ADA2 S20

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

Spring 2020 Syllabus follows the timetable below.

Spring 2020 schedule; Time: TR 1530-1645; Location: CTLB 300; Stat 428.001, CRN 33933; Stat 528.001, CRN 33935


Goal

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 (multiple regression, analysis of covariance, logistic regression, and multivariate methods), which you’ll be proud to present (poster).


News

Use the new COVID-19 Timetable near the top of this page for the rest of the semester assignments.




COVID-19 Timetable

The rest of the semester will follow this schedule.  Please ignore the original timetable further down.

Changes:

  • No poster project or presentation.  No final project.
  • Remove MANOVA assignment (24), but keep reading and quiz content.
  • Quizzes still due Tuesday, but by midnight.  (Extra time the first week, until Thursday.)
  • Spread all assignments out, one assignment per week (instead of two).  Each assignment “starts” on Tuesday with a due date of the following Monday at midnight.
  • Instructor and TA assistance will be via Zoom remote computer conferencing (more details below timetable).
  • Video COVID-19 introduction.  A great agent-based modeling of COVID-19 disease spread.

YouTube Playlist for assignment introductions

Date Cl Topic Reading, Video, Quiz In-class Worksheet, Data Homework
03/24 19 11 Logistic Regression read: Ch 11
video: 11-1 11-2 11-3 11-4
quiz: 10
Due: Thu 3/26 11:59PM
In-class: Rmd html dat
video
Due: Mon 3/30 11:59PM
03/26 20
03/31 21 HW:
20 Logistic Regression
Rmd html dat
video
Due: Mon 4/6 11:59PM
04/02 22
04/07 23 12 An Introduction to Multivariate Methods read: Ch 12-13
video: 12 13-1 13-2 13-3
quiz: 11 (2 parts)
Due: Tue 4/7 11:59PM
In-class: Rmd html dat
Due: Mon 4/13 11:59PM
04/09 24
04/14 25 13 Principal Components Analysis (PCA) HW:
22 PCA
Rmd html dat
Due: Mon 4/20 11:59PM
04/16 26
04/21 27 14 Cluster Analysis read: Ch 14-15
video: 14-1 14-2 14-3 15
quiz: 12 (2 parts)
Due: Tue 4/21 11:59PM
In-class:
Clustering
Rmd html dat
Due: Mon 4/27 11:59PM
04/23 28
04/28 29 16 Discriminant Analysis
17 Classification
read: Ch 16-17
video: 16-1 16-2 17-1 17-2 17-3
quiz: 13 (2 parts)
Due: Tue 4/28 11:59PM
In-class:
Discriminant analysis for classification
Rmd html dat
Due: Mon 5/3 11:59PM
04/30 30
05/05 31 13+11+17 PCA and logistic regression classification HW:
26+22+28 PCA and logistic Classification
Rmd html dat
Due: Mon 5/11 11:59PM
05/07 32
05/12 FINALS WEEK (no final) Surveys Due — submit receipt or confirmation page to UNM Learn
* Learning Studio
* EvalKit in Learn
(no poster)

COVID-19 Instructor support

Instructors
Erik Erhardt <erike@stat.unm.edu>, he/him, Zoom
Leah Puglisi <lhpuglisi@unm.edu>, she/her, Zoom
Ola Anifowoshe <oanifowoshe@unm.edu>, he/him, Zoom
Mohammad Ahmadi <mahmadi@unm.edu>, he/him, Zoom

Procedure for online meetings via Zoom
Click on Zoom link (within email: “ADA2 Zoom personal meeting rooms”) to connect to the instructor’s Personal Meeting Room.  If prompted, download and install the Zoom client for your computer and let it run. Be prepared to share your screen with the instructor, either just your RStudio window or your desktop.  If someone else is already in a meeting with the instructor, then you’ll be asked to be put on hold (into the “waiting room”) and you’ll be helped in the order that you called in.

Times
Mon: 12-3p Ola, 1-3p Leah, 3-5p Erik,
Tue: 10a-12p Leah, 12-2p Mohammad, 2-4p Erik
Wed: 9a-12p Ola, 1-3p Leah, 2-5p Mohammad
Thu: 10a-12p Mohammad, 10a-12p Leah, 12-2p Ola, 2-4p Erik
Fri: 10a-12p Ola, 10a-12p Leah, 12-2p Erik, 2-5p Mohammad
Sat: NA
Sun: NA




Course content

Weekly schedule (also see Assessment below)

  1. Pre-class (pre-Tuesday): Reading, Video, Quiz (due before Tue class — solutions become available Tue 3:30 pm, after quiz is due)
  2. In-class (Tue): Worksheet started in class Tuesday submitted to UNM Learn by Wed 11:59 pm.
  3. In-class (Thu): Homework started in class Thursday submitted to UNM Learn by the next Thu 3:30 pm.

UNM Learn for submitting assignments, YouTube Video playlist (try 1.5 speed, then pause/rewatch as needed).
Video: Upgrading R on Windows.


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.dat 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

ada_functions.R function for a large set of standard diagnostic plots


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

Timetable (OLD, do not use after 3/22)

Date Cl Topic Reading, Video, Quiz In-class Worksheet, Data Homework
01/10 00 Install software See Step 0
video: 00
01/21 01 01 R, Review read: Ch 01
video: 01-1, 01-2
01/23 02 In-class quiz In-class:
02 R Review Rmd html dat
Videos: 1, 2, 3
No HW 02
01/28 03 02 Introduction to Multiple Linear Regression read: Ch 02
video: 02-1, 02-2
quiz: 02
In-class: Rmd html dat
Submit pdf with solutions by Wed 5pm.
01/30 04 HW:
04 Mult LR
Rmd html dat
Submit your pdf to UNM Learn.
02/04 05 03 A Taste of Model Selection for Multiple Linear Regression read: Ch 03, 04
video: 03-1, 03-2, 04
quiz: 03 (2 parts)
In-class: Rmd html dat
02/06 06 04 Experimental Design: One and Two Factor Designs HW:
06 Taste Model Sel
Rmd html dat
02/11 07 05 Paired Experiments and Randomized Block Designs read: Ch 05 (start – 5.2)
video: 05-0 05-1 05-2 05-3 05-4 05-5
quiz: 04
In-class: Rmd html
02/13 08 HW:
08 Experiments 1
Rmd html
02/18 09 read: Ch 05 (5.3 – end)
video: 05-6 05-7 05-8 05-9
quiz: 05
In-class: Rmd html dat
02/20 10 HW:
10 Experiments 2
Rmd html dat
02/25 11 06 Discussion of Observational Studies read: Ch 06-07
video: 06 07-1 07-2 07-3
quiz: 06 (2 parts)
In-class: html
turn in paper version
Erik will bring print paper worksheets.
02/27 12 07 Analysis of Covariance: Comparing Regression Lines HW:
12 ANCOVA 1
Rmd html dat
Discuss Wald test matrix specification.
03/03 13 08 Polynomial Regression read: Ch 08-1 08-2 09-1 09-2 09-3
video:
quiz: 07 (2 parts)
In-class: Rmd html dat
03/05 14 09 Response Models with Factors and Predictors HW:
14 ANCOVA 2
Rmd html dat
Helper video
03/10 15 10 Model Selection for Multiple Regression read: Ch 10
video: 10-1 10-2 10-3
quiz: 08
HW 07 Continued in class
03/12 16 HW 14 Continued in class, due Friday by midnight.
03/17 17 Spring Break
03/19 18 Spring Break
03/24 19 11 Logistic Regression read: Ch 11
video: 11-1 11-2 11-3 11-4
quiz: 10
In-class: Rmd html dat Poster:
Poster Planning
Rmd html
Due Tuesday.
Choose/define poster project requiring a method from class: ANCOVA, Logistic multiple regression, PCA, etc.
03/26 20 HW:
20 Logistic Regression
Rmd html dat
03/31 21 12 An Introduction to Multivariate Methods read: Ch 12-13
video: 12 13-1 13-2 13-3
quiz: 11 (2 parts)
In-class: Rmd html dat
04/02 22 13 Principal Components Analysis (PCA) HW:
22 PCA
Rmd html dat
04/07 23 14 Cluster Analysis read: Ch 14-15
video: 14-1 14-2 14-3 15
quiz: 12 (2 parts)
In-class:
Clustering
Rmd html dat
04/09 24 15 Multivariate Analysis of Variance (MANOVA) HW:
24 MANOVA
Rmd html dat
04/14 25 16 Discriminant Analysis
17 Classification
read: Ch 16-17
video: 16-1 16-2 17-1 17-2 17-3
quiz: 13 (2 parts)
In-class:
Discriminant analysis for classification
Rmd html dat
04/16 26 13+11+17 PCA and logistic regression classification HW:
26+22+28 PCA and logistic Classification
Rmd html dat
04/21 27 Posters begin HW:
Poster document 1 of 2: Analysis, Due Friday

Rmd html
04/23 28
04/28 29 HW:
Poster document 2 of 2: Intro/Discuss/Bib, Due Friday

Rmd html
04/30 30
05/05 31 Survey
Poster finalize
Poster template
pdf, Rnw, sty, bib, logo
Example poster

pdf, Rnw
Transition from Markdown to LaTeX
Video for poster transition
$10 poster printing
Minuteman Press, Eubank
1631 Eubank Boulevard NE, Suite D, Albuquerque, NM 87112
(505)881-0164
Open Mon-Fri 8a-5p
Submit poster to website
Project name: “UNM ADA2 class poster”
Due Date: try to submit a few days early so the printer isn’t overwhelmed by requests
Additional Details:
“3’x4′ portrait poster on bond paper”
File #1: Name the poster pdf with your name in the filename, such as “FirstLast_ADA1_poster.pdf”. Arrange to pick up the poster.
05/07 32 POSTERS Poster session in SMLC lobby
3:30-6:30pm
Poster:
Submit poster pdf to UNM Learn
Due Fri 5pm
Poster reviewing rubric
05/12 FINALS WEEK (no final) Surveys Due — submit receipt or confirmation page to UNM Learn
* Learning Studio
* EvalKit in Learn

 


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 (ADA1)
Semesters offered: Spring
Lecture: Stat 428/528.001 (CRN 33933 or 33935), TR 1530-1645, CTLB 300 Video
email: “Erik B. Erhardt” <erike@stat.unm.edu>, please include “ADA2” in the subject line
Textbook: Peter Dalgaard, “Introductory Statistics with R“, Second Edition, 2008, ISBN: 978-0-387-79053-4. The book is not required, but it will provide a backup for what you learn in class.
Laptops running R: I encourage you to bring a laptop to class each day so you can work on the exercises in class. If you don’t have one, no problem, there are laptops in class and teamwork is encouraged — sit next to someone friendly and discuss your work.

Classroom computers: Please reboot classroom laptops at the end of class period by request of the IT staff.
Saving data: If you’re using classroom computers, use flash drives or UNM’s OneDrive (available in LoboMail) for saving files. I recommend using the simple but systematic folder structure: one main folder called Stat428_ADA2 with all of your assignments (keep the original filenames) with subfolders for lecture notes and your poster.


Instructors

Please include “ADA2” in the subject line of all emails.

Professor

Erik Erhardt <erike@stat.unm.edu>, he/him, SMLC 312

Teaching Assistants

Leah Puglisi <lhpuglisi@unm.edu>, she/her, SMLC 319
Ola Anifowoshe <oanifowoshe@unm.edu>, he/him, SMLC 208
Mohammad Ahmadi <mahmadi@unm.edu>, he/him, SMLC 323

Additional Assistants, Peer Mentors, SEP

Kelli Kasper, she/her
Grace Mayer, she/her

Office hours

Mon: 14:00-16:00 Leah
Tue: 12:30-13:30 Leah, 13:30-15:00 Erik
Wed: 9:00-11:00 Ola, 14:00-16:00 Mohammad
Thu: 12:30-13:30 Ola, 13:30-15:00 Erik
Fri: 14:00-16:00 Mohammad

  • 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. Purpose: to assess reading and video comprehension and assure you’re prepared to actively participate in class activities with a minimal lecture. (About 12, 20% of the final grade, the lowest few are dropped.) Most weeks plan for 1-3 hours reading and video, 30-60 minute quiz.
    • 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 “Calculated Grade” to see the feedback for each question of the quiz.
  • In-class assignments are due the following day (Wed) by 5 pm, submitted to UNM Learn. Purpose: to struggle and find success in class with the concepts and skills. (About 12, includes class participation, 20% of the final grade, the lowest few are dropped.) Plan to start and finish in class, sometimes 1-2 hours beyond class.
  • Homework (HW) assignments are assigned each Thursday and due the following Thursday, submitted to UNM Learn. Purpose: to apply concepts and skills to your class poster project. (About 12, 40% of the final grade, the lowest few are dropped.) Most weeks plan on 2-12 hours per assignment.
  • Poster will be developed and completed in the last weeks of the semester, 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! (16% total: 1 poster and presentation, 2% preparation, 10% poster, 2% presentation, and 2% evaluations of others of the final grade.) In the last couple of weeks, assembling this poster may take 3-5 hours, using a template provided to you.
  • Course surveys are to collect information to help facilitate the class or to encourage participation in course evaluations. Purpose: to participate in national project-based learning projects and improve the course. (About 2, 4% of final grade [and a simple way to go from B+ to A].)

The final grade may include a small buffer at the discretion of the instructor. For example, the final grade could be the total points earned divided by the total possible points times 0.95 for graduate students and 0.90 for undergraduate students. That is [Final Grade] = [Points Earned]/[Points possible * 0.95] so that your grade is slightly higher than you earned.

Student Attendance: If a student has more than 3 absences, I reserve the right to assign to that student a WF and drop mid-semester or assign an F at the end of the semester without warning. Students in this situation need to speak with Erik immediately.

Late assignments will not be accepted.

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

Disability statement

If you have a documented disability that will impact your work in this class, please contact me to discuss your needs. You’ll also need to register with the Accessibility Resource Center in 2021 Mesa Vista Hall (building 56) across the courtyard east from the SUB.

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 pg 15). 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. For more information on the campus policy regarding sexual misconduct.

UNM Indigenous Peoples Land and Territory Acknowledgment

I would like to acknowledge the original peoples of this land. The Sandia Pueblo (other pueblo communities) and the Navajo nation have ties and stories on this land and within the broader community that are connected within New Mexico. I am grateful to be able to work here in relationship and strengthen community on this territory.


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

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.

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).)

 

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.

Pre-course to-dos

Did you receive a registration error for Fall 2019? 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:
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.
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.

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


Acumen in Statistics