UNM Stat 427/527: Advanced Data Analysis I (ADA1)
Fall 2019 Syllabus is below tables
Please complete Step 0 below.
Fall 2019 schedule;
Time: TR 15301645;
Location: CTLB 300 (building 55, northeast of Zimmerman);
Stat 427.001, CRN 59508; Stat 527.001, CRN 59509
Goal
Learn to produce beautiful (markdown) and reproducible (knitr) reports with informative plots (ggplot2) and tables (kable) by writing code (R, Rstudio) to answer questions using fundamental statistical methods (all one and twovariable methods), which you’ll be proud to present (poster).
Course introduction materials
Precourse todos
Did you receive a registration error for Fall 2019? 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 prerequisites)?
If you are waitlisted, as long as there are seats available I will override you into the course. Don’t worry.
Step 0
Before our first class (Tue 8/20) please read through the following actions and install the required software on your computer and complete the brief survey. If you don’t have a computer, there are classroom computers which will be available only when the classroom is open. Video for this process.
 Complete surveys
 a short Opinio presurvey required for classroom assessment (8/7 – 9/6/2019).
 Respondus survey about passion driven statistics (PDS) course content, by email (8/20 – 8/27/2019).
 Install R (windows or mac) or upgrade , then Rstudio. Videos that may be helpful:
 Install R on Mac (2 min).
 Install R for Windows (3 min).
 Install R and RStudio on Windows (5 min).
 Install R packages, also update all packages within RStudio.
 Set up your computer
 RStudio disable notebook
 Operating system to be more friendly to programming.
 (Postpone until later: Install LaTeX (for poster at end of the semester).)
Course content
Weekly structure
(also see “Assessment” below)
 Preclass (Tuesday): Reading, Video, Quiz due before class Tue 3:30 PM — solutions become available after the quiz is due.
 Inclass (Tuesday and Thursday): Activities in class due by 5 PM the following day, submitted to UNM Learn (evaluated by TA within 1 week).
 Postclass (Thursday): Homework due the following Thursday by 3:30 PM, submitted to UNM Learn (evaluated by TA within 1 week).
UNM Learn for quizzes and inclass assignments.
YouTube Video playlist (try 1.5 speed, then pause as needed).
Course notes, code, data, and video lectures
Notes from Fall 2019: ADA1_notes_F19.pdf includes all chapters in one document.
Citing lecture notes example: Erhardt EB, Bedrick EJ, and Schrader RM. (2019) Lecture notes for Advanced Data Analysis 1. Retrieved Sep 1, 2019, from statacumen.com/teach/ADA1/notes/ADA1_notes.pdf, 136–144.
Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 University of New Mexico is licensed under a Creative Commons AttributionNonCommercialShareAlike 3.0 Unported License. Based on a work at http://statacumen.com/teach/ADA1/notes/ADA1_notes_F19.pdf.
Ch  Chapter Title  Notes  R code  Datasets  Video lectures playlist  Helper videos 

00  Introduction to R, Rstudio, and ggplot  R  001 002  markdown, 01 PDS codebook, 01 HW codebook, 02 HW Lit review 

01  Summarizing and Displaying Data  R  011  03 HW 03 subset  
02  Estimation in OneSample Problems  R  021 022 023  
03  TwoSample Inferences  R  031 032 033  
04  Checking Assumptions  R  041  
05  OneWay Analysis of Variance  R  CHDS dat desc  051  
06  Nonparametric Methods  R  061 onesample, 062 paired, 063 twosample, 064 ANOVA, 065 perm test.  
07  Categorical Data Analysis  R  071 intro, 072 single prop, 073 GOFtest, 074 two prop & cond prob, …  
08  Correlation and Regression  R  BodyMass dat desc pdf  081 corr/log, 082 corr hyp test, 083 LS reg eq, 084 085  
09  Introduction to the Bootstrap  R  091  
10  Power and Sample size  R  101  
11  Data Cleaning  R  111  14 HW to poster  
12  ADA2 Ch 11 Logistic Regression  R  121 122 123 124  Upgrading R on Windows 
lm_diag_plots.R function for a large set of standard diagnostic plots.
PassionDriven Statistics (PDS) data
I encourage you to use one of the AddHealth datasets. Use W1 if you want to understand adolescents when they were young and W4 if you want to understand adult relationships. NESARC is also interesting, but we had too many people choose many related questions in F15 from this dataset.
PDS Textbook
AddHealthW1 Sampling Design, Codebook, RData. Adolescents when they were young, unique ID “AID”.
AddHealthW4 Sampling Design, Codebook, RData. Same adolescents when they were older, unique ID “aid”.
NESARC Sampling Design, Codebook, RData. Alcohol abuse and related conditions, unique ID “IDNUM”.
OutlookOnLife Sampling Design, Codebook, RData. Interesting data, but not enough continuous variables to use, unique ID “CASEID”.
GapMinder Sampling Design, Codebook, RData. Country data, but it’s complicated to average large and small countries, unique ID “country”.
SEV LTER data
Sevilleta (SEV) Long Term Ecological Research (LTER) Program
Anthropod Description and Codebook Rmd html, data.zip
Erik’s example homework document:
NESARC data, nicotine and depression: .Rmd + .bib + EditRules = .html.
Timetable
WkDate  Cl  Topic  Reading, Video, Quiz  Inclass Worksheet, Data  Homework 

0008/20  00  Install software, survey  Step 0 (above)  presurvey required for classroom assessment (8/7 – 9/6/2019)  
0108/20  01  Intro, data, poster  read: PDS Chs 23; video: Rmd, Ch 23, Med records 
CTLB video, Active Learning, 01 Syllabus subset, 01a Medical records Rmd html Turn in assignment in Thursday’s class to learn how UNM Learn works. 
(Intro to using RMarkdown: Rmd html) 
0108/22  02  Rmd, codebook  video: 01 Personal codebook 
Inclass: yesterday’s 01a submit by 16:00
Work as a group, each submit own copy. 
HW: 01 Personal codebook Rmd html
Choose from PDS datasets 
0208/27  03  Research questions  read: PDS Ch 24; video: Lit Rev biblio & Mendeley; quiz: 02 codebook and lit review 
Inclass: Rmd html Turn in one question of variable association. 
(UNM Google Scholar) 
0208/29  04  Literature review  Inclass: Rmd html Turn in one citation to a research question. 
HW: 02 Literature review Rmd html bib
(While we won’t be doing a research proposal as part of this class, if we were covering more on research methods, then we might continue with a short research proposal (Rmd html).) 

0309/03  05  R programming, data subset and numerical summaries  read: PDS Chs 5, 8, & 18, Ch 00 R, Ch 01 R; video: Ch 00 p1, Ch 00 p2, Ch 01; quiz: 03 programming, univariate 
Inclass: Rmd html Look at datasets in R, create subset of data, rename variables, numerical summaries. 

0309/05  06  Plotting univariate  video: HW 03 vid 
Inclass: Rmd html Univariate plots of numerical and categorical variables. 
HW: 03 Data subset, univariate summaries and plots Rmd html (See the link above the table “Erik’s NESARC data, nicotine and depression”.) 
0409/10  07  Plotting bivariate  read: PDS Ch 9, Ch 00 R, Ch 11 R; video: 111; quiz: quiz 
Inclass: Rmd html Complete at least one bivariate coding relationship. 

0409/12  08  Data cleaning  Inclass: Rmd html Edit rules, run with dataset, assess exceptions, decide what to do with them. Erik’s EditRules. 
HW: 04 Rmd html  
0509/17  09  Simple linear regression, intro  read: Ch 8.4, 8.2 R; video: 081 corr/log, 083 LS reg eq; quiz: quiz 
Inclass: Rmd html dat Build intuition using SLR App, interpret properties of linear regression fit. 

0509/19  10  Logarithm transformation  (novel example)  Inclass: Rmd html dat Plot, transform, plot, and interpret. 
HW: 05 Rmd html 
0609/24  11  Correlation  read: Ch 8.1, 8.3.1 R, Ch 7.5.1 only sections on “conditional probability” and the following example R; video: 081 corr/log, 082 corr hyp test, 074 two prop & cond prob; quiz: quiz 
Inclass: Rmd html Data collection (hand span and word memory), correlation, regression to the mean. 
Spurious Correlations 
0609/26  12  Categorical contingency tables  quiz 06b, Guess Ages (for next inclass)  Inclass: Rmd html d1 Interpret condition proportions in two examples. Simpson’s Paradox 
HW: 06 Rmd html 
0710/01  13  Inference, intro  read: Ch 2.12.2 R; video: see table above; quiz: quiz 
Inclass: Rmd html Guess Ages, Legos. (Legos part 2 Rmd html dat, diagram). 
BBC Radio 4: More or Less, “sampling” 9 min audio 
0710/03  14  Parameter estimation (onesample)  Inclass: Rmd html Water on Earth. 
HW: 07 Rmd html PDS Data Sampling Designs: AddHealth, OOL, NESARC 

0810/08  15  Hypothesis testing (twosample)  read: Ch 2.3end R Ch 3 R; video: see table above; quiz: quiz 
Inclass: Rmd html one and twosample tests using data we collected in class. 

0810/10  Fall Break  HW: 08 Rmd html  
0910/15  16  Paired data, assumption assessment  read: Ch 2.2.1, Ch 3.4 & 3.6, Ch 4, Ch 5; video: see table above; quiz: quiz 
Inclass: Rmd html Paired data and checking model assumptions. 

0910/17  17  ANOVA, posthoc comparisons  Inclass: Rmd html ANOVA, model assumptions, and paired comparisons. 
HW: 09 Rmd html  
1010/22  18  Nonparametric methods  read: Ch 6, Ch 7.27.4, Ch 10; video: see table above; quiz: quiz 
Inclass: Rmd html NP onesample tests and CIs, and ANOVA with pairwise comparisons. 

1010/24  19  Binomial and multinomial proportion tests  Inclass: Rmd html dat Multinomial: World series number of games. 
HW: 10 Rmd html  
1110/29  20  Twoway categorical tables  read: Ch 7.8end, Ch 8.58.7; video:; quiz: quiz 
Inclass: Rmd html dat Popular kids. 

1110/31  21  Simple linear regression, inference  Inclass: Rmd html Regression of height vs hand span using data from our class. 
HW: 11 Rmd html  
1211/05  22  Logistic regression, intro  read: ADA2 Ch 11.13, 11.6, PDS Ch 16; video:; quiz: quiz 
Inclass: Rmd html AddHealth W4 Pregnancy. 
Summary of Methods we’ve covered 
1211/07  23  Experiments and observational studies  Inclass: Rmd html Describing a study reported in the media. 
HW: 12 Rmd html  
1311/12  24  Statistical communication  read: PDS Ch 18; video:; quiz: no quiz 
Inclass: Rmd html Key statistical principles, ethics.With additional time, clarify which research questions you’ll present in your poster with a peer mentor. (Null results are ok!) 
Statistics is about communication, including writing and presenting. 
1311/14  25  Poster Preparation  Inclass: Rmd html Work on designing poster content at the bottom of your HW document. 
HW: 13 Rmd html
Work on your poster content. Try to complete your poster planning in your HW document. 

1411/19  26  Posters wrapping up  poster template pdf, Rnw, sty, bib, logo 
Prof Erhardt’s example poster pdf, Rnw 

1411/21  Thanksgiving break  move: Txgvg 11/28 next week  
1511/26  27  Show poster  Inclass: Course evaluations, submit receipt (capture screen image) as inclass assignment.
See email for more details. 
HW: 15 Rmd html
Due next Wednesday 12/7. Complete and submit your poster in LaTeX pdf format. Transition from Markdown to LaTeX 

1511/28  28  Approve poster, final touches  ARI Graphix $9 poster printing Open MonFri 7:305:30 Do not use their website! Email plotting@abqrepro.com, Subject: ADA1 class poster Text: indicate to print “in color on bond paper”. Attach: Poster pdf with your name in the filename, such as “FirstLast_ADA1_poster.pdf”. Try to send by Friday noon for the poster to be ready by Monday. Arrange to pick up the poster. Price is $0.75/sq ft for Fall 2016. 
Have a peer mentor approve your poster for printing and presentation. Congratulations!  
1612/03  29  POSTERS  Poster sessions in SMLC Atrium  Poster Schedule (be on time): 3:303:40 Organization 3:404:40 Group 1 4:455:45 Group 2 5:506:50 Group 3 Congratulations on a great semester! 

1612/05  30  Class finishes early  Erik traveling, no class  
1712/08  Finals week  (no final) 
(I reserve the right to continue to modify the schedule and improve the materials throughout the semester.)
Syllabus
Description: Statistical tools for scientific research, including parametric and nonparametric methods for ANOVA and group comparisons, simple linear and multiple linear regression and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as R. Course cannot be counted in the hours needed for graduate degrees in Mathematics and Statistics.
Prerequisite: Math 1350 [Stat 145] (or other intro stats course)
Semesters offered: Fall
Lecture: Stat 427.001, CRN 59508; Stat 527.001, CRN 59509; TR 15301645; Location: CTLB 300 (building 55, northeast of Zimmerman) Video
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.
Saving data: If you’re using classroom computers, use Flashdrives or UNM’s OneDrive (available in LoboMail) for saving files. The CTLB computers do not connect to your standard UNM drive space (as of 2016, this may not still be an issue).
Instructors
Please include “ADA1” in the subject line of all emails.
Professor
Erik Erhardt <erike@stat.unm.edu>, he/him, SMLC 312
Teaching Assistants
Kelli Kasper <kkasper@unm.edu>, she/her, SMLC 306
Leah Puglisi <lhpuglisi@unm.edu>, she/her, SMLC 319
Ola Anifowoshe <oanifowoshe@unm.edu>, he/him, SMLC 208
Additional Assistants, Peer Mentors, SEP
Grace Mayer, she/her
Office hours
Mon: 11:0014:00 Kelli
Tue: 13:0015:00 Erik
Wed: 11:0013:00 Leah
Thu: 13:0015:00 Erik
Fri: 11:0012:00 Erik
Student learning outcomes
At the end of the course, you will be able to: (student results: R, all years, 2015, 2014, 2013, 2012)
General outcomes:
 Organize knowledge in graphs, tables, and code to support concise, comprehensible, and scientifically defensible written interpretations to produce knowledge within a reproducible research environment.
 Distinguish a testable scientific hypothesis or datasupported interpretation from an opinion.
 Understand from a data story the goals of the study and apply the correct statistical procedure.
 Explain the scientific aspects of a problem to nonscientists in a fashion that enhances understanding and decision making.
Topical outcomes:
 Define parameters of interest and hypotheses in words and notation.
 Summarize data visually, numerically, and descriptively and interpret the observed characteristics. Calculate and interpret numerical summaries such as mean, variance, fivenumber summary, confidence intervals, and pvalues, and create visual summaries such as bar plots, scatter plots, and histograms. (Never pie charts!)
 Distinguish between statistical significance and scientific relevance.
 Use statistical software, such as R, to read and manage data, create informative plots, report numerical summaries, and apply statistical models, by recommended programming practice including abstraction and documentation.
 Understand the differences and limitations of controlled experiments and observational studies. Design experiments to infer causal treatment effects. Analyze observational data to infer associations between measured variables.
 Identify and explain the statistical methods, assumptions, and limitations used in reported studies in scientific literature or popular media.
 Evaluate and criticize published studies, the work of peers, and your own work and assess what was done well, what could be done better, and examine whether their conclusions are supported using statistical principles.
 Make evidencebased decisions by constructing and deciding between testable hypotheses using appropriate data and methods.
 Discover relationships and make predictions through model development and selection.
Meeting the learning outcomes
You will acquire new information in this class, but the emphasis is comprehending, integrating, and applying information. Rote factual memorization is the lowest form of learning. Effective learning takes place by explaining, integrating, applying, and analyzing facts, hypotheses, and theories.
Learning in this class occurs by:
 Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.
 Discussion – interaction with classmates to assemble and synthesize information you’d utilizing the collective skills and knowledge base of the group.
 Listening, acting, and reflecting – activities during class time provide insights into information not available in readings and includes review difficult material to aid comprehension. Note taking permits later reflection on lecture content. Listening to the professor lecture is the least effective learning tool for both students, however, and you should plan on coming to every class prepared to participate in active and reflective learning opportunities.
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 minimal lecture. (About 12, 20% of final grade, the lowest few are dropped.) Most weeks plan for 12 hours reading and video, 20 minute quiz. Quizzes are not timed, they can be taken twice, and the higher of the two scores is used for grade calculation.
 Viewing quiz solutions after the due date in UNM Learn is not intuitive. Click on the “Begin” button (this is the nonintuitive 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.
 Inclass assignments are due by 5pm the next day, submitted to UNM Learn. Purpose: to struggle and find success in class with the concepts and skills. (About 24, includes class participation, 30% of final grade, the lowest several are dropped.) Most weeks plan to finish in 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, 30% of final grade, the lowest few are dropped.) Most weeks plan on 14 hours per assignment.
 Poster will be developed through semester (most HW assignment contribute to poster), the last couple weeks we’ll complete them, 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, 12% poster, 2% presentation, and 2% evaluations of others of final grade.) In the last couple weeks, assembling this poster may take 510 hours, using a template provided to you.
 Course surveys are due at the beginning and end of the course. Purpose: to participate in national projectbased learning projects and improve course. (About 2, 4% of final grade.)
Final grade may include a small buffer at the discretion of the instructor. For example, 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.
All assignments in this class are electronic, submitted to UNM Learn.
Late assignments will not be accepted.
Rubrics guide assessment (and selfassessment) of homework, code, projects, exams, and presentations. Each assignment will have its own specific rubric.
Use of R and RMarkdown are required for the course. This will include all of the R code for the assignment with the part of the problem it addresses in a fixedwidth font and syntax highlighting. You will weave your code with prose narrations of your work and solutions.
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 submit 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 discuss your problems with other classmates or meet with or email questions to the me or the TAs.
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). You won’t be the first person to do anything in this class, so give credit where it’s due.
Statements
Accommodation Statement
In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. It is imperative that you take the initiative to bring such needs to the instructor’s attention, as he/she are not legally permitted to inquire. Students who may require assistance in emergency evacuations should contact the instructor as to the most appropriate procedures to follow. Contact Accessibility Resource Center at 2773506 for additional information.
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
 Realtime feedback promotes efficient learning
“It encourages me to engage actively with the course material and to 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
Archive
Problems installing PDS package? Solution.
If you had problems installing the PDS package, no problem; here’s how to get the data:
1. Download the “.RData” file above for your dataset.
2. Where I have “library(PDS)” in my code, change it to the two lines below. You’ll need to update the “PATH_TO_FILE” below to the path on your computer’s hard drive, and “filename” needs to be changed to the name of the file. This will directly read the data file.
# library(PDS) setwd("/PATH_TO_FILE") load("filename.RData")
Joining AddHealth waves 1 and 2 together into a single dataset can be done if you want to use variables from when the participants were both adolescents and adults. See Erik’s example project for the code.
Saving data: If you’re using classroom computers, use Flashdrives or UNM’s OneDrive (available in LoboMail) for saving files. The CTLB computers do not connect to your standard UNM drive space. I recommend using a very systematic folder structure, such as ADA1/HW, ADA1/Class, ADA1/Reading, ADA1/Poster, etc. Do not just work on files in your downloads folder or your desktop; respect your data and code!
Unicode compile problems: If you knit to pdf you may get this error: “! Package inputenc Error: Unicode char”. ASCII is a small character set what we use to program in, Unicode is an extended character set that looks pretty (for example “straight quotes” become “curly quotes”) but causes code to break. You get unwanted Unicode when you copy/paste from a pdf or some other source into your code. To fix this, you have to find the Unicode and replace it with it’s ASCII equivalent. To do this: CtrlF to find, search for “[^\x00\x7F]” (without quotes), select “Regex” for regular expressions, and find the “Next” one. As it finds instances, replace the characters manually until there are no more. These characters will typically be curly quotes or fancy dashes.
Random stuff
UNM has a 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 (2016) in these UNM Locations: DSH 141 and 143, Econ 1004, SMLC pods, and SUB ITLoboLab Pod and ITLoboLab Classroom.
R style matters. There is a lot of online help on R, such as at UCLA, tryr, 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 prereq refresher from Khan Academy.
Coursera has a free 4week 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 “ADA1” 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/
* If attaching code, please include all the files necessary to run your code (data, etc.).
Help:
LaTeX wiki, lshort, Detexify LaTeX symbols (linux texlive package management)
R tutorials: TryR (gentle), Kelly Black
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.
Knitr in Rstudio (knitr is modern version of Sweave intro, demo, guide)
xtable to produce LaTeX tabular environment from R data.frames
Cookbook for R for helpful examples, visualization tutorials, diagrams
Image formats: vector (pdf, eps) vs raster (jpeg, bmp, tiff, gif)
Why stats now?
Important enough to have a US Chief Data Scientist (1) (2).
Table of selected statistical methods
The data and design determine 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)  ADACh 
1  0 (1 population) 
continuous normal  not applicable (none) 
mean  onesample ttest 
102 
continuous nonnormal 
median  onesample median 
106  
categorical  proportions  Chi Square goodnessoffit, binomial test 
107  
1 (2 independent populations) 
normal  2 categories  mean  2 independent sample ttest 
103  
nonnormal  medians  Mann Whitney, Wilcoxon rank sum test 
106  
categorical  proportions  Chi square test Fisher’s Exact test 
107  
0 (1 population measured twice) or 1 (2 matched populations) 
normal  not applicable/ categorical 
means  paired ttest  102  
nonnormal  medians  Wilcoxon signed ranks test 
106  
categorical  proportions  McNemar, Chisquare test 
107  
1 (3 or more populations) 
normal  categorical  means  oneway ANOVA  105  
nonnormal  medians  Kruskal Wallis  106  
categorical  proportions  Chi square test  107  
2 or more (e.g., 2way ANOVA) 
normal  categorical  means  Factorial ANOVA  205  
nonnormal  medians  Friedman test  not  
categorical  proportions  loglinear, logistic regression 
211  
0 (1 population measured 3 or more times) 
normal  not applicable  means  Repeated measures ANOVA 
not  
1  normal  continuous  correlation, simple linear regression 
108  
nonnormal  nonparametric correlation 
108  
categorical  categorical or continuous 
logistic regression  211  
continuous  discriminant analysis 
216  
2 or more  normal  continuous  multiple linear regression 
202  
nonnormal  
categorical  logistic regression  211  
normal  mixed categorical and continuous 
Analysis of Covariance, General Linear Models (regression) 
209  
nonnormal  not  
categorical  logistic regression  211  
2  2 or more  normal  categorical  MANOVA  215  
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) 
213  
categorical  cluster analysis  213  
discriminant analysis  216  
classification  217 