Traveling (Salesman) Pilot

Touring airports

Shortest tour of all airports within each state of the USA, ignoring terrain and airspace. Download Google Earth files for all the tours: I downloaded the FAA Facilities and Runways databases, joined them together, filtered to keep only the longest runway per airport, then filtered based on the criteria above.  Then, for each state and for the USA lower 48, I computed the distance between all airports accounting for the WGS84 ellipsoid earth curvature and took the shortest tour from multiple iterations of solving the traveling salesman problem (TSP).  Finally, I pulled terrain maps using the Google Maps API and plotted the labeled airports and the tour on the map with summaries of the number of airports and the tour distance.  Tables of the tour details are also provided.  State tours are likely to be minimal, but the USA48 tour is likely only (very) close to minimal since the problem is NP-hard. This project came from wondering what it would take and whether it would be fun to visit all of the airports in NM while on a cross-country flight with my brother.

Airports with Paved, 1500+’, Public runways

  • Criteria:
    • USA FAA Airports
    • Surface Asphalt or Concrete in fair or better condition
    • Longest runway length at least 1500′
    • Status open
    • Public use
Some airports may not be labeled when crowded.  Some distances may be greater than the legend maximum.
ST State Airports Dist (nm) Map Tour kml
USA Lower 48 3591 75930 png, pdf html, csv kmz
AK Alaska 53 5895 png, pdf html, csv kmz
AL Alabama 77 1532 png, pdf html, csv kmz
AR Arkansas 93 1727 png, pdf html, csv kmz
AZ Arizona 67 1991 png, pdf html, csv kmz
CA California 229 4109 png, pdf html, csv kmz
CO Colorado 67 1845 png, pdf html, csv kmz
CT Connecticut 17 222 png, pdf html, csv kmz
DC Dist. Of Columbia 3 57 png, pdf html, csv kmz
DE Delaware 6 128 png, pdf html, csv kmz
FL Florida 103 1951 png, pdf html, csv kmz
GA Georgia 101 1906 png, pdf html, csv kmz
HI Hawaii 14 611 png, pdf html, csv kmz
IA Iowa 90 1806 png, pdf html, csv kmz
ID Idaho 52 1476 png, pdf html, csv kmz
IL Illinois 84 1713 png, pdf html, csv kmz
IN Indiana 79 1296 png, pdf html, csv kmz
KS Kansas 111 2369 png, pdf html, csv kmz
KY Kentucky 56 1270 png, pdf html, csv kmz
LA Louisiana 66 1266 png, pdf html, csv kmz
MA Massachusetts 35 534 png, pdf html, csv kmz
MD Maryland 29 615 png, pdf html, csv kmz
ME Maine 38 901 png, pdf html, csv kmz
MI Michigan 120 2283 png, pdf html, csv kmz
MN Minnesota 114 2469 png, pdf html, csv kmz
MO Missouri 108 2082 png, pdf html, csv kmz
MS Mississippi 76 1463 png, pdf html, csv kmz
MT Montana 74 2408 png, pdf html, csv kmz
NC North Carolina 85 1735 png, pdf html, csv kmz
ND North Dakota 71 1737 png, pdf html, csv kmz
NE Nebraska 76 1846 png, pdf html, csv kmz
NH New Hampshire 16 352 png, pdf html, csv kmz
NJ New Jersey 31 390 png, pdf html, csv kmz
NM New Mexico 52 1936 png, pdf html, csv kmz
NV Nevada 33 1383 png, pdf html, csv kmz
NY New York 83 1585 png, pdf html, csv kmz
OH Ohio 110 1636 png, pdf html, csv kmz
OK Oklahoma 110 2199 png, pdf html, csv kmz
OR Oregon 74 1912 png, pdf html, csv kmz
PA Pennsylvania 88 1408 png, pdf html, csv kmz
RI Rhode Island 7 107 png, pdf html, csv kmz
SC South Carolina 57 1027 png, pdf html, csv kmz
SD South Dakota 56 1667 png, pdf html, csv kmz
TN Tennessee 75 1458 png, pdf html, csv kmz
TX Texas 305 6641 png, pdf html, csv kmz
UT Utah 45 1387 png, pdf html, csv kmz
VA Virginia 58 1314 png, pdf html, csv kmz
VT Vermont 11 318 png, pdf html, csv kmz
WA Washington 96 1837 png, pdf html, csv kmz
WI Wisconsin 95 1789 png, pdf html, csv kmz
WV West Virginia 27 794 png, pdf html, csv kmz
WY Wyoming 35 1455 png, pdf html, csv kmz
AS American Samoa 3 151 png, pdf html, csv kmz
GU Guam 1 0 png, pdf html, csv kmz
MP N Mariana Islands 3 127 png, pdf html, csv kmz
PR Puerto Rico 8 201 png, pdf html, csv kmz
QM Midway Islands 1 0 png, pdf html, csv kmz
VI Virgin Islands 2 79 png, pdf html, csv kmz

Airports with Grass or Dirt runways (backcountry)

  • Criteria:
    • USA FAA Airports
    • Surface Grass, Turf, Dirt, Gravel, or Sand in any condition
    • Status open
    • Public or private use
Some airports may not be labeled when crowded.  Some distances may be greater than the legend maximum.
ST State Airports Dist (nm) Map Tour kml
USA Lower 48 8228 91843 png, pdf html, csv kmz
AK Alaska 514 10789 png, pdf html, csv kmz
AL Alabama 88 1304 png, pdf html, csv kmz
AR Arkansas 118 1557 png, pdf html, csv kmz
AZ Arizona 88 1746 png, pdf html, csv kmz
CA California 144 3166 png, pdf html, csv kmz
CO Colorado 204 2438 png, pdf html, csv kmz
CT Connecticut 30 287 png, pdf html, csv kmz
DE Delaware 22 166 png, pdf html, csv kmz
FL Florida 330 2800 png, pdf html, csv kmz
GA Georgia 208 2066 png, pdf html, csv kmz
HI Hawaii 5 352 png, pdf html, csv kmz
IA Iowa 122 1858 png, pdf html, csv kmz
ID Idaho 179 2544 png, pdf html, csv kmz
IL Illinois 361 3002 png, pdf html, csv kmz
IN Indiana 294 2048 png, pdf html, csv kmz
KS Kansas 258 2744 png, pdf html, csv kmz
KY Kentucky 79 1124 png, pdf html, csv kmz
LA Louisiana 163 1627 png, pdf html, csv kmz
MA Massachusetts 36 436 png, pdf html, csv kmz
MD Maryland 106 841 png, pdf html, csv kmz
ME Maine 79 1069 png, pdf html, csv kmz
MI Michigan 242 2447 png, pdf html, csv kmz
MN Minnesota 228 2846 png, pdf html, csv kmz
MO Missouri 247 2673 png, pdf html, csv kmz
MS Mississippi 96 1351 png, pdf html, csv kmz
MT Montana 183 3175 png, pdf html, csv kmz
NC North Carolina 222 2209 png, pdf html, csv kmz
ND North Dakota 207 2507 png, pdf html, csv kmz
NE Nebraska 146 2140 png, pdf html, csv kmz
NH New Hampshire 35 438 png, pdf html, csv kmz
NJ New Jersey 58 401 png, pdf html, csv kmz
NM New Mexico 72 2021 png, pdf html, csv kmz
NV Nevada 59 1680 png, pdf html, csv kmz
NY New York 269 2392 png, pdf html, csv kmz
OH Ohio 287 2137 png, pdf html, csv kmz
OK Oklahoma 239 2335 png, pdf html, csv kmz
OR Oregon 223 2595 png, pdf html, csv kmz
PA Pennsylvania 296 2425 png, pdf html, csv kmz
RI Rhode Island 2 61 png, pdf html, csv kmz
SC South Carolina 87 1099 png, pdf html, csv kmz
SD South Dakota 110 2032 png, pdf html, csv kmz
TN Tennessee 132 1487 png, pdf html, csv kmz
TX Texas 945 9513 png, pdf html, csv kmz
UT Utah 45 1279 png, pdf html, csv kmz
VA Virginia 197 1882 png, pdf html, csv kmz
VT Vermont 48 477 png, pdf html, csv kmz
WA Washington 222 2366 png, pdf html, csv kmz
WI Wisconsin 323 2798 png, pdf html, csv kmz
WV West Virginia 35 742 png, pdf html, csv kmz
WY Wyoming 64 1590 png, pdf html, csv kmz
MP N Mariana Islands 1 0 png, pdf html, csv kmz
PR Puerto Rico 2 97 png, pdf html, csv kmz

All Airports

  • Criteria:
    • USA FAA Airports
    • All in any condition
    • Status open
    • Public or private use
Some airports may not be labeled when crowded.  Some distances may be greater than the legend maximum.
ST State Airports Dist (nm) Map Tour kml
USA Lower 48 12435 123426 png, pdf html, csv kmz
AK Alaska 571 12595 png, pdf html, csv kmz
AL Alabama 177 2041 png, pdf html, csv kmz
AR Arkansas 224 2340 png, pdf html, csv kmz
AZ Arizona 185 2893 png, pdf html, csv kmz
CA California 490 5879 png, pdf html, csv kmz
CO Colorado 268 3106 png, pdf html, csv kmz
CT Connecticut 48 367 png, pdf html, csv kmz
DC Dist. Of Columbia 3 57 png, pdf html, csv kmz
DE Delaware 29 192 png, pdf html, csv kmz
FL Florida 485 3668 png, pdf html, csv kmz
GA Georgia 330 2917 png, pdf html, csv kmz
HI Hawaii 31 1465 png, pdf html, csv kmz
IA Iowa 192 2484 png, pdf html, csv kmz
ID Idaho 243 2934 png, pdf html, csv kmz
IL Illinois 440 3344 png, pdf html, csv kmz
IN Indiana 372 2514 png, pdf html, csv kmz
KS Kansas 341 3508 png, pdf html, csv kmz
KY Kentucky 147 1672 png, pdf html, csv kmz
LA Louisiana 245 2141 png, pdf html, csv kmz
MA Massachusetts 70 657 png, pdf html, csv kmz
MD Maryland 146 1008 png, pdf html, csv kmz
ME Maine 119 1312 png, pdf html, csv kmz
MI Michigan 337 3340 png, pdf html, csv kmz
MN Minnesota 317 3635 png, pdf html, csv kmz
MO Missouri 369 3426 png, pdf html, csv kmz
MS Mississippi 187 2147 png, pdf html, csv kmz
MT Montana 255 3986 png, pdf html, csv kmz
NC North Carolina 341 2958 png, pdf html, csv kmz
ND North Dakota 263 2897 png, pdf html, csv kmz
NE Nebraska 210 2778 png, pdf html, csv kmz
NH New Hampshire 56 551 png, pdf html, csv kmz
NJ New Jersey 96 585 png, pdf html, csv kmz
NM New Mexico 132 2775 png, pdf html, csv kmz
NV Nevada 100 2150 png, pdf html, csv kmz
NY New York 352 2817 png, pdf html, csv kmz
OH Ohio 409 2811 png, pdf html, csv kmz
OK Oklahoma 364 3354 png, pdf html, csv kmz
OR Oregon 325 3394 png, pdf html, csv kmz
PA Pennsylvania 393 2867 png, pdf html, csv kmz
RI Rhode Island 9 125 png, pdf html, csv kmz
SC South Carolina 157 1554 png, pdf html, csv kmz
SD South Dakota 146 2437 png, pdf html, csv kmz
TN Tennessee 222 2143 png, pdf html, csv kmz
TX Texas 1472 12660 png, pdf html, csv kmz
UT Utah 95 1930 png, pdf html, csv kmz
VA Virginia 279 2290 png, pdf html, csv kmz
VT Vermont 60 541 png, pdf html, csv kmz
WA Washington 357 3119 png, pdf html, csv kmz
WI Wisconsin 411 3239 png, pdf html, csv kmz
WV West Virginia 71 1029 png, pdf html, csv kmz
WY Wyoming 96 2065 png, pdf html, csv kmz
AS American Samoa 3 151 png, pdf html, csv kmz
GU Guam 2 20 png, pdf html, csv kmz
MP N Mariana Islands 4 481 png, pdf html, csv kmz
PR Puerto Rico 14 253 png, pdf html, csv kmz
QM Midway Islands 1 0 png, pdf html, csv kmz
QW Wake Island 1 0 png, pdf html, csv kmz
VI Virgin Islands 2 79 png, pdf html, csv kmz

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Core Curriculum Teaching Fellow 2018-19

As a Core Curriculum Teaching Fellow, my project will develop a new course called “Statistics for Research” focusing on innovation, undergraduate research, and writing across the curriculum as an alternative to the traditional Introduction to Statistics, Stat 145. Intro Stats is one of the largest courses on campus with 1000-1200 students per semester in over 20 sections. This Statistics for Research course will cover the traditional material, but in a modern evidence-based way by integrating real data in the context of case studies, fostering active learning, and using technology to explore concepts and analyze large real datasets and communicate the results. This is a computer- and a project-based course in the style of Dierker’s Passion-Driven Statistics and modeled directly after the already-successful UNM Stat 427/527 Advanced Data Analysis 1 and 2 where I’ve piloted these learning strategies for advanced undergraduates and graduate students for four semesters.  Unlike my previous six-section intervention study showing active learning increases success by a third of a letter grade for everyone, this will be a pilot course developed for one or two sections. This implementation is just one of the many evidence-based recommendations I made as a 2016-17 Teaching Fellow for improving statistics education at UNM, including offering multiple versions (Stat Literacy with a first-year learning community, Stats for Research, Advanced Math-Stats); train/mentor our TAs prior to their teaching a course; offer a new Undergraduate Stat Ed Practicum course for all undergrad stat majors to serve as peer learning facilitators in at least one intro stat course; use multidisciplinary project-based learning to improve the outcomes of statistical interest, attitude, and confidence for under-represented minority students; and make other small changes such as moving the final from the first day of finals at 7:30 AM to a later time when students are shown to have greater success. A long-term goal is to hire a professor of practice in statistics for continued leadership and research in statistical education at UNM.
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UNM Stats R Package Development “R-Hack-a-Pack”

Workshop at The UNM Dept of Math & Stats on Friday 8/17/2018 10AM-5PM. Erik Erhardt
Please click this link to RSVP.
No workshop fee.  Food and caffeine contribution: $10-15. Math & Stat event link.
 
I’m going to host an R Package Development R-Hack-a-Pack one-day workshop on Friday 8/17 from 10-5.  This is like a “hack-a-thon”, but, instead of tackling a dataset analysis, the goal is to learn and use the skills to package R code.  The first hour or so will cover the basic ideas of packaging code using modern tools such as RStudio, devtools, and usethis.  The rest of the day is intended as a protected time where you can turn your code into a package. We’ll cover the basics of creating a package, package testing, writing vignettes, and using github (and CRAN) for version control and making your package available.
To get the most out of this workshop, bring a package idea to start on.  A few ideas:
  1. If you already have a set of functions that you load with script(“my_functions.R”), then you’re an afternoon away from making a great package.
  2. If you have a large script with repeated code, then you can start by turning the repeated code into functions, package those functions, and write a short vignette to perform the same analysis using your package.
  3. If you have an idea for a new package to develop, bring that idea with you and we can consider trying to develop it during the workshop.
  4. My project is to package all the code and data for my two-semester data analysis course while I’m not helping others.
Food and helper:
If you would like to volunteer to be a helper during the day for logistical issues, please email me directly.  It would be nice to have lunch and an afternoon coffee break with a snack.

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R package: RmdNameChunk

Enumerate Rmd code chunks

I wrote the RmdNameChunk package to automatically name the Rmd code chunks. This is important for my workflow because I don’t click “Knit” in RStudio, instead I run rmarkdown::render(fn). This has the advantage of using the console environment instead of the RMarkdown environment, so all the objects are available for manipulation in the console. However, it is hard to debug when there are scores of unnamed chunks. Now, I can easily name all the code chunk and can quickly identify where issues are. Install from github: https://github.com/erikerhardt/RmdNameChunk

Example

Install from source by running this command:
devtools::install_github("erikerhardt/RmdNameChunk")
Read an Rmd file, update existing prefixed code chunks, and renumber.
library(RmdNameChunk)
rmd_name_chunks(
  fn_in  = "test_in.Rmd"
, fn_out = "test_out.Rmd"
, prefix_chunk_name = "chunk-"
)
Review the input and output files to see how the chunk header names have been updated. Below is an example of the two Rmd files in the vignette. test_in.Rmd was read in and test_out.Rmd was created. The chunk headers are shown below from each file.
test_in.Rmd
8 : ```{r setup, include=FALSE}
22 : ```{r cars}
28 : ```{r}
32 : ```{ r }
36 : ```{r, echo=FALSE}
40 : ```{ r, eval=FALSE}
44 : ```{r , eval=FALSE}
48 : ```{r chunk-2, eval=FALSE}
52 : ```{r chunk-XXX , eval=FALSE}
56 : ```{r chunk-XXX2 , eval=FALSE}
60 : ```{r chunk-XXX3 , eval=FALSE}
64 : ```{r chunk-XXX4 , eval=FALSE}
68 : ```{r chunk-XXX5 , eval=FALSE}
72 : ```{r chunk-XXX6 , eval=FALSE}
81 : ```{r pressure, echo=FALSE}
These code chunk headers were changed to those below:
test_out.Rmd
8 : ```{r setup, include=FALSE}
22 : ```{r cars}
28 : ```{r chunk-01}
32 : ```{r chunk-02}
36 : ```{r chunk-03, echo=FALSE}
40 : ```{r chunk-04, eval=FALSE}
44 : ```{r chunk-05, eval=FALSE}
48 : ```{r chunk-06, eval=FALSE}
52 : ```{r chunk-07, eval=FALSE}
56 : ```{r chunk-08, eval=FALSE}
60 : ```{r chunk-09, eval=FALSE}
64 : ```{r chunk-10, eval=FALSE}
68 : ```{r chunk-11, eval=FALSE}
72 : ```{r chunk-12, eval=FALSE}
81 : ```{r pressure, echo=FALSE}

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On scientific writing

The resources below are helping me become a more disciplined writer capable of writing more first-author statistics papers. Since this is not an academic paper, I’m being loose about citation style but am including links, titles, and authors. The point is to write, not to stress about reviewing.

1 How to write a lot

Visiting my friend Jennifer in San Diego, I came across a book on her shelf that has motivated me to write more: “How to Write a Lot: A Practical Guide to Productive Academic Writing” by Paul J. Silvia. The main ideas that I took away from the book were well summarized in a University of Oregon post – “Becoming a Productive Faculty Writer – A Summary of Best Practices” – which also has a variety of helpful resources. The primary takeaways for me are:
  1. Write Daily. Write on a schedule and don’t move your writing for other meetings. Use phrases like “recurring intractable obligation” and “previously encombered temporal placement” to decline invitations.
  2. Have a Goal. Project goals, daily goals. Prewriting activities count.
  3. Stop at a Good Place. It gives you a better place from which to begin the next day. Jot down critical ideas to get started from.
  4. Limit Distractions. Shut the door and tune out the outside world.
  5. Take Care of Yourself. Eat well, sleep well, exercise well, and work well.

2 Writing scientific manuscripts

There is lots of harmonious advice for writing academic papers and getting these manuscripts through review and into publication. Below are the points of advice that resonated with me. My main conclusions are from these two sources (also Gelman): O’Connor’s algorithm overview is the order of parts to write that I’m using as a basis below.  Baldwin’s order is slightly different and indicated in each citation. [caption id="" align="aligncenter" width="240"]Overview of the formulation of a scientific manuscript. (O’Connor) Overview of the formulation of a scientific manuscript. (O’Connor)[/caption]

1. Figures/Tables

“Construction of first-draft figures and tables based on data is the critical first step toward preparation of an outline for the manuscript.” (O’Connor) “Paste the figures and tables on a wall in their approximate order of appearance. This will provide the skeleton from which to begin the construction of a more detailed outline and draft.” (O’Connor) “Write the first draft of figure legends. Make each legend’s opening sentence into a figure title to describe the variables compared.” (O’Connor) “The entire story of your paper should be comprehensible from this abstract and the figures and tables.” (2b. Baldwin) “Each figure should illustrate at least one important take-home message of your paper and ideally be understandable in 30 seconds to an uninformed scientific reader.” (Baldwin) Caption: “The first sentence should summarize the main point of the figure.” “The entire figures should be fully comprehensible from the figure caption.” “Table legends should include only a stand-alone first sentence.” (Baldwin) For more on figures, see our chapter in the 4th ed of the “Handbook of Psychophysiology”.

2. Summary Statements

“These important statements are conclusions summarizing the major contributions of the manuscript to the scientific community. Use short rigid statements usually containing cause/effect words.” (O’Connor)

3. Scientific Audience

Identify the scientific audience and journals. (O’Connor)

4. Materials and Methods

“Write the materials and methods section to supplement and explain the figure legends.” (O’Connor) “Write while you are still conducting experiments.” (1.+4. Baldwin)

5. Re-evaluate Data

“For each figure and table, make a note of which summary statement it addresses.” (O’Connor)

6. Results

“Once each figure and table has a legend describing the mathematical variables compared, then the first sentence of each results paragraph is simply the results of this comparison.” (O’Connor) “The Results section should highlight all of the conclusions that you can draw from your data.” (3. Baldwin)

7. Discussion/Conclusions

“Convert by logical arguments the relations of mathematical variables stated in the results section into mechanistic interpretations of cause and effect. Simply restate the data relation from each results paragraph and convert each to mechanistic conclusions.” (O’Connor) Table 1 (O’Connor) gives examples of results words for data relations vs. discussion words for logic and mechanism. “The discussion should start with a paragraph which succinctly states your motivation, and the conclusions that you draw from your data that do not require discussion and ends with an introduction to the weaker conclusions that you would like to draw, but do require discussion.” “The following paragraphs should discuss the conclusions that you would like to draw from your data, but require discussion because the inferences are indirect, or your data or the data in the literature are contradictory.” “The last paragraph should summarize all major conclusions from the discussion of your results and indicate future research directions.” (6. Baldwin)

8. References

This is taken care of by a reference manager with well-curated reference data and style files.

9. Introduction

“First, summarize the subject and review the literature to allow the reader to (i) understand the statements in the Results and Discussion, (ii) understand how the statements fit into the extant scientific body of knowledge, and (iii) Understand that the conclusions are indeed novel, the next step in the knowledge of the subject.” (O’Connor) “Write the last paragraph of the Introduction first; this should be an abbreviated road map of the question that you are addressing and the means by which you answered the question” (5. Baldwin) “Circle all of the words and concepts that you used in this last paragraph that need to be introduced and elaborated on in preceding paragraphs of the Introduction. In this way, you will be reverse-engineering the entire Introduction from your last paragraph.” (Baldwin) Remove the fluff.

10. Title

“The title should be a positive statement from the summary statements.” (O’Connor)

11. Conclusion Paragraph

“In this paragraph, restate the logical conclusions and explain why these conclusions are important, how they will influence future thinking in this and other fields. In the introduction, these conclusions were the next step. Now discuss the future based on the conclusions in this manuscript or an alternative path to further substantiate the validity of your conclusions. Also, state the relevance of results in the present manuscript to other fields.” (O’Connor)

12. Abstract

“Use the abstract in general terms to describe the most important points in the work.” (O’Connor) “Write a rough draft of the abstract … to create the roadmap of the logic of the results and conclusions that you will be developing.” (2b. Baldwin)

13. Revise, revise, revise.

Erase your memory of your writing and see it fresh.
  • Ask others to read it.
  • Read it after time has passed.
  • Have someone read it to you. (Baldwin)

3 Words and phrases

Thanks thesaurus.com. When you can’t find the phrase to get you started, start with “70 useful sentences for academic writing” by Luiz Otávio Barros.

4 Presentations

Use story structure in the first five slides to set up the scenario and characters in your heroic journey. Chapter 4 of Cliff Atkinson’s “Beyond Bullet Points” tells you how. Introduction:
  1. The Setting Headline (Where am I, and when is it?)
  2. The Role Headline (Who am I in this setting?)
  3. The Point A Headline (What challengee do I face in this setting?)
  4. The Point B Headline (Where do I want to be?)
    1. (The Gap Between A and B) (Why am I here?)
  5. The Call to Action Headline (How do I get from A to B?)
Body:
Chapter 5 was also helpful for structuring the remaining slides in a hierarchy of key points (sections) with explanations (subsections) and details (slides).

Key Point 1
Analysis 1
Fact 1
Fact 2
Fact 3
Analysis 2
Fact 1
Fact 2
Fact 3
Analysis 3
Fact 1
Fact 2
Fact 3

Key Point 2
Analysis 1
Fact 1
Fact 2
Fact 3
Analysis 2
Fact 1
Fact 2
Fact 3
Analysis 3
Fact 1
Fact 2
Fact 3

Key Point 3
Analysis 1
Fact 1
Fact 2
Fact 3
Analysis 2
Fact 1
Fact 2
Fact 3
Analysis 3
Fact 1
Fact 2
Fact 3

Conclusion (recap):
The Point A Headline (What challenges do I face in this setting?)
The Point B Headline (Where do I want to be?)
(The Gap Between A and B) (Why am I here?)
The Call to Action Headline (How do I get from A to B?)
 

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Sabbatical

I’m enjoying Sabbatical during the Fall 2017 – Spring 2018 academic year.  My Sabbatical Leave Plan includes visiting both UC Irvine and the Mind Research Network to:
  • learn more about Bayesian graphical models,
  • learn more about Hamiltonian Monte Carlo (HMC),
  • learn about their statistical and computational implementations, and
  • apply both to extend current models in the application to fMRI brain imaging data.
  • Continue UNM 100-level statistics and mathematics education initiatives to understand factors influencing student success and find strategies to increase success.

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Book Published: All Join Hands: Dances and stories

Richard Wilson book page includes purchasing details. Six and a half years ago we called for contributions.  Now we contribute this book back to the FolkMADS community.

Introduction to the first edition

This book is the fruit of an old idea, a long fallow period, and a final burst of enthusiastic commitment. The idea was to provide Richard with a meaningful focus and contribution during his last year of life. He wrote and wrote, filling notebooks; Lauren transcribed what she could. We photocopied all his cards that could be found in October 2010; Katherine provided an initial transcription. This book has taken many forms in its many revisions and reimaginings: experimenting with different paper sizes, interleaving the stories from other people in with his own story, modifying the dance formats, and finally returning to this fairly simple format. The long lull, after much compilation and redrafting, was partly due to my grieving after Richard passed; with Richard gone the urgency of the project had passed, since my primary commitment was to him. Then I allowed other projects to take over, including the trials of earning tenure as faculty at UNM. Finally, after almost six years, it is done. While I may not have been able to realize the book I envisioned, this is it. I’m heartened by a thoughtful and generous community to whom I present this gift and tribute. Five years since Richard’s passing, I still feel his spirit and gentleness as I teach new dancers and callers, just as he taught me, feeling how each foot lifted is as important as how each is set upon. Erik Barry Erhardt Albuquerque, NM October 2016
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Acumen in Statistics