UNM Stat 428/528: Advanced Data Analysis II (ADA2)schedule; Time: TR 1530-1645; Location: DSH 224; Stat 428, CRN 25445; Stat 528, CRN 25449 + Peer mentors via UNM Stat 495/595: Statistics Education Practicum (SEP) Stat 495.— or Stat 595.—, CRN —– or —–
Learn to produce beautiful (markdown) and reproducible (knitr) reports with informative plots (ggplot2) and tables (xtable) by writing code (R, Rstudio) to answer questions using fundamental statistical methods (analysis of covariance, logistic regression, and multivariate methods), which you’ll be proud to present (poster).
NewsSee you next semester.
Saving data: If you’re using classroom computers, use Flashdrives or UNM’s OneDrive (available in LoboMail) for saving files. I recommend using a very systematic folder structure, such as a main folder called Stat428_ADA2, with subfolders called homework, in-class, reading, poster, etc.
Weekly structure (also see Assessment below)
- Pre-class (Tuesday): Reading, Video, Quiz (due before class — solutions become available Tue 3:30, after the quiz is due)
- In-class: Activities in class Tuesday submitted to UNM Learn (evaluated by TA within 1 week), Tues 5pm turn in what you have, Wed 5pm turn in completed assignment. Thursday we will start the homework in class to allow you to struggle but get questions answered before finishing on your own.
- Post-class (Thursday): Homework (crowdgrader, due following Thursday before class, feedback available until Tuesday after grading). Assignments will be common for all students.
- Post-class (Following Thursday-Tuesday): Grading (crowdgrader, following 1 week + Tuesday before class)
Course notes, code, data, and video lecturesNotes from Spring 2016: ADA2_notes_S16.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/ADA2_notes_S16.pdf.
|Ch||Chapter Title||Notes||R code||Datasets||Video lectures playlist|
|01||R statistical software and review||R||turkey.csv, rocket.dat||01-1, 01-2|
|02||Introduction to Multiple Linear Regression||R||indian.dat, gce.dat||02-1, 02-2|
|03||A Taste of Model Selection for Multiple Regression||R||ratliver.csv||03-1, 03-2|
|04||One Factor Designs and Extensions||R||none||04|
|05||Paired Experiments and Randomized Block Experiments||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||R||sat.dat||06|
|07||Analysis of Covariance: Comparing Regression Lines||R||tools.dat, toolsfake.dat, twins.dat||07-1 07-2 07-3 HW helper video|
|08||Polynomial Regression||R||cloudpoint.dat, mooney.dat||08-1 08-2|
|09||Discussion of Response Models with Factors and Predictors||R||faculty.dat||09-1 09-2 09-3|
|10||Automated Model Selection for Multiple Regression||R||oxygen.dat||10-1 10-2 10-3|
|11||Logistic Regression||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||R||none||12|
|13||Principal Component Analysis||R||bgs.dat, shells.dat, sparrows.dat, temperature.dat||13-1 13-2 13-3|
|14||Cluster Analysis||R||birthdeath.dat, teeth.dat||14-1 14-2 14-3|
|15||Multivariate Analysis of Variance||R||shells_mf.dat||15|
|16||Discriminant Analysis||R||mower.dat||16-1 16-2|
|17||Classification||R||business.dat||17-1 17-2 17-3|
|18||Data Cleaning||R||conversions.txt, dalton.txt, dirty_iris.csv, edits.txt, people.txt, unnamed.txt|
Passion Driven Statistics (PDS) dataInstall 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. (I reserve the right to continue to improve the materials throughout the semester.)
|Wk-Date||Cl||Topic||Reading, Video, Quiz||In-class Worksheet, Data||Homework||HW Submit Grading||Due before class|
|00-01/18||00||Install software||See Step 0 video: 00|
|01-01/19||01||01 R, Review||read: Ch 01 video: 01-1, 01-2 quiz: in-class||01 R Review Rmd html dat Videos: 1, 2, 3 We will grade this assignment in class on Thursday, plan to finish it in the first half of Thursday’s class.||Note: numbers refer to week numbers|
|01-01/21||02||3:30 Submit as HW to CG –> 4:00 Grade 4:20 Review feedback 4:30 Calculate grades, discuss||Turn in and grade: 00 crowdgrader 1/21 Submit 1/21 Grade||No HW 01|
|02-01/26||03||02 Introduction to Multiple Linear Regression||read: Ch 02 video: 02-1, 02-2 quiz: 02||In-class: Rmd html dat Submit your html with solutions by 5pm.||Quiz 02|
|02-01/28||04||02 Mult LR Rmd html dat Submit your html to crowdgrader.||02 crowdgrader 2/4 Submit 2/9 Grade solutions|
|03-02/02||05||03 A Taste of Model Selection for Multiple Linear Regression||read: Ch 03, 04 video: 03-1, 03-2, 04 quiz: 03||In-class: Rmd html dat sol: Rmd html Submit your html with solutions by 5pm.||Quiz 03|
|03-02/04||06||04 Experimental Design: One and Two Factor Designs||03 Taste Model Sel Rmd html dat||03 crowdgrader 2/11 Submit 2/16 Grade solutions||Turn in HW 02|
|04-02/09||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||Quiz 04, Grade HW 02|
|04-02/11||08||04 Experiments 1 Rmd html||04 crowdgrader 2/18 Submit 2/23 Grade solutions||Turn in HW 03|
|05-02/16||09||read: Ch 05 (5.3 – end) video: 05-6 05-7 05-8 05-9 quiz: 05||In-class: Rmd html dat||Quiz 05, Grade HW 03|
|05-02/18||10||05 Experiments 2 Rmd html dat||05 crowdgrader 2/25 Submit 3/1 Grade solutions||Turn in HW 04|
|06-02/23||11||06 Discussion of Observational Studies||read: Ch 06-07 video: 06 07-1 07-2 07-3 quiz: 06, 07||In-class: html turn in paper version||Quiz 06, Grade HW 04|
|06-02/25||12||07 Analysis of Covariance: Comparing Regression Lines||06 ANCOVA 1 Rmd html dat||06 crowdgrader 3/3 Submit 3/8 Grade solutions||Turn in HW 05|
|07-03/01||13||08 Polynomial Regression||read: Ch 08-1 08-2 09-1 09-2 09-3 video: quiz: 07||In-class: Rmd html dat||Quiz 07, Grade HW 05|
|07-03/03||14||09 Response Models with Factors and Predictors||07 ANCOVA 2 Rmd html dat Helper video||07 crowdgrader 3/10 Submit 5pm 3/22 Grade a solution||Turn in HW 06|
|08-03/08||15||10 Model Selection for Multiple Regression||read: Ch 10 video: 10-1 10-2 10-3 quiz: 08||Quiz 08, Grade HW 06|
|08-03/10||16||08 Model Selection||Turn in HW 07|
|10-03/22||19||11 Logistic Regression||read: Ch 11 video: 11-1 11-2 11-3 11-4 quiz: 10||In-class: Rmd html dat||Choose/define poster project requiring a method from class: ANCOVA, Logistic multiple regression, PCA, etc.||Poster Planning Rmd html Due 3/29||Quiz 10, Grade HW 07|
|10-03/24||20||10 Logistic Regression Rmd html dat||10 crowdgrader 3/31 Submit 4/5 Grade solutions|
|11-03/29||21||12 An Introduction to Multivariate Methods||read: Ch 12-13 video: 12 13-1 13-2 13-3 quiz: 11||In-class: Rmd html dat||Quiz 11|
|11-03/31||22||13 Principal Components Analysis (PCA)||11 PCA Rmd html dat||11 crowdgrader 4/7 Submit 4/12 Grade solutions||Turn in HW 10|
|12-04/05||23||14 Cluster Analysis||read: Ch 14-15 video: 14-1 14-2 14-3 15 quiz: 12||In-class: Clustering Rmd html dat||Quiz 12, Grade HW 10|
|12-04/07||24||15 Multivariate Analysis of Variance (MANOVA)||12 MANOVA Rmd html dat||12 crowdgrader 4/14 Submit 4/19 Grade solutions||Turn in HW 11|
|13-04/12||25||16 Discriminant Analysis 17 Classification||read: Ch 16-17 video: 16-1 16-2 17-1 17-2 17-3 quiz: 13||In-class: Discriminant analysis for classification Rmd html dat||Quiz 13, Grade HW 11|
|13-04/14||26||13+11+17 PCA and logistic regression classifcation||13+11+17 PCA and logistic Classification Rmd html dat||13 crowdgrader 4/21 Submit 4/26 Grade solutions||Turn in HW 12|
|14-04/19||27||Posters begin||Poster document 1/2: Analysis, Due Friday 4/22 Rmd html||Grade HW 12|
|14-04/21||28||14 crowdgrader 4/22 Submit 4/26 Grade solutions||Turn in HW 13, Turn in Poster Doc 1/2 Fri 4/22|
|15-04/26||29||Poster document 2/2: Intro/Discuss/Bib, Due Friday 4/29 Rmd html||Grade HW 13|
|15-04/28||30||15 crowdgrader 4/29 Submit 5/3 Grade solutions||Turn in Poster Doc 2/2 Fri 4/29|
|16-05/03||31||Survey Poster finalize||Poster template pdf, Rnw, sty, bib, logo Example poster pdf, Rnw Transition from Markdown to LaTeX Video for poster transition||Poster printing ARI Graphix $9 poster printing Open Mon-Fri 7:30-5:30 Do not use their website! Do: Email email@example.com, indicate to print “in color on bond paper” and attach poster pdf file. Price is $0.75/sq ft. 4716 McLeod Rd NE|
|16-05/05||32||POSTERS||Poster session in SMLC lobby 3:30-5:30pm||Poster reviewing rubric||Submit poster pdf to UNM Learn Fri 5/6 5pm|
|17-05/10||FINALS WEEK||(no final)||Surveys Due — submit receipt or confirmation page to UNM Learn * Learning Studio * EvalKit in Learn||Surveys Due 5/10 5pm|
SyllabusDescription: 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 25445 or 25449), TR 1530-1645, DSH 224 Video email: “Erik B. Erhardt” <firstname.lastname@example.org>, please include “ADA2” in 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. Office hours: SMLC 312, TR 1300-1500 Laptops running R: I encourage you to bring a laptop to class each day so you can try the R programming exercises in class. If you don’t have one, no problem, there are some laptops in class and teamwork is encouraged — sit next to someone friendly who likes to share.
Teaching Assistants and Peer Mentors
Stat grad students TAsChauntal Andrews <email@example.com>, office hours Mon 0900-1100 in SMLC 301
Student learning outcomesSimilar as in ADA1, but at a higher level.
- 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, the lowest few are dropped.) Most weeks plan for 1-3 hours reading and video, 30-60 minute quiz.
- In-class assignments are due each day by 5pm, submitted to UNM Learn. Purpose: to struggle and find success in class with the concepts and skills. (About 12, includes class participation, 20% of final grade, the lowest few are dropped.) Plan to start and finish in class.
- Homework (HW) assignments are assigned each Thursday and due the following Thursday, submitted to crowdgrader.org (75% of HW grade). Purpose: to apply concepts and skills to your class poster project. (About 12, 40% of final grade, the lowest few are dropped.) Most weeks plan on 2-8 hours per assignment.
- Peer grading is due by the following Tuesday after each homework is due (25% of HW grade). Purpose: to gain skill assessing the work of others, as well as see alternative strategies to answer questions. Most weeks this will take about 45 minutes to grade 5 other students’s HW.
- 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! (1 poster and presentation, 2% preparation, 10% poster, 2% presentation, and 2% evaluations of others of final grade.) In the last couple 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 course. (About 2, 4% of final grade [and a simple way to go from B+ to A].)
- Students usually get far more feedback on their work than they would get from over-worked teaching assistants/faculty.
- Students get to see what other students are doing, and they can learn from the work of others (taking the best ideas, and leaving the rest).
- In exchange for this, they need to put in some amount of work in reviewing the work of others.
- It is important that students understand that their final grade is determined both by the quality of their work, and by the precision of the grades they give, and the helpfulness of the reviews they write.
Collaboration and citationFor homeworks 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 homeworks, 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 homeworks 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 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 in as a comment (which is doubly helpful for finding the resource again).
Disability statementIf 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. Peer mentor Carrie Booth <firstname.lastname@example.org>, Education grad student, course alumnus, and member of the Delta Alpha Pi Honor Society (Disability Achievement Pride) has a background in special education and is familiar with challenges surrounding learning and accessibility for students with disabilities in college. She has offered to be available, if you choose to seek her out, for assistance. I legally can’t connect her to you, but you can let her know if you have needs she’ll be particularly qualified to be helpful with. I’m glad to have her available since one of her goals through DAP is to reduce stigma of students with disabilities in academia through visibility, achievement, and community service.
Title IX statementIn 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.
Our ClassroomWe’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 ConnectionsOur 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
ArchiveDid you receive a registration error? 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-reqs)? If you are waitlisted and qualified and we have enough seats, I will override you into the course. Don’t worry. Step 0: Before our first class (Tue 1/19) please read through the following and install the required software on your computer. If you don’t have a computer, there are classroom computers which will be of limited availability when the room is open.
- Create a Google account (if you don’t already have one) to use with crowdgrader.
- Sign up for crowdgrader (which uses gmail account).
- Complete a quick 5-question survey so I can link your crowdgrader gmail account with your UNM user ID for homework assignments.
- Install or upgrade R (windows or mac) then Rstudio. Videos that may be helpful:
- Install R packages, also update all packages within RStudio.
- Install Mendeley.
- Install LaTeX (for poster at end of semester).
Random stuffinnovationAcademy video UNM has license for free online access to the definitive books for the Lattice and ggplot2 graphing platforms. Note you must be on campus or logged in through the UNM proxy to access these. R is currently available in these UNM Locations: DSH 141 and 143, Econ 1004, SMLC pods, and SUB IT-LoboLab Pod and IT-LoboLab Classroom. R style matters. There is a lot of online help on R, such as at UCLA, try-r, and Google’s Intro to R video series. Usually try searching for “R [mytopic]” and you’ll get lots of results. ggplot2 plotting cookbook. R reference card by Jonathan Baron. Translate between MATLAB and R. Figure checklist. Choosing the right chart. Nature Methods points of view on visualization. Statistical consulting and collaboration slides Raster vs vector graphics. Statistics pre-req refresher from Khan Academy. Coursera has a free 4-week course on computing for data analysis with R. Muddy points in perspective. R+LaTeX+knitr for reproducible research. See my SC1 lecture notes (Ch01), and Mohammad Arbabshirani’s notes (pdf, rnw). Asking smart questions “Smart Questions” guide (note “hackers build things, crackers break them”) Email Question Rubric: * Send one email per question. — Use “Reply” to continue conversation on a question; send a new email for a new question. * Include “ADA2” as the first word of the subject line in new emails (if replying, just use reply). * Begin email with a short question summary. * When possible, include commented code in email body — Comments should indicate where the problem is, what the expected behavior is, and what steps are necessary to reproduce problem. — Code should include a “Minimum representative test cast” (http://www.catb.org/esr/faqs/
Why stats now?140,000 analysts needed. Important enough to have a US Chief Data Scientist (1) (2).
Citing and using notes, including previous editionsCiting lecture notes, example: Erhardt EB, Bedrick EJ, and Schrader RM. (2016) Lecture notes for Advanced Data Analysis 2. Retrieved Mar 1, 2016, from statacumen.com/teach/ADA2/ADA2_notes.pdf, 136–144. Notes from Spring 2015 using R: ADA2_notes_S15.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/ADA2_notes_S15.pdf. Notes from Spring 2014 using R: ADA2_notes_S14.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/ADA2_notes_S14.pdf. Notes from Spring 2013 using R: ADA2_notes_S13.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/ADA2_notes_S13.pdf. Notes from Spring 2012 using SAS: ADA2_notes_S12.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/ADA2_notes_S12.pdf.
R tutorials: TryR (gentle), Kelly Black Cookbook for R for helpful examples, visualization tutorials, diagrams.
Table of selected statistical methodsThe data and design determines which method you use: original or UCLA. Here’s a table of methods with the applicable semester of ADA and Chapter.
|Number of Dependent Variables||Number of Independent Variables||Type of Dependent Variable(s)||Type of Independent Variable(s)||Measure||Test(s)||ADA-Ch|
|1||0 (1 population)||continuous normal||not applicable (none)||mean||one-sample t-test||1-02|
|continuous non-normal||median||one-sample median||1-06|
|categorical||proportions||Chi Square goodness-of-fit, binomial test||1-07|
|1 (2 independent populations)||normal||2 categories||mean||2 independent sample t-test||1-03|
|non-normal||medians||Mann Whitney, Wilcoxon rank sum test||1-06|
|categorical||proportions||Chi square test Fisher’s Exact test||1-07|
|0 (1 population measured twice) or 1 (2 matched populations)||normal||not applicable/ categorical||means||paired t-test||1-02|
|non-normal||medians||Wilcoxon signed ranks test||1-06|
|categorical||proportions||McNemar, Chi-square test||1-07|
|1 (3 or more populations)||normal||categorical||means||one-way ANOVA||1-05|
|categorical||proportions||Chi square test||1-07|
|2 or more (e.g., 2-way ANOVA)||normal||categorical||means||Factorial ANOVA||2-05|
|categorical||proportions||log-linear, logistic regression||2-11|
|0 (1 population measured 3 or more times)||normal||not applicable||means||Repeated measures ANOVA||not|
|1||normal||continuous||correlation, simple linear regression||1-08|
|categorical||categorical or continuous||logistic regression||2-11|
|2 or more||normal||continuous||multiple linear regression||2-02|
|normal||mixed categorical and continuous||Analysis of Covariance, General Linear Models (regression)||2-09|
|2||2 or more||normal||categorical||MANOVA||2-15|
|2 or more||2 or more||normal||continuous||multivariate multiple linear regression||not|
|2 sets of 2 or more||0||normal||not applicable||canonical correlation||not|
|2 or more||0||normal||not applicable||factor analysis||not|
|0 or more||mixed categorical and continuous||principal component analysis (w/multiple regression)||2-13|