Archived from Fall 2015 (Current year here.)

### 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.

# Timetable

Each week has this structure:
1. Pre-class (Tuesday): Reading, Video, Quiz (due before class — solutions become available Tue 3:30, after the quiz is due)
2. In-class: Activities in class Tuesday and Thursday due by 5pm each day, submitted to UNM Learn (evaluated by TA within 1 week).
3. Post-class (Thursday): Homework (crowdgrader, due following Thursday before class)
4. Post-class (Following Thursday-Tuesday): Grading (crowdgrader, following 1 week + Tuesday before class)
UNM Learn for content, YouTube Video playlist (try 1.5 speed, then pause as you need). Video: Upgrading R on Windows.

### Course notes and R code

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 2-3Med 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

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 302

## Student learning outcomes

At the end of the course, you will be able to: (student results: R, all years20152014, 20132012) General outcomes:
1. Organize knowledge in graphs, tables, and code to support concise, comprehensible, and scientifically defensible written interpretations to produce knowledge within a reproducible research environment.
2. Distinguish a testable scientific hypothesis or data-supported interpretation from an opinion.
3. Understand from a data story the goals of the study and apply the correct statistical procedure.
4. Explain the scientific aspects of a problem to nonscientists in a fashion that enhances understanding and decision making.
Topical outcomes:
1. Define parameters of interest and hypotheses in words and notation.
2. 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!)
3. Distinguish between statistical significance and scientific relevance.
4. 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.
5. 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.
6. Identify and explain the statistical methods, assumptions, and limitations used in reported studies in scientific literature or popular media.
7. 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.
8. Make evidence-based decisions by constructing and deciding between testable hypotheses using appropriate data and methods.
9. 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:
1. Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.
2. Discussion – interaction with classmates to assemble and synthesize information you’d utilizing the collective skills and knowledge base of the group.
3. 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.)
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. Final grade calculation: (so much rounding up!) I’m really proud of my class; you’ve worked hard this semester in a new format.  I was especially pleased with the closing poster session, a celebration of what we were able to accomplish.
1. Drop lowest 4 in-class, 2 quizzes, and 2 homework assignments (your worst two weeks).
2. Take weighted average as discussed above.
4. Round this number up to the nearest integer (93.1 becomes a 94).
5. 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
All homework assignments in this class are electronic, submitted to crowdgrader.com for grading, except for the final poster. Crowdgrader:
1. Students usually get far more feedback on their work than they would get from over-worked teaching assistants/faculty.
2. 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).
3. In exchange for this, they need to put in some amount of work in reviewing the work of others.
4. 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.
Late assignments will not be accepted. Rubrics guide assessment (and self-assessment) of homework, code, projects, exams, and presentations.  Each assignment will have its own specific rubric. All R code for the assignment should be included with the part of the problem it addresses (for code and output use a fixed-width font, such as Courier). Do NOT use your R code and output as your answer to the problem, but include them to show me how you arrived at your answer. Your prose solution (in a non-fixed-width font) should be provided in addition to R output.

### 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
“It encourages me to engage actively with the course material and take responsibility for my learning.”

## GAISE Connections

Our six recommendations include the following:
1. Emphasize statistical literacy and develop statistical thinking
2. Use real data
3. Stress conceptual understanding, rather than mere knowledge of procedures
4. Foster active learning in the classroom
5. Use technology for developing conceptual understanding and analyzing data
6. 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

### Why stats now?

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