UNM Stat 427/527: Advanced Data Analysis I (ADA1)
Table of Contents
- Fall 2022
- Tuesday/Thursday: 9:30-10:45 AM at CTLB 300 map
- Stat 427.001, CRN 59508; Stat 527.001, CRN 59509
Goal
Learn to produce beautiful (markdown) and reproducible (knitr/quarto) 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)- Preparation (Tuesday): Reading, Video, Quiz due Tue 11:50 PM.
- Worksheet 1 (Tuesday): Assignment due by Fri 11:50 PM.
- Worksheet 2 (Thursday): Assignment due by Mon 11:50 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 statacumen.com/teach/ADA1/notes/ADA1_notes.pdf, 136–144.
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 https://statacumen.com/teach/ADA1/notes/ADA1_notes_F19.pdf.
| Ch | Chapter Title | Notes | R code | Video lectures playlist |
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| 00 | Introduction to R, Rstudio, and ggplot | |||
| 01 | Summarizing and Displaying Data |
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| 02 | Estimation in One-Sample Problems | |||
| 03 | Two-Sample Inferences | |||
| 04 | Checking Assumptions | |||
| 05 | One-Way Analysis of Variance |
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| 06 | Nonparametric Methods | |||
| 07 | Categorical Data Analysis | |||
| 08 | Correlation and Regression |
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| 09 | Introduction to the Bootstrap |
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| 10 | Power and Sample size | |||
| 11 | Data Cleaning (not used this semester, ADA2 Ch 11 refers to Ch 12 below) |
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| 12 | ADA2 Ch 11 Logistic Regression |
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.
- UNM Canvas for taking quizzes (graded automatically) and submitting assignments (evaluated by TA within 1 week).
- After uploading a pdf assignment, verify with a preview of the file.
- Lectures: YouTube Video playlist (try 1.5 speed, then pause as needed).
- Assignments: YouTube Video playlist
- RStudio cheatsheets
- Erik’s example homework document: NESARC data, nicotine and depression.
Timetable
| Date | Class | Topic | Reading, Video, Quiz | class Worksheet, Data |
|---|---|---|---|---|
| 08/22 | 00 | Install software | Step 0 (above) | (Video numbers may be slightly different from the class number.) |
| 08/23 Tue | 01 | |||
| 08/25 Thu | 02 | Codebook |
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| 08/30 Tue | 03 | R programming, data subset and numerical summaries | ||
| 09/01 Thu | 04 | Plotting univariate | ||
| 09/06 Tue | 05 | Plotting bivariate, numeric response, categorical response | ||
| 09/08 Thu | 06 | Figure and text finer points | (Related: MS Word cross-references, citations, and special topics video) |
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| 09/13 Tue | 07 | Simple linear regression, intro |
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| 09/15 Thu | 08 | Logarithm transformation |
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| 09/20 Tue | 09 | Correlation |
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| 09/22 Thu | 10 | Categorical contingency tables |
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| 09/27 Tue | 11 | Parameter estimation (one-sample) |
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| 09/29 Thu | 12 | |||
| 10/04 Tue | 13 | Hypothesis testing (two-sample) |
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| 10/06 Thu | 14 | Paired data, assumption assessment | ||
| 10/11 Tue | 15 |
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| 10/13 Thu | Fall Break | Spurious Correlations | BBC Radio 4: More or Less, “sampling”, 9 min audio | |
| 10/18 Tue | 16 | ANOVA, post-hoc comparisons |
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| 10/20 Thu | 17 |
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| 10/25 Tue | 18 | Nonparametric methods |
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| 10/27 Thu | 19 | Binomial and multinomial proportion tests | ||
| 11/01 Tue | 20 | Two-way categorical tables, simple linear regression, inference |
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| 11/03 Thu | 21 | |||
| 11/08 Tue | 22 | Logistic regression, intro | ||
| 11/10 Thu | 23 | Summary of Methods we’ve covered |
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| 11/15 Tue | 24 | Poster Preparation: research questions, data sources, analyses |
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| 11/17 Thu | 25 | Poster Preparation: literature review, references, discussion, future work |
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| 11/22 Tue | 26 | Poster Preparation: complete content | ||
| 11/24 Thu | Thanksgiving break | |||
| 11/29 Tue | 27 | Poster Preparation: into poster template |
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| 12/01 Thu | Poster Preparation: reviewed by an instructor | |||
| 12/06 Tue | 28 | Poster Presentations: Graduate students |
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| 12/08 Thu | 29 | Poster Presentations: Undergraduate students |
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| 12/13 Tue | Finals week | (no final) | Congratulations on a great semester! |
Syllabus
- 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, do not send messages via UNM Canvas.
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)
- Arwyn Lewis, she/her
- Alexis P Amodio-Cardwell, she/her
Office hours
See email “ADA1, Stat 427/527, Announcements” from 8/26/22 for Zoom links and instructions. UNM Authentication instructions.| Time | Mon | Tue | Wed | Thu | Fri | Sat | Sun |
| 8 AM | |||||||
| 9 AM | BF | Class | BF | Class | |||
| 10 AM | EE | Class | ML | Class | EE | ||
| 11 AM | EE | EE | ML | EE | EE | ||
| 12 PM | BF | BF | |||||
| 1 PM | |||||||
| 2 PM | ML | ||||||
| 3 PM | ML | ||||||
| 4 PM | BF | ||||||
| 5 PM | ML | BF | |||||
| 6 PM | ML | ||||||
| 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
At the end of the course, you will be able to: (student results: R, all years, 2015, 2014, 2013, 2012) General outcomes:
- Organize knowledge in graphs, tables, and code to support concise, comprehensible, and scientifically defensible written interpretations to produce knowledge within a reproducible research environment.
- Distinguish a testable scientific hypothesis or data-supported interpretation from an opinion.
- Understand from a data story the goals of the study and apply the correct statistical procedure.
- Explain the scientific aspects of a problem to nonscientists in a fashion that enhances understanding and decision making.
- Define parameters of interest and hypotheses in words and notation.
- 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!)
- Distinguish between statistical significance and scientific relevance.
- 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.
- 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.
- Identify and explain the statistical methods, assumptions, and limitations used in reported studies in scientific literature or popular media.
- 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.
- Make evidence-based decisions by constructing and deciding between testable hypotheses using appropriate data and methods.
- 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 on comprehending, integrating, and applying information. Rote factual memorization is the lowest form of learning. Effective learning occurs by explaining, integrating, applying, and analyzing facts, hypotheses, and theories. Learning in this class occurs by:- Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.
- Discussion – interaction with classmates to assemble and synthesize information utilizing the collective skills and knowledge base of the group.
- 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.
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, 12% 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.
- Viewing quiz solutions after the due date in UNM Canvas
- Worksheet assignments. Purpose: to struggle and find success in class with the concepts and skills. (About 31, includes class participation, 70% 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).
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.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 email me; 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 us know that you need support so that we can connect you to the right resources and please be aware that UNM will publish information on websites and email about any changes to our public health status and community response.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 my email or in office 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 three 50-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.Title IX statement
In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees.” This designation requires that any report of gender discrimination which 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 (oeo.unm.edu). For more information on the campus policy regarding sexual misconduct, see: https://policy.unm.edu/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.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 and equitably. 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 (https://pathfinder.unm.edu) and the Faculty Handbook (https://handbook.unm.edu). Please ask for help in understanding and avoiding plagiarism or academic dishonesty, which can both have very serious consequences.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 including 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:- Teach statistical thinking.
- Teach statistics as an investigative process of problem-solving and decision-making.
- Give students experience with multivariable thinking.
- Focus on conceptual understanding.
- Integrate real data with a context and purpose.
- Foster active learning.
- Use technology to explore concepts and analyze 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
Pre-course to-dos
Did you receive a registration error for Fall 2020? 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)?
Step 0
Before our first “class” (Mon 8/22) please read through the following actions and install the required software on your computer.- 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).
- Install erikmisc package (also at the end of “Install R packages”, above).
- Submit these the two lines to the R console:
install.packages("devtools")devtools::install_github("erikerhardt/erikmisc")- If it asks to update packages (it should not ask this if you updated packages above), press 3 [Enter] for “None”.
- If asks about “make” command, click “Cancel”.
- If asks about “git” command, click “Cancel”.
- Make sure it works by printing the logo:
library(erikmisc)erikmisc_logo()
- Submit these the two lines to the R console:
- Set up your computer
- RStudio disable notebook
- Operating system to be more friendly to programming.
type="binary" option.
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.
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)
setwd("/PATH_TO_FILE")
load("filename.RData")
Unicode compile problems: If you render 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.