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.

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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.

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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.

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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