ADA1 F20

UNM Stat 427/527: Advanced Data Analysis I (ADA1)

Fall 2020 Syllabus is below tables
  • Fall 2020
  • Time: None/Always (Remote Arranged)
  • Location: Zoom
  • Stat 427.001, CRN 59508; Stat 527.001, CRN 59509
COVID-19 Year Remote Arranged: A fully remote course in which all components are delivered remotely and there are no set times for face-to-face or remote meetings. Coursework will be done remotely and your coursework for a given day or week, such as viewing lectures and completing modules, can be completed online within deadlines set by the instructor.

Step 0

Before our first “class” (Fri 8/21) please read through the following actions and install the required software on your computer.
  1. Install R (windows or mac) or upgrade, then RStudio.
  2. Install R packages, also update all packages within RStudio.
  3. Set up your computer
    1. RStudio disable notebook
    2. Operating system to be more friendly to programming.


This Is Statistics

Learn to produce beautiful (markdown) and reproducible (knitr) reports with informative plots (ggplot2) and tables (kable) by writing code (R, tidyverse, Rstudio) to answer questions using fundamental statistical methods (all one- and two-variable methods), which you’ll be proud to present (poster).

Course content

Weekly structure

(also see “Assessment” below)
  1. Preparation (Tuesday): Reading, Video, Quiz due Tue 11:59 PM.
  2. Worksheet 1 (Tuesday): Assignment due by Fri 11:59 PM.
  3. Worksheet 2 (Thursday): Assignment due by Mon 11:59 PM.

Course notes, code, data, and video lectures

Second text: PDS Textbook Notes from Fall 2019: ADA1_notes_F19.pdf includes all chapters in one document. Citing lecture notes example: Erhardt EB, Bedrick EJ, and Schrader RM. (2019) Lecture notes for Advanced Data Analysis 1. Retrieved Sep 1, 2019, from, 136–144. Creative Commons License Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 University of New Mexico is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at
Ch Chapter Title Notes R code Datasets Video lectures playlist Helper videos
00 Introduction to R, Rstudio, and ggplot pdf R 00-1 00-2 markdown, 01 PDS codebook, 01 HW codebook, 02 HW Lit review
01 Summarizing and Displaying Data pdf R 01-1 03 HW 03 subset
02 Estimation in One-Sample Problems pdf R 02-1 02-2 02-3
03 Two-Sample Inferences pdf R 03-1 03-2 03-3
04 Checking Assumptions pdf R 04-1
05 One-Way Analysis of Variance pdf R CHDS dat desc 05-1 (no videos recorded)
06 Nonparametric Methods pdf R 06-1 one-sample, 06-2 paired, 06-3 two-sample, 06-4 ANOVA, 06-5 perm test.
07 Categorical Data Analysis pdf R 07-1 intro, 07-2 single prop, 07-3 GOF-test, 07-4 two prop & cond prob, …
08 Correlation and Regression pdf R BodyMass dat desc pdf 08-1 corr/log, 08-2 corr hyp test, 08-3 LS reg eq, 08-4 08-5
09 Introduction to the Bootstrap pdf R 09-1
10 Power and Sample size pdf R 10-1
11 Data Cleaning pdf R 11-1 14 HW to poster
12 ADA2 Ch 11 Logistic Regression pdf R 12-1 12-2 12-3 12-4 Upgrading R on Windows
ada_functions.R function for a large set of standard diagnostic plots.

Passion-Driven Statistics (PDS) data

  • NESARC Sampling Design, Codebook, RData. Alcohol abuse and related conditions.
    • Unique ID “IDNUM”.
    • AGE is in the data but not in the codebook.


Date Class Topic Reading, Video, Quiz class Worksheet, Data
08/17 00 Install software, survey Step 0 (above)
08/18 01
08/20 02
  • RMarkdown
  • (Intro to using RMarkdown: Rmd html)
  • 01a Medical records Rmd html
  • Download Rmd file to your computer, open in RStudio, edit it, print HTML to pdf, turn in assignment by Monday midnight to UNM Learn.
  • Class 02, Medical Records (separate)
  • Due M 08/24
08/25 03 Codebook
  • Quiz due Thu 8/27, and “literature review” questions won’t be graded.
08/27 04
  • Class 04, Personal Codebook Rmd html
  • (Find this assignment contained the Outline below)
  • video: CL04
  • Due M 08/31
09/01 05 R programming, data subset and numerical summaries
  • ADA1 ALL Outline file Rmd html
  • Start using this Rmd file.
  • All of your assignments will be written in this file.
  • Read dataset in R, create subset of data, rename variables, numerical summaries.
  • Class 05, Data subset and numerical summaries
  • video: CL05a, CL05b, CL05c
  • Due F 09/04
09/03 06 Plotting univariate (Due date one day later for Labor Day)
09/08 07 Plotting bivariate, numeric response
  • Class 07, Plotting bivariate, numeric response
  • video: CL07a, CL07b
  • Due F 09/11
09/10 08 Plotting bivariate, categorical response
  • Class 08, Plotting bivariate, categorical response
  • video: CL08a, CL08b
  • Due M 09/14
09/15 09 Simple linear regression, intro
  • read: Ch 8.4, 8.2 R;
  • video: 08-1 corr/log, 08-3 LS reg eq;
  • quiz: quiz
  • Quiz 09, Simple linear regression, Logarithm transformation
  • Due T 09/15
  • Rmd html dat
  • Build intuition using SLR App, interpret properties of linear regression fit.
  • Class 09, Simple linear regression (separate)
  • video: CL09
  • Due F 09/18
09/17 10
  • Class 10, Simple linear regression
  • video: CL10
  • Due M 09/21
09/22 11 Logarithm transformation
  • Rmd html dat
  • Plot, transform, plot, and interpret.
  • video: CL11
  • Class 11, Logarithmic transformation, intro (separate)
  • Due F 09/25
09/24 12
  • Class 12, Logarithmic transformation
  • video: CL12
  • Due M 09/28
09/29 13 Correlation
10/01 14 Categorical contingency tables
10/06 15 Quiz 15, (NONE)
  • Class 15, Correlation and Categorical contingency tables
  • video: CL15a CL15b
  • Due M 10/12
10/08 Fall Break 1/2 (Wed 10/7) Spurious Correlations BBC Radio 4: More or Less, “sampling”, 9 min audio
10/13 16 Parameter estimation (one-sample)
  • read: Ch 2.1-2.2 R;
  • video: see table above;
  • quiz: quiz
  • Quiz 16, Inference and Parameter estimation
  • Due T 10/13
  • Rmd html
  • Class 16, Parameter estimation (one-sample) (separate)
  • video: CL16
  • Due F 10/16
10/15 17
  • Class 17, Inference and Parameter estimation (one-sample)
  • video: CL17a CL17b
  • Due M 10/19
10/20 18 Hypothesis testing (two-sample)
  • read: Ch 2.3-end RCh 3 R
  • video: see table above;
  • quiz: quiz
  • Quiz 18, Hypothesis testing
  • Due T 10/20
10/22 19 Paired data, assumption assessment
  • Rmd html dat
  • Class 19, Paired data, assumption assessment (separate)
  • video: CL19
  • Due M 10/26
10/27 20 ANOVA, post-hoc comparisons
  • read: Ch 2.2.1, Ch 3.4 & 3.6, Ch 4, Ch 5;
  • video: see table above;
  • quiz: quiz
  • Quiz 20, ANOVA, Pairwise comparisons
  • Due T 10/27
  • Class 20, Hypothesis testing (one- and two-sample)
  • video: CL20
  • Due F 10/30
10/29 21
  • Rmd html dat
  • Class 21, ANOVA, Pairwise comparisons (separate)
  • video: CL21
  • Due M 11/02
11/03 Fall Break 2/2 (Tue 11/03)
11/05 22 Quiz 22, (NONE)
  • Class 22, ANOVA and Assessing Assumptions
  • video: CL22
  • Due M 11/09
11/10 23 Nonparametric methods
  • read: Ch 6, Ch 7.2-7.4, Ch 10;
  • video: see table above;
  • quiz: quiz
  • Quiz 23, Nonparametric methods, Binomial and Multinomial tests
  • Due T 11/10
  • Rmd html
  • Class 23, Nonparametric methods (separate)
  • video: CL23
  • Due F 11/13
11/12 24 Binomial and multinomial proportion tests
  • Rmd html dat
  • Class 24, Binomial and Multinomial tests (separate)
  • video: CL24
  • Due M 11/16
11/17 25 Two-way categorical tables
  • read: Ch 7.8-end, Ch 8.5-8.7;
  • video: see table above;
  • quiz: quiz
  • Quiz 25, Two-way categorical tables
  • Due T 11/17
  • Rmd html dat
  • Class 25, Two-way categorical tables (separate)
  • video: CL25
  • Due F 11/20
11/19 26 Simple linear regression, inference
  • Rmd html dat
  • Regression of height vs hand span using data from our class.
  • Class 26, Simple linear regression (separate)
  • video: CL26
  • Due M 11/23
11/24 27 Quiz 27, (NONE)
  • Class 27, Two-way categorical and simple linear regression
  • video: CL27a CL27b
  • Due M 11/30
11/26 Thanksgiving break Summary of Methods we’ve covered
12/01 28 Logistic regression, intro
  • read: ADA2 Ch 11.1-3, 11.6, PDS Ch 16;
  • video: see table above;
  • quiz: quiz
  • Quiz 28, Logistic regression
  • Due T 12/01
  • Rmd html dat
  • Class 28, Logistic regression (separate)
  • video: CL28
  • Due F 12/04
12/03 29
  • Class 29, Logistic regression
  • video: CL29
  • Due M 12/07
  1. EvalKit course evaluation: print a pdf of your email confirmation that you’ve completed the EvaluationKIt Survey and upload that to UNM Learn.
12/06 Finals week (no final) Congratulations on a great semester!
(I reserve the right to continue to modify the schedule and improve the materials throughout the semester.)


  • Description: Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression, and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as R. Course cannot be counted in the hours needed for graduate degrees in Mathematics and Statistics.
  • Prerequisite: Math 1350 [Stat 145] (or other intro stats course)
  • Semesters offered: Fall
  • Lecture: Stat 427.001, CRN 59508; Stat 527.001, CRN 59509; TR 1530-1645; Location: Zoom
  • Email: Please include “ADA1” in the subject line of all emails.


  • Professor
    • Erik Erhardt <>, he/him
  • Teaching Assistants
    • Ola Anifowoshe <>, he/him
    • Jonathan Emery <>, he/him
  • Peer Learning Facilitators (PLF)
    • John Romero <>, he/him
    • Pratap Khattri <>, he/him
    • Coby Segay <>, he/him
    • Jacob Matthew Moya <>, he/him

Office hours

See email “ADA1, Stat 427/527, Announcements” from 8/23/20 for Zoom links and instructions.
Time Mon Tue Wed Thu Fri Sat Sun
8 AM
9 AM
10 AM JM
9 PM
  • We are also all available by appointment by email if these many hours do not work for you.

Student learning outcomes

At the end of the course, you will be able to: (student results: R, all years20152014, 20132012) General outcomes:
  1. Organize knowledge in graphs, tables, and code to support concise, comprehensible, and scientifically defensible written interpretations to produce knowledge within a reproducible research environment.
  2. Distinguish a testable scientific hypothesis or data-supported interpretation from an opinion.
  3. Understand from a data story the goals of the study and apply the correct statistical procedure.
  4. Explain the scientific aspects of a problem to nonscientists in a fashion that enhances understanding and decision making.
Topical outcomes:
  1. Define parameters of interest and hypotheses in words and notation.
  2. Summarize data visually, numerically, and descriptively and interpret the observed characteristics. Calculate and interpret numerical summaries such as mean, variance, five-number summary, confidence intervals, and p-values, and create visual summaries such as bar plots, scatter plots, and histograms. (Never pie charts!)
  3. Distinguish between statistical significance and scientific relevance.
  4. Use statistical software, such as R, to read and manage data, create informative plots, report numerical summaries, and apply statistical models, by recommended programming practice including abstraction and documentation.
  5. Understand the differences and limitations of controlled experiments and observational studies. Design experiments to infer causal treatment effects. Analyze observational data to infer associations between measured variables.
  6. Identify and explain the statistical methods, assumptions, and limitations used in reported studies in scientific literature or popular media.
  7. Evaluate and criticize published studies, the work of peers, and your own work and assess what was done well, what could be done better, and examine whether their conclusions are supported using statistical principles.
  8. Make evidence-based decisions by constructing and deciding between testable hypotheses using appropriate data and methods.
  9. Discover relationships and make predictions through model development and selection.

Meeting the learning outcomes

You will acquire new information in this class, but the emphasis is comprehending, integrating, and applying information. Rote factual memorization is the lowest form of learning. Effective learning takes place by explaining, integrating, applying, and analyzing facts, hypotheses, and theories. Learning in this class occurs by:
  1. Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.
  2. Discussion – interaction with classmates to assemble and synthesize information utilizing the collective skills and knowledge base of the group.
  3. Listening, acting, and reflecting – activities during class time provide insights into information not available in readings and includes review difficult material to aid comprehension. Note-taking permits later reflection on lecture content. Listening to the professor lecture is the least effective learning tool for both students, however, and you should plan on coming to every class prepared to participate in active and reflective learning opportunities.


  • Quizzes will be due each Tuesday before class.  Purpose: to assess reading and video comprehension and assure you’re prepared to actively participate in class activities with minimal lecture. (About 12, 20% 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 Learn 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 24, includes class participation, 78% of final grade) Most weeks plan to finish in class.
  • Course surveys are due at the end of the course (EvalKit).  (About 1, 2% of final grade.)
  • 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.
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 Learn.  For all submissions: (1) In RMarkdown, knit Rmd file to HTML, (2) Open HTML file in your internet browser, (3) Print HTML to pdf file, (4) Submit pdf to UNM Learn. Browser choice: Chrome is the best browser choice.  On a Mac, Safari adds “.txt” to RMarkdown files when downloaded, and Firefox sometimes fails on upload of a pdf to UNM Learn. 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 RMarkdown 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 submit 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 me or the TAs. 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 in as a comment (which is doubly helpful for finding the resource again).  You won’t be the first person to do anything in this class, so give credit where it’s due.



In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. It is imperative that you take the initiative to bring such needs to the instructor’s attention, as I am not legally permitted to inquire. Students who may require assistance in emergency evacuations should contact the instructor as to the most appropriate procedures to follow. Contact Accessibility Resource Center at 277-3506 for additional information. 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 or by phone 277-3506.


This is a three-credit-hour course. Class meets for two 75-minute sessions of direct instruction for fifteen weeks during the semester. Students are expected to complete a minimum of six hours of out-of-class work (or homework, study, assignment completion, and class preparation) each week.

Title IX statement

In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees” by the Department of Education (see page 15 of requires that any report of gender discrimination that includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity ( For more information on the campus policy regarding sexual misconduct, see:

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:

Support in Receiving Help and in Doing What is Right

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 and to find your place at UNM, see or ask me for information about the right resource center or person to contact. UNM has important policies to preserve and protect the academic community, especially policies on student grievances (Faculty Handbook D175 and D176), academic dishonesty (FH D100), and respectful campus (FH CO9). These are in the Student Pathfinder ( and the Faculty Handbook ( Please ask for help in understanding and avoiding plagiarism or academic dishonesty, which can both have very serious disciplinary consequences.

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.

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 to take responsibility for my learning.”

GAISE Connections

Our six recommendations include the following:
  1. Teach statistical thinking.
    • Teach statistics as an investigative process of problem-solving and decision making.
    • Give students experience with multivariable thinking.
  2. Focus on conceptual understanding.
  3. Integrate real data with a context and purpose.
  4. Foster active learning.
  5. Use technology to explore concepts and analyze 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



Pre-course to-dos

Did you receive a registration error for Fall 2020? 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.  

Course introduction materials


Problems installing PDS package?  Solution. If you had problems installing the PDS package, no problem; here’s how to get the data: 1. Download the “.RData” file above for your dataset. 2. Where I have “library(PDS)” in my code, change it to the two lines below.  You’ll need to update the “PATH_TO_FILE” below to the path on your computer’s hard drive, and “filename” needs to be changed to the name of the file. This will directly read the data file.

# library(PDS)
  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.

Acumen in Statistics