ADA2
UNM Stat 428/528: Advanced Data Analysis II (ADA2)
Spring 2014 Syllabus is below table.
Spring 2014 schedule:
Time: TR ????-????
Location: ????
Stat 428, CRN ????
Stat 528, CRN ????
Did you receive a registration error for Spring 2014? 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)?
News:
5/16 The notes have been updated for Spring 2014.
Tentative Timetable
| Wk-Date | Ch | Topic | Slides Code Data | pts HW sol Data |
Read |
HW Due |
Plot |
| 01-01/21 | Two-hour delay, cancellation | ||||||
| 01-01/23 | 01 | R, Review | Ch 01 R d1 d2 |
HW01 sol dat |
01/29 | ||
| 02-01/28 | |||||||
| 02-01/30 | 02 | Introduction to Multiple Linear Regression |
Ch 02 R d1 d2 |
HW02 sol dat |
02/05 | ||
| 03-02/04 | |||||||
| 03-02/06 | 03 | A Taste of Model Selection for Multiple Linear Regression |
Ch 03 R d1 |
HW03 sol dat |
02/12 | ||
| 04-02/11 | 04 | Experimental Design: One and Two Factor Designs |
Ch 04 | ||||
| 04-02/13 | 05 | Paired Experiments and Randomized Block Designs |
Ch 05 Coef R d1 d2 d3 d4 |
HW05 sol dat |
02/21 | ||
| 05-02/18 | |||||||
| 05-02/20 | |||||||
| 06-02/25 | 06 | Discussion of Observational Studies |
Ch 06 R d1 | ||||
| 06-02/27 | 07 | Analysis of Covariance: Comparing Regression Lines |
Ch 07 R d1 d2 d3 |
HW07 sol dat |
03/07 | ||
| 07-03/04 | |||||||
| 07-03/06 | |||||||
| 08-03/11 | 08 | Polynomial Regression | Ch 08 R d1 d2 |
||||
| 08-03/13 | |||||||
| 09-03/18 | Spring Break | ||||||
| 09-03/20 | Spring Break | ||||||
| 10-03/25 | 09 | Response Models with Factors and Predictors |
Ch 09 R d1 |
HW09 sol (dat = HW05) |
04/02 | ||
| 10-03/27 | |||||||
| 11-04/01 | 10 | Model Selection for Multiple Regression |
Ch 10 R d1 |
||||
| 11-04/03 | 11 | Logistic Regression | Ch 11 R d1 d2 d3 d4 |
HW11 sol dat |
04/16 | ||
| 12-04/08 | |||||||
| 12-04/10 | |||||||
| 13-04/15 | 12 | An Introduction to Multivariate Methods | Ch 12 R | ||||
| 13-04/17 | 13 | Principal Components Analysis (PCA) | Ch 13 R d1 d2 d3 d4 |
HW13 sol dat |
04/25 | ||
| 14-04/22 | |||||||
| 14-04/24 | 14 | Cluster Analysis | Ch 14 R d1 d2 |
||||
| 15-04/29 | 15 | Multivariate Analysis of Variance (MANOVA) |
Ch 15 R d1 |
||||
| 15-05/01 | 16 | Discriminant Analysis | Ch 16 R d1 |
wcloud images |
|||
| 16-05/06 | 17 | Classification | Ch 17 R | HW17 sol R dat |
Tue 05/07 by 3pm slid under my office door Math&Stat (SMLC 312) |
||
| 16-05/08 | Discussion of HW17 solutions | ||||||
| 17-05/13 | Finals Week |
I recommend printing (two to a page) only the upcoming chapter the day before class because future chapters are subject to edits.
Notes from Spring 2014 using R: ADA2_notes.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 http://statacumen.com/teach/ADA2/ADA2_notes.pdf.
Citing lecture notes, example: Bedrick EJ, Schrader RM, and Erhardt EB. (2013) Lecture notes for Advanced Data Analysis 2. Retrieved Mar 1, 2013, from statacumen.com/teach/ADA2/ADA2_notes.pdf, 136–144.
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 http://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 http://statacumen.com/teach/ADA2/ADA2_notes_S12.pdf.
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 25445 or 25449), TR 9:30–10:45, Hibben 105 (LoboWeb says we’re in GSM 230, we’re not)
Spring 2013 office hours: MSLC 312, Tue 11:00-12:00 and Thu 14:00-15:00
email: ”Erik B. Erhardt” <erike@stat.unm.edu>, 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.
Get started before class:
Step 0: Set up R with Rstudio
(1) Download R for windows or mac, (2) install Rstudio, and (3) install a package we’ll use with the following R command: install.packages("ggplot2").
R style matters. There is a lot of online help on R, such as at UCLA. Usually try searching for “R [mytopic]” and you’ll get lots of results.
R tutorials: TryR (gentle), Kelly Black
Cookbook for R for helpful examples, visualization tutorials, diagrams.
Teaching Assistants
Not sure who this is, yet. Might only be a grader.
Assessment
Rubrics guide self-assessment of homework and code.
Homework is designed to encourage you to review the material we’ve learned, synthesize new information from the R help pages or the web, and apply (and learn!) your new skills. Expect to spend 4-5 hours a week (outside of class!) to do well, and maybe double that to do outstandingly well. Start working on the homework when it is assigned, not the weekend before it’s due.
Homework is due 1 week (or 2 classes, whichever is shorter) after we complete each chapter. Homework grade based on points for homework, with a 10% bonus for excellent code (three 5s) based on the rubric.
Header for homework assignments:
First Last
ADA2 Stat 428 (or 528)
HW ##
MM/DD/YYYY
All R code for the assignment should be included in an appendix at the end of the document.
Please hand in a physical version of your homework – a grader will write comments on it and give it back to you. An electronic version will be accepted under exception circumstances (almost never).
Late assignments will be penalized 20% if handed in (or slid under my office door) by 5pm the following day, and will not be accepted after that.
Final grade is the proportion correct of HW points, possibly with a safety cushion built-in (such as by reducing the denominator).
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.
Random stuff:
UNM R programming group, organized and taught by Christian Gunning, meeting at 12:00pm on Friday in the PIBBS space in Castetter Hall.
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 style matters. There is a lot of online help on R, such as at UCLA. 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.
Raster vs vector graphics.
Statistics pre-req refresher from Khan Academy.
Table of selected statistical methods
The 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 | |
| non-normal | medians | Kruskal Wallis | 1-06 | |||
| categorical | proportions | Chi square test | 1-07 | |||
| 2 or more (e.g., 2-way ANOVA) |
normal | categorical | means | Factorial ANOVA | 2-05 | |
| non-normal | medians | Friedman test | not | |||
| 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 | ||
| non-normal | non-parametric correlation |
1-08 | ||||
| categorical | categorical or continuous |
logistic regression | 2-11 | |||
| continuous | discriminant analysis |
2-16 | ||||
| 2 or more | normal | continuous | multiple linear regression |
2-02 | ||
| non-normal | ||||||
| categorical | logistic regression | 2-11 | ||||
| normal | mixed categorical and continuous |
Analysis of Covariance, General Linear Models (regression) |
2-09 | |||
| non-normal | ||||||
| categorical | logistic regression | 2-11 | ||||
| 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 | |||
| categorical | cluster analysis | 2-13 | ||||
| discriminant analysis | 2-16 | |||||
| classification | 2-17 | |||||