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

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.

COVID-19 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 video Due: Mon 4/13 11:59PM
04/09	24
04/14	25	13 Principal Components Analysis (PCA)			HW: 22 PCA Rmd html dat video 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 video 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 video Due: Mon 5/4 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 video Due: Mon 5/11 11:59PM
05/07	32
05/12		FINALS WEEK	(no final)	Surveys Due — * Learning Studio * EvalKit in Learn (log in, then right side under “EvaluationKIT”)	(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)

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

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:

Emphasize statistical literacy and develop statistical thinking
Use real data
Stress conceptual understanding, rather than mere knowledge of procedures
Foster active learning in the classroom
Use technology for developing conceptual understanding and analyzing data
Use assessments to improve and evaluate student learning

Learning without thought is labor lost. What I hear, I forget. What I see, I remember. What I do, I understand. – Confucius

Citing and using notes, including previous editions

Citing lecture notes: Erhardt EB, Bedrick EJ, and Schrader RM. (2020) Lecture notes for Advanced Data Analysis 2. Retrieved Mar 1, 2020, from statacumen.com/teach/ADA2/notes/ADA2_notes_S20.pdf, 136–144. Notes from Spring 2020 using R with tidyverse: ADA2_notes_S20.pdf includes all chapters in one document.

Lecture notes for Advanced Data Analysis 2 (ADA2) Stat 428/528 University of New Mexico is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at https://statacumen.com/teach/ADA2/notes/ADA2_notes_S20.pdf. Notes from Spring 2017 using R: ADA2_notes_S17.pdf Notes from Spring 2016 using R: ADA2_notes_S16.pdf Notes from Spring 2015 using R: ADA2_notes_S15.pdf Notes from Spring 2014 using R: ADA2_notes_S14.pdf Notes from Spring 2013 using R: ADA2_notes_S13.pdf Notes from Spring 2012 using SAS: ADA2_notes_S12.pdf

StatAcumen.com

ADA2 S20