UNM Stat 428/528: Advanced Data Analysis II (ADA2)
Table of Contents
Spring 2015 Syllabus is below table.
Spring 2015 schedule; Time: TR 1230-1345; Location: DSH 120; Stat 428, CRN 25445; Stat 527, CRN 25449
News:
Tentative Timetable
Wk-Date | Ch | Topic | Slides Code Data | pts HW sol Data | Read | HW Review HW Due | Plot |
01-01/13 | 01 | R, Review | Ch 01 R d1 d2 | 60 HW01 sol dat, FB | 01/22 01/27 | rivers | |
01-01/15 | fire pie | ||||||
02-01/20 | 02 | Introduction to Multiple Linear Regression | Ch 02 R d1 d2 | 60 HW02 sol dat, FB | 01/29 01/03 | lifecycle | |
02-01/22 | 03 | A Taste of Model Selection for Multiple Linear Regression | Ch 03 R d1 | 85 HW03 sol dat, FB | 02/05 02/10 | diss | |
03-01/27 | 04 | Experimental Design: One and Two Factor Designs | Ch 04 | support | |||
03-01/29 | snow | ||||||
04-02/03 | 05 | Paired Experiments and Randomized Block Designs | Ch 05 Coef R d1 d2 d3 d4 | 140 HW05 sol dat, FB | 02/19 02/24 | city | |
04-02/05 | music | ||||||
05-02/10 | history | ||||||
05-02/12 | olymp 2 | ||||||
06-02/17 | freq | ||||||
06-02/19 | 06 | Discussion of Observational Studies | Ch 06 R d1 | tilt 2 | |||
07-02/24 | 07 | Analysis of Covariance: Comparing Regression Lines | Ch 07 R d1 d2 d3 | 80 HW07 sol dat, FB | 03/03 03/05 | drought | |
07-02/26 | 08 | Polynomial Regression | Ch 08 R d1 d2 | band 2 | |||
08-03/03 | 09 | Response Models with Factors and Predictors | Ch 09 R d1 | 100 HW09 sol (dat = HW05), FB | 03/19 03/24 | ||
08-03/05 | 10 | Model Selection for Multiple Regression | Ch 10 R d1 | pleasant | |||
09-03/10 | Spring Break | best13 | |||||
09-03/12 | Spring Break | ||||||
10-03/17 | 11 | Logistic Regression | Ch 11 R d1 d2 d3 d4 d5 | 80 HW11 sol dat, FB | 03/31 04/02 | elem | |
10-03/19 | moon | ||||||
11-03/24 | 12 | An Introduction to Multivariate Methods | Ch 12 R | ||||
11-03/26 | |||||||
12-03/31 | 13 | Principal Components Analysis (PCA) | Ch 13 R d1 d2 d3 d4 | 60 HW13 sol dat, FB | 04/14 04/16 | water | |
12-04/02 | daily | ||||||
13-04/07 | 14 | Cluster Analysis | Ch 14 R d1 d2 | ||||
13-04/09 | 15 | Multivariate Analysis of Variance (MANOVA) | Ch 15 R d1 | ||||
14-04/14 | 16 | Discriminant Analysis | Ch 16 R d1 | die-d3 | |||
14-04/16 | 17 | Classification | Ch 17 R | 100 HW17 sol R dat, FB | 04/28 04/30 | spiral | |
15-04/21 | deadly | ||||||
15-04/23 | 18 | Data Cleaning | Ch 18 R d1 d2 d3 d4 d5 | 90 HW18 sol dat, FB | Had SN | 05/7 by 3pm slid under my office door Math&Stat (SMLC 312) | wcloud images |
16-04/28 | histomap | ||||||
16-04/30 | Evaluations click “login” on left | ||||||
17-05/07 | Finals Week | FB |
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, DSH 120 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. Office hours: MSLC 312, Tue 11:00-12:00, Thu 14:00-15:00 Teaching Assistants Andisheh Dadashi, andishehATmath.unm.edu, Office Hours Mon and Wed 12:00-13:00, table outside SMLC 312 door Xichen Li, jessieliATunm.edu, Office Hours Fri 13:00-14:00, SMLC 201. R tutorials: TryR (gentle), Kelly Black Cookbook for R for helpful examples, visualization tutorials, diagrams.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 score includes an ethos (credibility) multiplier:[points earned]*1.1 for exceptional work, code, exposition (rubric top level) [points earned]*1.0 acceptable to great work [points earned]*0.9 for unorganized work, undocumented code (rubric lower levels)Header for homework assignments should include:
First Last ADA2 Stat 428 (or 528) HW ## MM/DD/YYYYAll R code for the assignment should be included with each problem. 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). S15 cushion divided final score by 0.99 for grad and 0.97 for undergrad students.
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.Spring 2014 preamble: Did 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, I will override you into the course. Don’t worry.
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.
Spring 2014 schedule: Time: TR 0930-1045 Location: Hibben 105 Stat 428, CRN 25445 Stat 528, CRN 25449 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)? Installing GGally on Max OSX: 1. Download http://cran.r-project.org/src/contrib/GGally_0.5.0.tar.gz into your Downloads Folder. 2. Download the X11 program from http://xquartz.macosforge.org/landing/ and install it. 3. Run X11 — you’ll be at a command prompt where you can navigate your hard drive. 4. Type “pwd” (don’t type the quotes) and it will probably tell you you’re in “/Users/[your user name]”. 5. Change to the Downloads folder with “cd Downloads”. 6. Type “pwd” and it should tell you you’re in “/Users/[your user name]/Downloads”. 7. List the files with “ls” and verify that file GGally_0.5.0.tar.gz is there. 8. Install GGally with “R CMD INSTALL GGally_0.5.0.tar.gz”, it will give some messages about installing, with “DONE (GGally)” at the end. 9. Restart RStudio and it will work (fingers crossed)!
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 |