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[[research]] [[office hours]] [[courses]] [[questions?]]\n
If you are wanting to use QwikiWeb for your own website, there is an easy way to delete all the existing tiddler content and start over with a blank copy. This option should be used with caution as it will erase every tiddler in the existing file. \n\n''How to DeleteAllTiddlers:''\n#Make sure that you have downloaded the QwikiWeb file and are viewing that copy on your computer\n#Make sure that you are in EditMode by clicking on the "show advanced options" command. The DeleteAllTiddlers feature will not activate if EditMode is not enabled.\n#Use the save changes command so that you have a backup of the tiddler content (enable backups if necessary)\n#Click in the search box and type in the phrase DeleteAllContent\n#You will be asked to confirm that you want to delete the tiddler content; click OK to proceed\n#All tiddlers will be deleted, and you will be presented with a screen where you can setup new values for the site title, subtitle, main menu, etc.\n#When you have completed your edits, save the changes again to preserve your new content
Odds and ends of javascript that I've written\n\n*fractal curves\n*a race clock with javascript objects\n*[[a test|javascript:void(alert('OK?'))]]\n
From time to time I cobble up a solution to a ~LaTeX typesetting problem that someone* brings me. Here are some of my efforts:\n\n*[[How do I put computer code in a box?|./software/LaTeX/boxes.pdf]]\n*[[How do I put a matrix inside a matrix?|./software/LaTeX/nestedMatrices.pdf]]\n*[[How can I put a table sideways on a page?|./software/LaTeX/RotatingTables.pdf]]\n\n *Dr Roy asks the really tough ones!
*[[office hours]]\n*[[courses]]\n*[[data sets]]\n*''[[Hot Notes|]]''\n*[[questions?]]\n*''[[Stats Club|]]''\n*[[software]]\n*[[reading list]]\n*[[research]]\n*[[teaching]]\n*[[vita]]\n*''[[weblog |]]''
''Course description'': Probability and Statistics for the Biosciences [TCCN: MATH 2342.] (3-0) 3 hours credit. Prerequisite: MAT 1193 or an equivalent. Probability and statistics from a dynamical perspective, using discrete-time dynamical systems and differential equations to model fundamental stochastic processes such as Markov chains and the Poisson processes important in biomedical applications. Specific topics to be covered include probability theory, conditional probability, Markov chains, Poisson processes, random variables, descriptive statistics, covariance and correlations, the binomial distribution, parameter estimation, hypothesis testing and regression. (Formerly STA 1404. Credit cannot be earned for both STA 1403 and STA 1404.)\n\nThe course is organized into 6 topic areas. In the fall and spring semesters, we cover two topics, take an exam, rinse and repeat until finals. In the summer semester (usually the first 5-week session) we cover 3 areas, take and exam, etc.\n\n* ''Proportions''\n** [[simple probability|./lectures/STA1403/probability_1.pdf]]\n** [[the birthday problem|./lectures/STA1403/theBirthdayProblem.pdf]]\n** [[binomial and hypergeometric models|./lectures/STA1403/binomial_and_hypergeometric_models.pdf]]\n** [[inference with proportions|./lectures/STA1403/reasoning_with_proportions.pdf]]\n* ''Categories''\n** [[conditional probability|./lectures/STA1403/conditional_probability.pdf]]\n** [[multinomial models and the chi-square test|./lectures/STA1403/multipleProportions.pdf]]\n* ''Waiting Times and Counting''\n** [[random variables|./lectures/STA1403/random_variables.pdf]]\n** [[expectation|./lectures/STA1403/expectations.pdf]]\n** [[Poisson processes|./lectures/STA1403/Poisson_process.pdf]]\n* ''Sampling Distributions''\n** [[estimation|./lectures/STA1403/estimates.pdf]]\n* ''ANOVA''\n** [[one-way ANOVA|./lectures/STA1403/ANOVA.pdf]]\n* ''Regression''\n** [[correlation and regression|./lectures/STA1403/simpleRegression.pdf]]
lecturer in statistics, department of management science and statistics
michael anderson [img[Mike Anderson|mini-me.jpg]]\n
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*Richard Feynmann, ''//"What Do You Care What Other People Think?"//'' \n**Revealing sketches from one of the geniuses of the 20th century.\n*Dave Sobel, ''//Longitude : The True Story Lone Genius Who Solved Greatest Scientific Problem his Time//'' \n**William Harrison and his amazing chronometer.
The following journals are a good source of statistical applications:\n\n* //American Journal of Epidemiology//\n* //British Medical Journal//\n* //Statistics in Medicine//\n\nThese are available online through the UTSA Library (use the e-journal locator)
* Donald E. Knuth, ''//Fundamental Algorithms//'', The Art of Computer Programming, Vol. I. What Feller is to probability, Knuth is to computing. In addition, his books are highly mathematical, with loads of discrete math, number theory, and combinatorics. This is just the stuff for analysis of algorithms and discrete probability models.\n*Press, et. al., ''//Numerical Recipes in FORTRAN//''. This book comes in many flavors, pick your favorite programming language. The code all works, the book is the most fabulous software documentation ever written, explaining how each and every routine works along with its mathematical justification.\n*Heikki Ruskeepaa;,'' //Mathematica Navigator//'' This may be my ultimate Mathematica how-to book.\n*Jerrold Wagener, ''//FORTRAN 77, Principles of Programming//'' I dusted this one off recently, and it holds up surprisingly well. Great for non-programmers.
I'm teaching or have current preparations for the following courses. Check them out for lecture notes:\n*[[STA 1053]] Introductory Statistics //Fall 2013//\n*[[STA 1403|]] Statistics for the Biosciences //Fall 2014, Spring 2015//\n*[[STA 2303]] Applied Probability and Statistics for Engineers (usually cross-listed and combined with [[STA 3513]])\n*[[STA 3513]] Probability and Statistics\n*[[STA 4643]] Introduction to Stochastic Processes //Summer 2014//\n*[[STA 4961]]Actuarial Science Exam Prep //Fall 2014, Spring 2015//\n\nThese are some addition lectures I give when I get the opportunity to substitute for one of my colleagues:\n* [[covariance and correlation|]]\n* [[generating functions|]]
This is a collection of data sets used in all my courses.\n\n! Correlation and Regression Data\n* The [[Anscome data sets|datasets/AnscombeDataSets.htm]] remind us that correlation or a regression line doesn't capture all the behavior of a bivariate relationship\n! Categorical Data\n\n! Survival Data\n\n! Univariate Data
*Stephen Leavitt, ''//Freakonomics//''
*Michael Crichton, ''//State of Fear//''\n*David Liss, ''//A Spectacle of Corruption//''\n*Dorothy Sayers, ''//Gaudy Night//''\n*Neal Stephenson, ''//Cryptonomicon//''\n*Connie Willis, ''//Bellwether//''\n*Jacqueline Winspear, ''//Maisie Dobbs//''
*David Essinger, ''//Jacquard`s Web//''\n*Ian Hacking, ''//The Emergence of Probability//''\n*Thomas Kuhn, ''//The Structure of Scientific Revolutions//''\n*Simon Singh, ''//The Code Book//''
Definitions, ideas, and techniques that every statistician should know something about:\n\n!A\n* [[ABC|]] - approximate Bayesian computation\n!B\n* Black Swans\n* bootstrapping\n!C\n* curbstoning\n!D\n* the delta method\n!F\n* [[The Fourth Quadrant|]]\n!L\n* long tail distributions\n!R\n* ridge regression\n* [[The Rule of Three|]]\n* The Rule of 72 (of course, 72 isn't the correct number)\n!S\n* [[Simpson's Paradox|]] Here's a case where [[a Nobel Prize winner gets caught ignoring it|]].\n* split-plot designs -- if you understand these, you understand DOE\n* St Petersburg Paradox
* Richard Hamming, ''//Methods of Mathematics//''.\n** A compendium of the not-so-basic math statisticians need. Paperbound from Dover, cheap.
My official office hours for Spring 2015 held in the Statistics Lab, BB 3.02.16\n* Mondays, 12:00noon to 1:45pm and 3:00 to 5:00pm\n* Fridays, 12:00 noon to 1:45pm\n* by appointment\n\nMy office is BB 4.04.28, at the east end of the Business Building. Don't go there unless you've made an appointment, since I'm in and out. It's OK to come by and pick up an exam or ask a quick question, but you're taking a chance.\n\nI working on my Ph.D. dissertation. If it's not office hours, don't even //try// to find me.\n\n\n
*Csikszentmihalyi, [[Flow: The Psychology of Optimal Experience]]\n*Harry Frankfurt, ''//On Bullshit//''\n*Hacking,''//The Emergence of Probability//''\n*Postrel, ''//The Substance of Style//'' Pretty //and// smart. //I like that// becomes //I'm like that//. New esthetics for a new century.\n*Reynolds, ''//Playing Ball on Running Water//'' This westernized description of Morita psychotherapy is startling in its simplicity. If you would like to become a certain kind of person, just start doing what that kind of person does. Want to be scholar? Study!\n*Snow, ''//The Two Cultures//'' This lecture started the discussion of the gulf between the sciences and the humanities. The follow-up suggests how the gap might be bridged. A remarkably interesting book, given that the original lecture was given in 1959.
* ''the general approach'', thanks to George Polya's //How to Solve It//\n** //Understand the problem//\n*** //What is the unknown?//\n*** //What is the data?//\n*** //What are the constraints?//\n** //Make a plan//\n** //Carry out the plan//\n** //Look back// At a minimum, check your work. Try simple cases for general solutions to see if known cases work.\n\n* some techniques ''specific to mathematical statistics''\n** isolate a kernel, either a pdf or an expectation (you know what that equals)\n** transform an unfamiliar density into one you know (e.g. F to beta)\n** switch viewpoints for events (e.g. pigeonhole problems)\n** remember that ~CDFs and reliabilities are //probabilities//\n** look for complements, e.g //x+(1-x) = 1//, //a/(a+b) = 1 - b/(a+b)//
* Do you write [[recommendations]]?\n* What is your [[teaching philosophy]]?\n* Do you have tips for [[problem solving]]?\n\nDoctor amarus enim discenti semper ephebo \nnec dulcis ulli disciplina infantiae est.\n\n<<gradient vert #8888ff #ddddff #ffffff>>//For teachers ever are a bitter pill\nTo college youth, nor any serious course\nIs ever sweet to infants.//>>
*[[biography]]\n*[[biostatistics]]\n*[[computer science]]\n*[[fiction]]\n*[[history of science]]\n*[[idioms]]\n*[[mathematics]]\n*[[philosophy]]\n*[[statistics]]
*''How to Get One from Me'': If you would like a letter of recommendation for a scholarship, internship, or job, I'd be happy to write one for you, provided that you\n**have taken one or more of my courses, and \n**are doing well in the course, or \n**have received a grade of B or better \n\n*''What I Need:'' To write a good letter, I need some information from you:\n**a current resume. \n**information about what you are applying for (no cults, political action groups, or unions)\n**a name and address to send the letter to\n
I'm currently working on my Ph.D. in Applied Statistics, concentrating on weighted discrete distributions.\n* [[PhD proposal slides|]] I got a lot of constructive feedback from my committee, so there are big revisions coming.\n* [[Double, Double: Using Duality in Count Regression|]], my presentation for JSM 2011 (previewing some those revisions)\n* [[hyper-Poisson regression|]], an example Mathematica notebook used for Double, Double
!programming examples and tips\n\n\n!!complete programming languages\n*__Mathematica__ is what I use for computation and visualization. Someday, some examples.\n* [[MATLAB|software/the MATLAB page/theMATLABpage.html]] is greatly beloved by electrical engineers and many computational biologists\n* [[R|software/the R page/theRpage.html]] is the favorite of all the Cool Statisticians, bioinformaticians, and many other computational biologists\n*[[SAS|software/the SAS page/theSASpage.html]] is the hands-down best tool for working with very large data sets, and is beloved of nearly all ~UnCool Statisticians. A basic [[SAS Certification|]] is the ticket to a good-paying job if you only hold a Bachelor's degree in statistics.\n\n!! specialized statistical software\n* [[DIYABC|]] Approximate Bayesian Computation for population biologists, on Unix or Windows PCs\n* [[WinBUGS|]] build your own Bayesian models and simulate the posterior and predictive distributions\n\n!!web programming\n* HTML\n**references\n*** [[HTML 4.0 reference|]] from the Web Design Group\n*** [[CSS (cascading style sheets) reference|]] from the Web Design Group\n**tutorials\n*** [[w3schools|]] has tutorials on ~HTML4, CSS, XHTML, and ~HTML5, so// pig out!//\n**tools\n*** [[TopStyle|]] HTML, CSS, and XHTML editor\n**examples\n* ~JavaScript\n** documentation\n** [[JavaScript examples]]\n!!useful tools for presentation and publication\n\n* ~LaTeX\n** software\n*** [[MikTex|]] is a free ~TeX/~LaTeX engine that runs under Windows; comes with an installer and online docs.\n*** [[WinEdt|]] is a cheap ASCII editor with ~TeX/~LaTeX templates and links to your ~TeX/~LaTeX engine. Get it.\n** books\n*** Lamport, [[LaTeX: A Document Preparation System|]], by the guy who wrote--and can explain-- ~LaTeX.\n*** Buerger, [[LaTeX for Engineers and Scientists|]], is a less geeky explanation of using ~LaTeX. Gives a good overview of editing entire documents.\n** [[LaTeX examples]]\n\n*[[TiddlyWiki|]] is what I used to create this web page.\n*[[CMap Tools|]] draws concept maps that can be made into web pages.\n\n!other handy stuff\n*[[FileZilla|]] is a free FTP client: I use it to maintain this web page from home!\n\n!troubleshooting\nA mechanical engineer, an electrical engineer, and a software engineer from Microsoft were driving through the desert when the car broke down. The mechanical engineer said: "It seems to be a problem with the fuel injection system, why don't we pop the hood and I'll take a look at it?" To which the electrical engineer replied, "No, I think it's just a loose ground wire, I'll get out and take a look." Then the software engineer from Microsoft jumps in. "No, no, no. If we just close up all the windows, get out, wait a few minutes, get back in, and then reopen the windows everything will work fine." \n!!some specific ideas\n* check your spelling; compilers and interpreters tolerate NO deviations\n* instrument your code: set up a "debug" option that shows progress or intermediate results so you can see where things go wrong (or right)\n* check the nesting levels of your brackets, quotes, and parentheses; sometimes they get out of order. If this is a recurring problem, try writing your code with ''structured parentheses'', like this {{{model = myfunction( sin(x), ln(y), 6 );}}}
! basic reading \nAll of these books are complete accessible to non-statistician, no heavy math required.\n* Berstein, ''//Against the Gods//''\n** Insurance, hedge funds, derivatives, they're all based on probability; here's the story\n* Fung, ''//Numbers Rule Your World//''\n** [[Kaiser Fung|]] takes a peek "under the hood" of everyday activities and finds statistics everywhere.\n* Peck, Casella, et. al. ''//Statistics: A Guide to the Unknown//'' (4/ed)\n** A new series of essays about solving interesting problems with stastistics, from space junk to radon hazards to counting Siberian tigers.\n* Salsburg, ''//The Lady Tasting Tea//''. \n** A biographical history of statistics in the late, and not-missed, 20th century.\n\n! collections\n* Altman, Bland, Day, Kerry, and Vickers, ''[[BMJ Statistics Notes|readings/BMJarticles.htm#StatisticsNotes]]''\n** This is a series of short articles describing basic statistical ideas and methods, concentrated on experiments and clinical trials. \n* Swinscow, ''[[Statistics At Square One|readings/BMJarticles.htm#StatisticsAtSquareOne]]''\n** Another series of shorts from the BMJ,focussing on hypothesis testing\n* Tanur, Mosteller, et. al. ''[[Statistics: A Guide to the Unknown|]]'' (3/ed.)\n** This is the last edition of the original book of essays, compiled by Judy Tanur when she was still a grad student (studying under Mosteller). Fascinating stories, and it's FREE!\n\n! textbooks and teaching \nEven if you don't adopt these as texts, they're great resources for lectures, labs, and homeworks\n*David Diez, Christopher Barr, and Mine ~Cetinkaya-Rundel, ''[[OpenIntro Statistics|]]''\n** complete textbook, either entire or in chapter-sized ~PDFs, along with a growing course-management system\n*Deborah Nolan and Terry Speed, ''[[Stat Labs|]]'' \n** This is a whole set of "learn by doing" statistical case studies, with lots of theory developed on the fly.\n\n!references \n* Snedecor, ''//Statistical Methods//''. \n** George Snedecor never published an original statistical paper in his entire career. But this handbook has all the standard techniques, with worked examples, and plenty of good tips.\n\n! specializations \n* Alan Agresti, ''//Categorical Data Analysis//''\n* Box, Hunter, and Hunter, ''//Statistics for Experimenters//''\n** Casella and Berger, ''//Statistical Inference//''\n* Draper and Smith, ''//Applied Regression Modeling//''\n*Johnson and Wichern, ''//Applied Multivariate Statistical Analysis//''. \n** All the standard multivariate techniques, with solid mathematical development. Linear algebra required.\n* Grimmett and Stirzaker, ''//Probability and Random Processes//''. \n** This is the poor man's version of Feller. Definitely buy the companion solutions book; some of the problems are very difficult.\n* Hosmer and Lemeshow, ''//Applied Logistic Regression//''. \n** Written for the practioner, light on theory. Just what you need if you're in a hurry to fit a logistic regression model.\n* Ross, ''//Stochastic Processes//''. \n** Bet you can't read the whole thing, and it's a little book!\n* Kenneth Bollen. ''//Structural Equations with Latent Models//''\n* Cover and Thomas, ''//Elements of Information Theory//''. \n** This is THE book on entropy methods for statistics. Lots of theory.\n\n! advanced material \n* Feller, ''//An Introduction to Probability Theory and Its Applications//'', Volumes I and II. \n** Out of print, but such a classic, and so full of good stuff, you should scare up a set. Lots of theory, heavy going at the best of times. \n* Ferguson, ''// Mathematical statistics: a decision theoretic approach//''.\n* Fuller, ''//Introduction to Time Series Analysis//''\n* Graybill, ''//Theory and Methods of Linear Models//'' \n** Undoubtedly one of the driest statistics books ever written, but jammed with useful theorems and proofs. An indispensible reference.\n*Roger Kroenker, ''//Quantile Regression//''. \n** This suddenly fashionable technique has been in the works since the Boscovich-Lapace method, c. 1789. \n** Look out, stats kids -- it uses //linear programming// as its basic algorithm!\n<<gradient vert #8888ff #ddddff #ffffff>>You can download a [[copy of this list|theEssentials.pdf]].>>
I'm continually experimenting with various teaching techniques to improve my courses. Usually I "field test" new ideas in the summer sessions; I seem to get more immediate feedback from my students then.\nMy most recent introductions:\n*''not-at-homework'' Students get frustrated trying to work problems based on new material when they're not sure what questions to ask. Rather than stranding students //outside// class with problems to solve, I frequently give problems for small groups to solve //in// class, so they can ask questions as they work.\n\n*[[concept maps|]] There's a hyperabundance of models, math techniques, definitions, classic examples, and interrelationships in any statistics course. I use a concept map in STA3513 (Probability and Statistics) to help students see the properties and relationships among the 12+ models presented as part of the course.\n\n*[[scratch-off quizzes|]] Standard multiple-choice quizzes tend to be "fire and forget" events which don't provide timely feedback to students. Since Summer 2009, I've been using the Individual Feedback Assessment Technique (IF-AT), better described as scratch-off quizzes, instead. These give truly immediate feedback to students. I use them in a small group setting, so students can discuss their errors and misunderstandings, and work together to plug the holes in their knowledge.\n\n*[[grading rubrics]] I've used these for several years to standardize homework grading. These are especially useful for new TAs, since they standardize what's being graded in each problem, and how many points should be awarded. I also include points for neatness and presentation, to remind students to turn in legible, high-quality work.\n\n*''take office hours out of the office'' My office hours have always been well-attended. However, since I share an office with other Ph.D. candidates, my students make it crowded and noisy, which makes me a lousy office mate. I solved that problem by moving my office hours to the department Statistics Lab, where we have 30 PC workstations and whiteboards. Participation in office hours has soared! Better yet, more students are "getting it." My recommendation: hold office hours some place roomy, where students can work together, work on homework, and pester the instructor repeatedly for 2 or 3 hours. It works.\n\n*''a writing textbook for quantitative courses'' Instructors can't assume that students know how to write about quantitative analysis; this is something outside the experience of most literature-educated writing instructors. I use Jane Miller's excellent text, [[The Chicago Guide to Writing About Numbers|]], and assign homework exercises from her problem sets.
Thanks for asking. Here's some of my thoughts, in my words, and the words of others:\n!!Get 'em While They're Hot!\nGoing to university is the opportunity of a lifetime, so take advantage of it.\n* you are surrounded by smart people\n* there are excellent resources\n* courses are the starting point to become an expert\n!!In theory, there's no difference between theory and practice. In practice, there is. - Jan Schneupscheut\nI bring some very strong prejudices to my courses: I'm a retired military officer, a computer engineer, a former program manager, a statistician, a guy who worked his way through college, an Empiricist, an amateur athlete, a struggling musician, and a Bayesian. All these things color the way I construct my courses and the things I try to teach:\n* Statistics is not probability theory. Statistics is understanding and applying probability theory (and graphic design, and numerical methods, and number theory, and calculus...) to limited sets of data to make inferences. That means that every one of my courses includes applications, even the theory courses. //Of course, you can figure out how that works for the applications courses...//\n* Statistics is not about mathematics, it's about data, and most of the data is numeric. Unlike the pioneers of the early 20th century, we have tools to deal with lots of numbers - computers. So much of statistics is about computation. If you want to do good statistics, you must learn to use the computer effectively. //I'll push you in that direction...//\n* Statistics is one of the original multidisciplinary subjects (geography is Queen). Statisticians tackle problems from anthropology to astronomy, from criminal justice to chemistry. Cool. //I will expect you to have a mulit-dimensional education.//\n* The greatest results in the world are useless unless you can communicate them. Learn to write, do good graphics, and become familiar with basic tools for the Web. //There will be story problems and essay questions.//\n* You are a student, [[not a customer|]]. If you insist on applying a consumer model to my university courses, think of me as your personal trainer. //I'll work you until your brain gets cramps.//\n!!Ninety percent of success is just showing up. - Woody Allen\n* An adequate product delivered on time is more useful than a perfect product delivered too late. Get your assignments in on time.\n* Come to class. Cutting class shows me that either \n** you're overbooked and need to drop my course, or \n** you're not serious about your degree, and need to drop my course. \n* I don't lecture for my health. You have 3 or 4 guaranteed hours to ask me questions and learn how to think like a statistician. Sitting quietly in class all semester and never asking a question tells me that either \n** the course was too easy, and I was wasting your time, or \n** you don't care enough about the material to try to understand the hard bits.\n** A corollary of this is that I will give frequent in-class assignments specifically design to //make you ask questions.// That's how you learn.\n!!Playing Ball on Running Water\nUse your time at university to develop the habits you wish to practice for a lifetime. Practice being the kind of person you want to become. That's [[what I did]]. \n* popularity is out, learning is in\n** read more about your subjects than is in your textbook\n** learn to use basic computing tools\n** solve some simple problems that //aren't// in your syllabus\n** do puzzles\n* look and act the part\n** come to office hours (and class) with your questions ''written down''.\n** have your notes and problem sets organized and accessible\n** turn in homeworks and reports that are neat, legible, and edited\n** stay focussed on the material at hand in classes\n** what's your personal fashion statement?\n* Get a new attitude: \n** participate in the academic community\n** do your part to "advance the ball"\n** join a service organization or professional society\n** provide specific, constructive, actionable feedback to your professors \n\n!!If you're not part of the solution, you're part of the problem. - Huey Newton
!!!//Cornell notes// is a simple note-taking system:\n* [[plain notepaper|materials/CornellNotes/CornellNotesPlain.pdf]]\n* [[quad-ruled notepaper|materials/CornellNotes/CornellNotesGraph.pdf]]\n* [[instructions|materials/CornellNotes/CornellNotesSystem.pdf]]
[>img[up at Taos|TAOS_ski_school.jpg]]\nWhen I was a student, I always admired skiers; I thought folks who were that athletic, that well-traveled, that confident, were the epitome of cool. When my [[weird Uncle|]] sent me off to Colorado Springs for a couple of years, I decided I'd [[learn to ski|]]. For the first few months, it scared the living be-jebus out of me, and I always came home covered in bruises from falling - hard. (I had been an inept athlete as a kid.) But I saw how the other skiers took it, both on and off the slopes, so I stuck with it. I practiced endless turns, walked down hills I couldn't ski, took lessons whenever I could, and tagged along with experts wherever I could find them. I'll never be an expert skier, but I frequently ski speeds and terrain that would have terrified me even a few years ago. More importantly, it gave me poise and confidence in ways I had never imagined - and I don't really give a damn about cool anymore (which is pretty cool itself).