ADA2 S23

UNM Stat 428/528: Advanced Data Analysis II (ADA2)

  Spring 2023 The syllabus is below the tables.
  • Spring 2023
  • Time: 9:30-10:45 AM
  • Location: CTLB 300
  • Stat 428.001, CRN 33933; Stat 528.001, CRN 33935

Goal

Learn to produce beautiful (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).

This is Statistics

Pre-course to-dos

Did you receive a registration error for Spring 2023? Send me an email with the following answers:
  1. What registration error did you get (copy/paste is best)?
  2. What is your UNM ID?
  3. What is your Math/Stat background (that is, do you have the prerequisites)?
If you are waitlisted, as long as there are seats available, I will override you into the course. Don’t worry.

Step 0

Before our first “class” (Mon 1/16/23) please read through the following actions and install the required software on your computer.
  1. Install:
    1. R (windows or mac) or upgrade (Install R video (5 min)),
    2. RStudio, and
    3. Quarto.
  2. Install R packages.
    1. Follow these instructions: R packages.  (Ignore warning about rtools or any packages unavailable.)
    2. In RStudio, open Packages tab, click on “Update”, Select All, Install Updates (“No” to restart, “No” to compile from source).
  3. Install erikmisc package (also at the end of “Install R packages”, above).
    1. Submit these the two lines to the R console:
      1. install.packages("devtools")
      2. devtools::install_github("erikerhardt/erikmisc")
        1. If it asks to update packages (it should not ask this if you updated packages above), press 3 [Enter] for “None”.
        2. If asks about “make” command, click “Cancel”.
        3. If asks about “git” command, click “Cancel”.
    2. Make sure it works by printing the logo:
      1. library(erikmisc)
      2. erikmisc_logo()
  4. Set up your computer
    1. RStudio disable notebook
    2. Operating system to be more friendly to programming.
If you have a Chromebook or no laptop, consider using RStudio Cloud > Individuals,  and when installing packages remove the type="binary" option.

Course content

Weekly structure

(also see “Assessment” below)
  1. Preparation (Tuesday): Reading, Video, Quiz due Tue 9:30 AM (before class).
  2. Worksheet 1 (Tuesday): Assignment due by Fri 11:50 PM.
  3. Worksheet 2 (Thursday): Assignment due by Mon 11:50 PM.
  4. This is the typical schedule; some dates may differ depending on the circumstance.

Course notes, code, data, and video lectures

Notes from Spring 2020: ADA2_notes_S20.pdf includes all chapters in one document. Creative Commons License 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.
Ch Chapter Title Notes R code Datasets Video lectures playlist
01 R statistical software and review pdf R turkey.csv, rocket.dat (Videos based on S16 notes) 01-1, 01-2
02 Introduction to Multiple Linear Regression pdf R indian.dat, gce.dat 02-1, 02-2
03 A Taste of Model Selection for Multiple Regression pdf R ratliver.csv 03-1, 03-2
04 One Factor Designs and Extensions pdf R none 04
05 Paired Experiments and Randomized Block Experiments pdf R battery.dat, beetles.dat, itch.csv, ratinsulin.dat 05-0 05-1 05-2 05-3 05-4 05-5 05-6 05-7 05-8 05-9
06 A Short Discussion of Observational Studies pdf R sat.csv 06
07 Analysis of Covariance: Comparing Regression Lines pdf R tools.dat, toolsfake.dat, twins.dat 07-1 07-2 07-3 HW helper video
08 Polynomial Regression pdf R cloudpoint.dat, mooney.dat 08-1 08-2
09 Discussion of Response Models with Factors and Predictors pdf R faculty.dat 09-1 09-2 09-3
10 Automated Model Selection for Multiple Regression pdf R oxygen.dat 10-1 10-2 10-3
11 Logistic Regression pdf R beetles.dat, leuk.dat, menarche.csv, shuttle.csv, trauma.dat 11-1 11-2 11-3 11-4
12 An Introduction to Multivariate Methods pdf R none 12
13 Principal Component Analysis pdf R bgs.dat, shells.dat, sparrows.dat, temperature.dat 13-1 13-2 13-3
14 Cluster Analysis pdf R birthdeath.dat, teeth.dat 14-1 14-2 14-3
15 Multivariate Analysis of Variance pdf R shells_mf.dat 15
16 Discriminant Analysis pdf R mower.dat 16-1 16-2
17 Classification pdf R business.dat 17-1 17-2 17-3
18 Data Cleaning pdf R conversions.txt, dalton.txt, dirty_iris.csv, edits.txt, people.txt, unnamed.txt
 

(I reserve the right to continue to improve the materials throughout the semester.)

Timetable

Date Class Topic Reading, Video, Quiz class Worksheet, Data
01/16 00 Install software
  • See Step 0
  • video: S21 Intro (similar for S23, except we’re face-to-face in class and probably no assignment preview videos)
01/17 01 01 R statistical software and review
  • read: Ch 01
  • video: 01-1, 01-2
  • quiz: 01 (uses worksheet data)
  • Due M 01/23 (11:50 PM)
01/19 02
  • continued
01/24 03 02 Introduction to Multiple Linear Regression
  • read: Ch 02
  • video: 02-1, 02-2
  • quiz: 03
  • Due T 01/24 (9:30 AM for all remaining)
  • qmd html dat
  • Class 03, Ch 02 Introduction to Multiple Linear Regression
  • video: CL03
  • Due F 01/27
01/26 04
  • qmd html dat
  • Class 04, Ch 02 Introduction to Multiple Linear Regression
  • video: CL04
  • Due M 01/30
01/31 05 03 A Taste of Model Selection for Multiple Linear Regression
  • read: Ch 03, 04
  • video: 03-1, 03-2, 04
  • quiz: 05a, 05b
  • Due T 01/31
  • qmd html dat
  • Class 05, Ch 03 A Taste of Model Selection for Multiple Regression
  • video:  CL05
  • Due F 02/03
02/02 06 04 Experimental Design: One and Two Factor Designs
  • qmd html dat
  • Class 06, Ch 03 A Taste of Model Selection for Multiple Regression
  • video: CL06
  • Due M 02/06
02/07 07 05 Paired Experiments and Randomized Block Designs
  • qmd html
  • Class 07, Ch 05a Paired Experiments and Randomized Block Experiments: Randomized complete block design (RCBD)
  • video: CL07
  • Due F 02/10
02/09 08
  • qmd html dat
  • Class 08, Ch 05a Paired Experiments and Randomized Block Experiments
  • video: CL08
  • Due M 02/13
02/14 09
  • qmd html dat
  • Class 09, Ch 05b Paired Experiments and Randomized Block Experiments: Two-way Factor design
  • video: CL09
  • Due F 02/17
02/16 10
  • qmd html dat
  • Class 10, Ch 05b Paired Experiments and Randomized Block Experiments: Two-way Factor design
  • video: CL10
  • Due M 02/20
02/21 11 06 Discussion of Observational Studies
  • read: Ch 06-07
  • video:  06 07-1 07-2 07-3
  • quiz: 11a, 11b
  • Due T 02/21
  • qmd HTML dat1 dat2
  • Class 11, Chs 05 and 07, writing and plotting model equations
  • video: CL11
  • Due F 02/24
02/23 12 07 Analysis of Covariance: Comparing Regression Lines
  • qmd html dat
  • Class 12, Ch 07a, Analysis of Covariance: Comparing Regression Lines
  • video: CL12
  • Due M 02/27
02/28 13 08 Polynomial Regression
  • qmd html dat
  • Class 13, Ch 08, polynomial regression
  • video: CL13
  • Due F 03/03
03/02 14 09 Response Models with Factors and Predictors
03/07 15 10 Model Selection for Multiple Regression
03/09 16 Look ahead to Class 25 regarding the poster presentation.  Start looking for data and an answerable research question.
03/14 Spring Break
03/16 Spring Break
03/21 17 11 Logistic Regression
  • qmd html dat
  • Class 17, Ch 11, Logistic Regression
  • video: CL17
  • Due F 03/24
03/23 18
  • qmd html dat
  • Class 18, Ch 11, Logistic Regression
  • video: CL18
  • Due M 03/27
03/28 19 12 An Introduction to Multivariate Methods 13 Principal Components Analysis (PCA)
  • read: Ch 12, Ch 13
  • video: 12 13-1 13-2 13-3
  • quiz: 19a, 19b
  • Due T 03/28
  • qmd html dat
  • Class 19, Ch 13, Principal Components Analysis (PCA)
  • video: CL19
  • Due F 03/31
03/30 20 PCA, continued
  • qmd html dat
  • Class 20, Ch 13, Principal Components Analysis (PCA)
  • video: CL20
  • Due M 04/03
04/04 21 14 Cluster Analysis 15 Multivariate Analysis of Variance (MANOVA)
  • read: Ch 14, Ch 15
  • video:  14-1 14-2 14-3  15
  • quiz: 21a, 21b
  • Due T 04/04
 
04/06 22
  • qmd html dat
  • Class 22, Ch 15, Multivariate Analysis of Variance (MANOVA)
  • video: CL22
  • Due M 04/10
04/11 23 16 Discriminant Analysis 17 Classification
  • qmd html dat
  • Class 23, Chs 16+17, Discrimination for Classification
  • video: CL23
  • Due M 04/17
04/13 24
04/18 25 13+11+17 PCA and logistic regression classification
  • qmd html dat
  • Class 25, Chs 13+11+17, PCA and Logistic Regression for Classification
  • video: CL25
  • Due M 04/24
04/20 26
04/25 27 10 Model Selection for Multiple Regression, revisited:  ATUS data subset and model selection
  • qmd html dat
  • Class 27, ATUS data subset and model selection
  • video: CL27
  • Due M 05/01
04/27 28
05/02 29 ONE MORE BIG ASSIGNMENT
  • qmd html dat
  • Class 29, TBA
  • video: CL29
  • Due M 05/08
05/04 30
  1. EvalKit course evaluation: print a pdf of your email confirmation that you’ve completed the EvaluationKIt Survey and upload that to UNM Canvas. (Due M 05/08)
  2. PDS Wesleyan U Qualtrics survey (email), no receipt required
05/09 FINALS WEEK (no final) Congratulations on a great 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 428/528.001 (CRN 33933 or 33935) (TR 0930-1045, CTLB 300 Video)
  • Email: Please include “ADA2” in the subject line of all emails.

Instructors

  • Professor
    • Erik Erhardt <erike@stat.unm.edu>, he/him
  • Teaching Assistants
    • Behzad FallahiFard <bfallahifard@unm.edu>, he/him
    • Mingyue Liu <mingyueliu@unm.edu>, she/her
  • Peer Learning Facilitators (PLF)
    • Alexis P Amodio-Cardwell, she/her

Office hours

See email “ADA2, Stat 428/528, Announcements” from 1/14/23 for Zoom links and instructions.
Time Mon Tue Wed Thu Fri Sat Sun
8 AM
9 AM Class Class
10 AM BF Class BF Class
11 AM BF EE EE
12 PM
1 PM
2 PM EE EE
3 PM EE 3:30 ML EE 3:30
4 PM ML ML
5 PM ML BF
6 PM ML BF
7 PM
8 PM
9 PM
  • We are also all available by appointment by email if these many hours do not work for you.

Student learning outcomes

Similar to ADA1, but at a higher level.

Assessment

  • Quizzes will be due each Tuesday before class (for fully face-to-face semesters).  Purpose: to assess reading and video comprehension and assure you’re prepared to actively participate in class 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 for grade calculation.
    • Viewing quiz solutions after the due date in UNM Canvas is not intuitive.  Click on the “Begin” button (this is the non-intuitive part since you are not actually beginning the quiz), then click “View All Attempts” to see the scores.  Finally, click the score in the “Calculated Grade” column to see the feedback for each question of the quiz.
  • Worksheet assignments.  Purpose: to struggle and find success in class with the concepts and skills. (About 21, includes class participation, 83% of final grade) Most weeks plan to finish in class.
  • Poster will be developed through semester (most assignments contribute to poster), the last couple weeks we’ll complete them, and the last week we’ll have poster presentations. Purpose: to have an overarching set of questions to answer using methods learned in the course, with a deliverable you can be proud of! (1 poster and presentation, 12% poster, 2% presentation, and 2% evaluations of others of final grade.)  In the last couple weeks, assembling this poster may take 5-10 hours, using a template provided to you.
  • Course surveys are due at the end of the course (EvalKit).  (About 2, 2% of final grade.)
  • Roughly speaking, the lowest 2-weeks worth of assignments are dropped, so your lowest 2 quizzes and 4 worksheet assignments are not included in the calculation of your grade (this could include a worksheet assignment that spanned a full week).
Final grade may include a small buffer at the discretion of the instructor. For example, final grade could be the total points earned divided by the total possible points times 0.98 for graduate students and 0.95 for undergraduate students. That is [Final Grade] = [Points Earned]/[Points possible * 0.95] so that your grade is slightly higher than you earned. 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. Late assignments will not be accepted. 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. To be registered or employed at UNM, Students, faculty, and staff must all meet UNM’s Administrative Mandate on Required COVID-19 vaccination. If you are experiencing COVID-19 symptoms, please do not come to class. If you have a positive COVID-19 test, please stay home for five days and isolate yourself from others, per the Centers for Disease Control (CDC) guidelines. If you do need to stay home, please communicate with me by email; I can work with you to provide alternatives for course participation and completion. UNM faculty and staff know that these are challenging times. Please 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.

Accommodations

UNM is committed to providing equitable access to learning opportunities for students with documented disabilities. As your instructor, it is my objective to facilitate an inclusive classroom setting, in which students have full access and opportunity to participate. To engage in a confidential conversation about the process for requesting reasonable accommodations for this class and/or program, please contact Accessibility Resource Center at arcsrvs@unm.edu or by phone at 505-277-3506. Support: Contact me at by email or in office/check-in hours and contact Accessibility Resource Center (https://arc.unm.edu/) at arcsrvs@unm.edu (505) 277-3506.

Credit-hours

This is a three-credit-hour course. Class meets for two 65-minute sessions of direct instruction for fifteen weeks during the Fall 2022 semester. Please plan for a minimum of six hours of out-of-class work (or homework, study, assignment completion, and class preparation) each week. Support: Center for Academic Program Support (CAPS). Many students have found that time management workshops can help them meet their goals (consult (CAPS) website under “services”).

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.

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 be incorrect or misleading. 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 involvedmental health resourcesacademic support including tutoringresource 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
“It encourages me to engage actively with the course material and take responsibility for my learning.”

GAISE Connections

Our six recommendations include the following:
  1. Emphasize statistical literacy and develop statistical thinking
  2. Use real data
  3. Stress conceptual understanding, rather than mere knowledge of procedures
  4. Foster active learning in the classroom
  5. Use technology for developing conceptual understanding and analyzing data
  6. 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

Old news

Step 0

Before our first class (Tue 1/21) please read through the following actions and install the required software on your computer and complete the brief survey. If you don’t have a computer, there are classroom computers which will be available only when the classroom is open. Video for this process (ignore the “crowdgrader” portion).
  1. Complete surveys
    1. a short Opinio pre-survey required for classroom assessment (1/20 – 2/1/2020).
  2. Install R (windows or mac) or upgrade, then Rstudio. Videos that may be helpful:
  3. Install R packages,
    1. Run RStudio
    2. Run code in R packages.
    3. Update all packages, RStudio Packages tab, click “update”, click “select all”, and “Install Updates”. Say “Yes” to restart R, but if it asks a second time, say “No”.  Say “No” to “install from sources” if it asks.
  4. Set up your computer
    1. RStudio disable notebook
    2. Operating system to be more friendly to programming.
  5. (Postpone until later: Install LaTeX (for poster at end of the semester).)

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.

Pre-course to-dos

Did you receive a registration error for Spring 2023? Send me an email with the following answers:
  1. What registration error did you get (copy/paste is best)?
  2. What is your UNM ID?
  3. What is your Math/Stat background (that is, do you have the pre-requisites)?
If you are waitlisted, as long as there are seats available I will override you into the course. Don’t worry.
3/1/17 – Data resources for poster:

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.

Acumen in Statistics