UNM Stat 427/527: Advanced Data Analysis I (ADA1)
Goal
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 (all one- and two-variable methods), which you’ll be proud to present (poster).
Why You Need to Study Statistics
Fall 2015 Syllabus is below table
Fall 2015 schedule; Time: TR 1530-1645; Location: CTLB 300 (building 55, northeast of Zimmerman); Stat 427.002, CRN 54725; Stat 527.002, CRN 54726 + Peer mentors via UNM Stat 495/595: Statistics Education Practicum (SEP) Stat 495.002 or Stat 595.001, CRN 13764 or 55072 (named “Individual Study”)
News:
9/26 – Course notes
These are now posted above the time table instead of by each day’s assigned reading.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!
Timetable
Each week has this structure:- 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 and Thursday due by 5pm each day, submitted to UNM Learn (evaluated by TA within 1 week).
- Post-class (Thursday): Homework (crowdgrader, due following Thursday before class)
- Post-class (Following Thursday-Tuesday): Grading (crowdgrader, following 1 week + Tuesday before class)
Course notes and R code
pdf R Ch 0 Introduction to R, Rstudio, and ggplot pdf R Ch 1 Summarizing and Displaying Data pdf R Ch 2 Estimation in One-Sample Problems pdf R Ch 3 Two-Sample Inferences pdf R Ch 4 Checking Assumptions pdf R Ch 5 One-Way Analysis of Variance pdf R Ch 6 Nonparametric Methods pdf R Ch 7 Categorical Data Analysis pdf R Ch 8 Correlation and Regression pdf R Ch 9 Introduction to the Bootstrap pdf R Ch 10 Power and Sample size pdf R Ch 11 Data Cleaning pdf R ADA2 Ch 11 Logistic Regression lm_diag_plots.R function for a large set of standard diagnostic plots Notes from Fall 2015 using R: ADA1_notes_F15.pdf includes all chapters in one document.
Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 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/ADA1/ADA1_notes_F15.pdf.
(I reserve the right to continue to improve the materials throughout the semester.)
Erik’s NESARC data, nicotine and depression: .Rmd + .bib + EditRules = .html.
| Wk-Date | Cl | Topic | Reading, Video, Quiz | In-class Worksheet, Data | Homework | HW Submit Grading | Due before class |
|---|---|---|---|---|---|---|---|
| 00-08/18 | 00 | Install software, survey | read: installvideo: installquiz: survey | “Quiz 00” | |||
| 01-08/18 | 01 | Intro, data, poster | read: PDS Chs 2-3video: Rmd, Ch 2-3, Med records, crowdgrader,quiz: survey | CTLB video, Active Learning, 01 Syllabus subset, 01a Medical records Rmd html | (Intro to using RMarkdown: Rmd html) | ||
| 01-08/20 | 02 | Rmd, codebook, crowdgrader | video: 01 Personal codebook | Work as a group, each submit own copy (anonymously). 01a crowdgrader submit 8/18-8/20 16:30 grading 8/20 16:30-17:00 Hey, awesome work today, everyone! FB | 01 Personal codebook Rmd html | 01 crowdgrader 8/27 Submit 9/1 Grade | |
| 02-08/25 | 03 | Research questions | read: PDS Ch 2-4,video: Lit Rev biblio & Mendeleyquiz: 02 codebook and lit review | In-class: Rmd html Turn in one question of variable association. | (UNM Google Scholar) | Quiz 02 | |
| 02-08/27 | 04 | Literature review | In-class: Rmd html Turn in one citation to a research question. | 02 Literature review Rmd html bib (the assignment above is short of a research proposal Rmd html, we won’t be doing a research proposal as part of this class) | 02 crowdgrader 9/3 Submit 9/8 Grade | Turn in HW 01 | |
| 03-09/01 | 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 | In-class: Rmd html Look at datasets in R, create subset of data, rename variables, numerical summaries. | Quiz 03, Grade HW 01 | ||
| 03-09/03 | 06 | Plotting univariate | video: HW 03 vid | In-class: Rmd html Univariate plots of numerical and categorical variables. | 03 Data subset, univariate summaries and plots Rmd html (See the link above the table “Erik’s NESARC data, nicotine and depression”.) | 03 crowdgrader 9/10 Submit 9/15 Grade | Turn in HW 02 |
| 04-09/08 | 07 | Plotting bivariate | read: PDS Ch 9, Ch 00 R, Ch 11 R,video:quiz: quiz | In-class: Rmd html Complete at least one bivariate coding relationship. | Quiz 04, Grade HW 02 | ||
| 04-09/10 | 08 | Data cleaning | In-class: Rmd html Edit rules, run with dataset, assess exceptions, decide what to do with them. Erik’s EditRules. | 04 Rmd html | 04 crowdgrader 9/17 Submit 9/22 Grade | Turn in HW 03 | |
| 05-09/15 | 09 | Simple linear regression, intro | read: Ch 8.4, 8.2 Rvideo:quiz: quiz | In-class: Rmd html dat Build intuition using SLR App, interpret properties of linear regression fit. | Quiz 05, Grade HW 03 | ||
| 05-09/17 | 10 | Logarithm transformation | (novel example) | In-class: Rmd html dat Plot, transform, plot, and interpret. | 05 Rmd html | 05 crowdgrader 9/24 Submit 9/29 Grade | Turn in HW 04 |
| 06-09/22 | 11 | Correlation | read: Ch 8.1, 8.3.1 R, Ch 7.5.1 only sections on “conditional probability” and the following example Rvideo:quiz: quiz | In-class: Rmd html Data collection (hand span and word memory), correlation, regression to the mean. | Spurious Correlations | Quiz 06, Grade HW 04 | |
| 06-09/24 | 12 | Categorical contingency tables | In-class: Rmd html d1 Interpret condition proportions in two examples. | Simpson’s Paradox 06 Rmd html | 06 crowdgrader 10/1 Submit 10/6 Grade | Turn in HW 05 | |
| 07-09/29 | 13 | Inference, intro | read: Ch 2.1-2.2 Rvideo:quiz: quiz | In-class: Rmd html Guess Ages and Candy weights. | Quiz 07, Grade HW 05 | ||
| 07-10/01 | 14 | Parameter estimation (one-sample) | In-class: Rmd html Water on Earth. | 07 Rmd html PDS Data Sampling Designs: AddHealth, OOL, NESARC | 07 crowdgrader 10/9 Submit 10/13 Grade | Turn in HW 06 | |
| 08-10/06 | 15 | Hypothesis testing (two-sample) | read: Ch 2.3-end R Ch 3 R video: quiz: quiz | In-class: Rmd html one- and two-sample tests using data we collected in class. | Quiz 08, Grade HW 06 | ||
| 08-10/08 | Fall Break | 08 Rmd html | 08 crowdgrader 10/15 Submit 15/20 Grade | Turn in HW 07 | |||
| 09-10/13 | 16 | Paired data, assumption assessment | read: Ch 2.2.1, Ch 3.4 & 3.6, Ch 4, Ch 5video:quiz: quiz | In-class: Rmd html Paired data and checking model assumptions. | Quiz 09, Grade HW 07 | ||
| 09-10/15 | 17 | ANOVA, post-hoc comparisons | In-class: Rmd html ANOVA, model assumptions, and paired comparisons. | 09 Rmd html | 09 crowdgrader 10/22 Submit 10/27 Grade | Turn in HW 08 | |
| 10-10/20 | 18 | Nonparametric methods | read: Ch 6, Ch 7.2-7.4, Ch 10video:quiz: quiz | In-class: Rmd html NP one-sample tests and CIs, and ANOVA with pairwise comparisons. | Quiz 10, Grade HW 08 | ||
| 10-10/22 | 19 | Binomial and multinomial proportion tests | In-class: Rmd html dat Multinomial: World series number of games. | 10 Rmd html | 10 crowdgrader 10/29 Submit 11/3 Grade | Turn in HW 09 | |
| 11-10/27 | 20 | Two-way categorical tables | read: Ch 7.8-end, Ch 8.5-8.7video:quiz: quiz | In-class: Rmd html dat Popular kids. | Quiz 11, Grade HW 09 | ||
| 11-10/29 | 21 | Simple linear regression, inference | In-class: Rmd html Regression of height vs hand span using data from our class. | 11 Rmd html | 11 crowdgrader 11/5 Submit 11/10 Grade | Turn in HW 10 | |
| 12-11/03 | 22 | Logistic regression, intro | read: ADA2 Ch 11.1-3, 11.6, PDS Ch 16video:quiz: quiz | In-class: Rmd html AddHealth W4 Pregnancy. | Summary of Methods we’ve covered | Quiz 12, Grade HW 10 | |
| 12-11/05 | 23 | Experiments and observational studies | In-class: Rmd html Describing a study reported in the media. | 12 Rmd html | 12 crowdgrader 11/12 Submit 11/17 Grade | Turn in HW 11 | |
| 13-11/10 | 24 | Statistical communication | read: PDS Ch 18 quiz: no quiz | In-class: 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. | Quiz 13, Grade HW 11 | |
| 13-11/12 | 25 | Poster Preparation | In-class: Rmd html Work on designing poster content at the bottom of your HW document. | 13 Rmd html Work on your poster content. Try to complete your poster planning in your HW document. | 13 crowdgrader 11/19 Submit 11/24 Grade | Turn in HW 12 | |
| 14-11/17 | 26 | Posters wrapping up | Grade HW 12 | ||||
| 14-11/19 | 27 | Show poster | Course evaluation, submit receipt as in-class assignment. | 14 Rmd html Due next Wednesday. Complete and submit your poster in template format. | 14 crowdgrader 11/25 Submit 12/3 Grade | Turn in HW 13 | |
| 15-11/24 | 28 | Approve poster, final touches | ARI Graphix $9 poster printingOpen Mon-Wed 7:30-5:30, Closed Thu-Sun Open Mon 11/30 7:30-5:30 Do not use their website! Do: Email plotting@abqrepro.com, indicate to print “in color on bond paper” and attach poster pdf file. Price is $0.75/sq ft. | Have a peer mentor approve your poster for printing and presentation. Congratulations! | Grade HW 13 Turn in HW 14 tomorrow (Wed) | ||
| 15-11/26 | Thanksgiving break | ||||||
| 16-12/01 | 29 | POSTERS A | Poster sessions in SMLC | poster template pdf, Rnw, sty, bib, logo | |||
| 16-12/03 | 30 | POSTERS B | Poster sessions in SMLC | Prof Erhardt’s example poster pdf, Rnw Transition from Markdown to LaTeX Video for poster transition | Poster rubric | Grade HW 14 | |
| 17-12/08 | Finals week | (no final) |
Syllabus
Description: Statistical tools for scientific research, including parametric and non-parametric 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: Stat 145 (or other intro stats course) Semesters offered: Fall Lecture: Stat 427.002, CRN 54725; Stat 527.002, CRN 54726, TR 1530-1645; Location: CTLB 300 (building 55, northeast of Zimmerman) Video Office hours: Tue/Thu 12:30-13:30, and by appointment in SMLC 312 email: “Erik B. Erhardt” <erike@stat.unm.edu>, please include “ADA1” in subject line Textbook: Required books will be provided free by pdf on UNM Learn. Optional: 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. 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. 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.Teaching Assistants and Peer Mentors
Stat grad students TAs
Chauntal Andrews <andrewsc@unm.edu>, office hours Tue/Thu 14:00-15:00 in SMLC 301 Huan Yu <hyu122@unm.edu>, office hours Mon 14:00-15:30 and Fri 9:00-10:30 in SMLC 302Peer Mentors
Carrie Booth <boothc@unm.edu>, Education grad student, ADA course alumnus and Delta Alpha Pi Honor Society (Disability Achievement Pride) Member Armida Carbajal <armyjc@unm.edu>, Stat grad student Andisheh Dadashi <andisheh1986@unm.edu>, Stat grad student Jerry Hatch <jihatch@unm.edu>, ADA course alumnus, Stat MS student John Pesko <jpesko@unm.edu>, Stat PhD student Ana Oaxaca <aoaxaca@unm.edu>, ADA course alumnus Juan Pablo Madrigal Cianci <jpmadrigalc@unm.edu>, Applied Math grad student, ADA course alumnus Angela Gregory <agregory@unm.edu>, ADA course alumnus, MS Erin Ochoa <eochoa@unm.edu>, ADA course alumnus innovationAcademy videoStudent 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 data-supported 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.
- 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, five-number summary, confidence intervals, and p-values, 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 evidence-based 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 1-2 hours reading and video, 20 minute quiz.
- In-class assignments are due each day by the end of day (midnight), submitted to UNM Learn. Purpose: to struggle and find success in class with the concepts and skills. (About 24, includes class participation, 20% 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 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 1-4 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 30 minutes to grade 5 other students’s HW.
- 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 3-5 hours, using a template provided to you.
- Course surveys are due at the beginning and end of the course. Purpose: to participate in national project-based learning projects and improve course. (About 2, 4% of final grade.)
- Drop lowest 4 in-class, 2 quizzes, and 2 homework assignments (your worst two weeks).
- Take weighted average as discussed above.
- Divide ugrad grade by 0.95 and grad grade by 0.98 (a little extra boost for ugrads).
- Round this number up to the nearest integer (93.1 becomes a 94).
- Assign letter grades with this these cutoffs (get’s lenient below a B-)
| Grades | |
| Letter | at least |
| A+ | 97 |
| A | 94 |
| A- | 90 |
| B+ | 87 |
| B | 84 |
| B- | 80 |
| C+ | 75 |
| C | 71 |
- 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 citation
For 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 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. Peer mentor Carrie Booth <boothc@unm.edu>, 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.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
- Real-time feedback promotes efficient 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
Random stuff
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 “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/
Why stats now?
140,000 analysts needed. Important enough to have a US Chief Data Scientist (1) (2).Archive
Before first day: Step 0: Instructions for “Pre-course Software Install and Survey”: google account, crowdgrader, R+Rstudio, Mendeley, LaTeX, and a pre-course survey. All are required. Did you receive a registration error for Fall 2015? 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, as long as there are seats available I will override you into the course. Don’t worry.
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 | not | |||||
| 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 | |||||
Citing and using notes
Notes from Fall 2014 using R: ADA1_notes_F14.pdf includes all chapters in one document. Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 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/ADA1/ADA1_notes_F14.pdf.
Notes from Fall 2013 using R: ADA1_notes_F13.pdf includes all chapters in one document.
Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 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/ADA1/ADA1_notes_F13.pdf.
Notes from Fall 2012 using R: ADA1_notes_F12.pdf includes all chapters in one document.
Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 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/ADA1/ADA1_notes_F12.pdf.
Notes from Fall 2011 using Minitab: ADA1_notes_F11.pdf includes all chapters in one document.
Lecture notes for Advanced Data Analysis 1 (ADA1) Stat 427/527 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/ADA1/ADA1_notes_F11.pdf.