When possible, include commented code in the email body — Comments (starting with # symbol) should indicate where the problem is, what the expected behavior is, and what steps are necessary to reproduce the problem.
- Attach your qmd file so that the instructor can reproduce the problem. If attaching code, please include all the files necessary to run your code (data, etc.)
- [Attaching code supersedes this:
Code should include a “Minimum representative test case” (http://www.catb.org/esr/faqs/]smart-questions.html#code) - Assume the best. Your instructors want to help and we will do our best. Do not abuse your helpers even if you feel frustrated.
RMarkdown and knitr issues
- R errors, unresolved, and out of time If you’re saying: “An error while knitting keeps me from turning in the assignment…”, then use code chunk option
```{r, error = TRUE}
to ignore the error and continue. This will allow you to turn in partial assignments with errors.
- Unicode compile problems: If you knit to pdf you may get this error: “! Package inputenc Error: Unicode char”. ASCII is a small character set what we use to program in, Unicode is an extended character set that looks pretty (for example “straight quotes” become “curly quotes”) but causes code to break. You get unwanted Unicode when you copy/paste from a pdf or some other source into your code. To fix this, you have to find the Unicode and replace it with it’s ASCII equivalent. To do this: Ctrl-F to find, search for “[^\x00-\x7F]” (without quotes), select “Regex” for regular expressions, and find the “Next” one. As it finds instances, replace the characters manually until there are no more. These characters will typically be curly quotes or fancy dashes.
3/1/17 – Data resources for poster:
- List of 50+
- kaggle
- drivendata
- 538
- agridat package
- wise data sources
- statsci datasets
- vanderbilt datasets
Citing and using notes, including previous editions
Citing lecture notes: Erhardt EB, Bedrick EJ, and Schrader RM. (2020) Lecture notes for Advanced Data Analysis 2. Retrieved Mar 1, 2020, from statacumen.com/teach/ADA2/notes/ADA2_notes_S20.pdf, 136–144.- Notes from Spring 2020 using R with tidyverse: ADA2_notes_S20.pdf includes all chapters in one document.
Lecture notes for Advanced Data Analysis 2 (ADA2) Stat 428/528 University of New Mexico is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at https://statacumen.com/teach/ADA2/notes/ADA2_notes_S20.pdf. - Notes from Spring 2017 using R: ADA2_notes_S17.pdf
- Notes from Spring 2016 using R: ADA2_notes_S16.pdf
- Notes from Spring 2015 using R: ADA2_notes_S15.pdf
- Notes from Spring 2014 using R: ADA2_notes_S14.pdf
- Notes from Spring 2013 using R: ADA2_notes_S13.pdf
- Notes from Spring 2012 using SAS: ADA2_notes_S12.pdf