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Re: [SBI] Groundhog Day, and a question about simulation methods

by Homer White

Hello Everyone, It has been a joy to read about of the great data sets and activities! I did not have time to respond yesterday, but here goes now. I've got an introductory class with 26 students, and we meet all the time in a computer classroom. Inspired by the Mosaic Project folks, we teach with R in the R Studio Server environment. Our approach to simulation-based inference is about 60% if of the way from traditional inference towards Locke5/Intro to Statistical Investigations. We do some simulation-based inference very early in the course -- binomial stuff, on the first day of class, in fact -- and we try again to work it in early with the chi-square test for the relationship between two factor variables. We're in that unit now, so students have played a couple of times with a "slow-simulation" app to get an idea of the null distribution of the chi-square statistic; subsequently they have been exposed to the standard chi-square test where the null distribution is approximated by a chi-square density curve. They are told that it was quite a godsend for Mr. Pearson to have stumbled on such a family of approximating curves, because Mr. Pearson had no access whatsoever to computing machinery. Groundhog Day began with a little come-to-Jesus chat about the first data analysis report, which the students tackled over the weekend. The project involved looking at some data from a Current Population Survey and to investigate the relationship between hourly wages and such factors as sex, union membership status, race, etc. Apparently there is a rule at my College that during Greek Rush Week critical thinking is forbidden, including the act of determining the type of variables involved in your research question prior to choosing your analytical tools. Accordingly about a third of the students had attempted to make bar charts and cross-tables to investigate, for example, whether men or women earn more, even though wage is a numerical variable. So we cleared that up, I hope. Harrumph. On with the intended show. Today's plan is to revisit simulation one more time, in a situation where you really need it (rather small number of observations). I bring up the "ledge-jump" data (#59 in the classic Handbook of Small Data Sets). a social psychologist studied 21 incidents in Britain involving a person threatening to leap from the ledge of a building or other high structure. The idea was to see what factors might affect the behavior of the crowd that gathers in the street below. To forestall morbid thoughts among students I get up on a nearby table and assure them through role-play that nobody really gets hurt: the fire truck comes right away and the firemen teeter back and forth with their big yellow trampoline, your rabbi, psychiatrist and spouse are phoned and within minutes they are leaning out of one nearby windows, soothing words are spoken, sage advice is given, hope is restored. Eventually they talk you back inside. But in the meantime a crowd has gathered. Sometimes they wait more or less in silence, appropriately mindful of the seriousness of the situation playing out above. Sometimes, though, they begin to bait the would-be jumper (muster up Cockney accent): "Go on, jump will ya?" In the 21 incidents under study, the following was found: weather/crowd Baiting Polite cool 2 7 warm 8 4 Discussion, with students ( me still on table): Me: Somebody could say that, for one reason or another, a crowd is more likely to bait in warm weather. Others might say that the outcome of these 21 incidents had nothing to do with weather, but is just the result of random variation in other factors -- above and beyond the weather at each particular incident. Can you think of any other things besides the weather that might affect crowd behavior? Student: How many people show up to watch. The more that show up, the more likely there jerk who will yell "jump' and get the rest of 'em started. Student: How long they have to wait. Maybe if they stand around a long time they'll get impatient. Student (looking right at me, still on table): What about the dorkiness of the would-be jumper? Me: Uh, maybe. Gee, thanks, Zach. I get down and we work it out on the blackboard: if weather has nothing to do with crowd behavior, then our best guess based on the data is that in each incident, regardless of weather, there is a 10/21 chance for the crowd to bait. The chi-square test function that the class uses has built-in provisions for simulation, of three possible types: * "fixed": the rows sums in the simulated table are constrained to be the rows sums of the observed table, and you determined the probability of each outcome in the columns by pooling the data, as we just did to get the 10/21 figure. * "double-fixed": both the row and column sums of the simulated table are constrained to be equal to the row and column sums of the observed table. * "random": neither row nor column sums are constrained, and the probability of a simulated observation landing in a particular cell is the observed cell count divided by the grand total of the table. "random" make sense when the observed data are a random ample a larger population, and chance comes into play just in the matter of who gets into the sample. For example, if you randomly sample people and ask their sex and where they prefer to sit a in classroom (front, middle or back), then chance is not in how a fixed person will respond but in whether or not that person gets into the sample. "double-fixed" (corresponding to the way simulation is done in R's chisq.test(), and probably in many other software systems as well), appears to be ideally suited for randomized experiment in which the Null hypothesis imagines that a subject's response is the same regardless of which treatment group one is placed into. In that case a different random assignment of subjects to treatment groups might result in a different table, but the row and column sums would be the same as for the table we observe in the actual experiment. "fixed" seems to be the right thing for the ledge-jump situation, if we assume that the 21 incidents weren't sampled randomly out of some larger population, that they were the only 21 incidents that occurred under the period of study in the region under study. In that case the weather at the time of each incident simply was it was, and chance comes into the production of the observed table through random variation in all other factors (conditional upon the weather). So we do simulation with the "fixed" option. Everybody's P-value comes in around 5%, so we decide that don't have overwhelming evidence that weather and crowd behavior are related. Now I come round to my questions. When we teach inference though simulation, we don't want it to become another "black box" for students. We want them to see that the simulation method generates simulated data that reasonably could occur if the study were to be conducted in a hypothetical world where the Null hypothesis is definitely true. Hence we the simulation method has to model, quite transparently, the role that we think chance played -- if the Null is true -- in giving us the data we actually see. But there appears to be controversy among statisticians as to which simulation method is best to use for contingency tables. (See e.g., Agresti, Categorical Data Analysis Third Edition section 3.5.6). I suppose that sometimes it's possible for a particular simulation method not to model the role of chance very well, but to possess superior statistical properties nonetheless, maybe even be the state-of-the art method. (This situation seems to occur also in bootstrap hypothesis testing, where the more preferred re-sampling method is rather more difficult to justify intuitively than is the "naive" re-sampling method.) Where does that leave us in teaching? Do we stick with simulation methods that model the role of chance intuitively? In that case we may get rather far into the weeds (after all, three options for simulation in the two-factor is alot for students to handle so early in the course), and we also end up, from time to time, using methods that won't be recommended in data analysis applications down the road. On the other hand if we employ simulation methods without regard to how intuitively they model the presumed role of chance variation in the production of the data, then we are back to using statistical procedures as black boxes that don't convey insight to students at the introductory level. My dilemma may be due in part to a lack of formal statistical training. Has anyone else found themselves puzzled by similar questions? Homer S. White Professor of Mathematics Georgetown College, KY 40324 502-863-8307 Notice: This message may contain confidential information and is intended for the person/entity to whom it was originally addressed. Any use by others is strictly prohibited. If you received this email in error, please permanently delete it and disregard.

9 years, 2 months

Happy Groundhog Day! What happened in your introductory statistics class today?

by Allan Rossman

Happy Groundhog Day! I continue to find it inexplicable that neither private colleges nor public universities see fit to cancel classes out of respect for this august occasion. But this year I've decided to try to make the best of this lamentable oversight, and I need your help! I think it might be fun to ask introductory statistics teachers to compare notes on what's happening in their classes on one particular day. What better day than Groundhog Day for revisiting the same question over and over, and over and over, and over and over, from multiple perspectives? I'm writing this after Groundhog Day has officially begun in Punxsutawney, Pennsylvania, but it's shortly after 9pm on Super Bowl Sunday here in California. So, to get the ball rolling on this whimsical idea (I strongly prefer the word "whimsical" to "silly" in this context), I'll use future tense to anticipate what will happen in my class on Monday. I plan to be sound asleep when Punxsutawney Phil makes his celebrated prognostication. (Too much information: Thirty years ago I did indeed make the trek to Gobbler's Knob with my future bride before sunrise on February 2, but I won't be up so early or anywhere near Punxsutawney this year!) My introductory students and I in STAT 217-09 at Cal Poly will begin the fifth week of our ten week term on February 2 by finishing up a discussion of principles of well-designed experiments.We’ll discuss a study conducted at Harvard about whether students spend $50 differently depending on whether they’re told that it’s a “tuition rebate” or “bonus income.”Then we’ll consider one of the first studies of the drug AZT for reducing mother-to-child transmission of HIV.We’ll culminate this discussion by collecting some in-class data on a very simple randomized experiment investigating whether grouping of letters can affect memory. All students will receive the same 30 letters in the same order, but some will find convenient, recognizable three-letter groupings and others will see more irregular groupings of letters. Then I expect to have time to introduce a study about whether swimming with dolphins is beneficial to patients who suffer from clinical depression. We'll discuss the design of the study and do a quick exploration of the 2x2 table of results, setting the stage for simulating a randomization test to assess whether the difference between success proportions in two treatment groups is statistically significant. Carrying out this simulation in class, using cards and then an applet, will have to wait until February 3 when the excitement of the momentous day has passed. (Or who knows, perhaps my students and I will find when we awake on Tuesday that we are destined to magically relive Monday again and again...) Please indulge me in this fanciful exercise by replying to this Simulation-Based Inference listserv with a description of what happened, or will happen, in your introductory statistics class on Groundhog Day 2015. Maybe we statistics teachers will learn something interesting by exchanging this information and reflecting on the variety of responses.Even if not, we can honor the grand tradition of Groundhog Day by engaging in a substantially less grand but only marginally more silly (oops, I mean whimsical) one. With best wishes for the special day and for an early spring (to those of you who must endure winter), Allan Rossman -- Allan J. Rossman Professor and Chair Statistics Department Cal Poly San Luis Obispo, CA 93407 arossman(a)calpoly.edu http://statweb.calpoly.edu/arossman/

9 years, 2 months

Groundhog Day

by Sean Bradley

It's not exactly what you asked for, Allan, but still in the Punxsutawney spirit -- from the Washington Post: http://www.washingtonpost.com/blogs/wonkblog/wp/2015/01/31/40-years-of-grou… Cheers, Sean Bradley Clarke University Dubuque Iowa

9 years, 2 months

Re: [SBI] Happy Groundhog Day! What happened in your introductory statistics class today?

by Nathan Tintle

SBI members, I'm teaching an 8-week 'accelerated' intro stat course this semester for students who've had calculus or AP statistics and am in week 4, which means I'm about halfway. The course is entirely online this semester so we don't have class 'today' per se, but are working this week on comparing two group proportions---seeing, for the first time, a permutation test as a way to simulate the null hypothesis (we've done one-sample inference since week one). Like Allan, I'll be doing the swimming with dolphins activity among others. Other highlights this week include (1) A two-proportion Z test (as a convenient mathematical approximation to the permutation test---with some extra data conditions needed), (2) Introducing R (we've been using web applets all semester so far but students will be introduced to R use on their projects if they wish) and (3) Reading and writing on the difference between mathematics (primarily deductive reasoning) and statistics (primarily inductive reasoning), and how this relates to what we can and can't learn from statistics, etc. etc (we can do this already because we've been doing statistical inference for 4 weeks already!) Great to hear from others of you about the innovative/interesting things you're doing in the class! On Sun, Feb 1, 2015 at 11:34 PM, Allan Rossman <arossman(a)calpoly.edu> wrote: > Happy Groundhog Day! > > I continue to find it inexplicable that neither private colleges nor > public universities see fit to cancel classes out of respect for this > august occasion. But this year I've decided to try to make the best of > this lamentable oversight, and I need your help! > > I think it might be fun to ask introductory statistics teachers to compare > notes on what's happening in their classes on one particular day. What > better day than Groundhog Day for revisiting the same question over and > over, and over and over, and over and over, from multiple perspectives? > > I'm writing this after Groundhog Day has officially begun in Punxsutawney, > Pennsylvania, but it's shortly after 9pm on Super Bowl Sunday here in > California. So, to get the ball rolling on this whimsical idea (I strongly > prefer the word "whimsical" to "silly" in this context), I'll use future > tense to anticipate what will happen in my class on Monday. I plan to be > sound asleep when Punxsutawney Phil makes his celebrated prognostication. > (Too much information: Thirty years ago I did indeed make the trek to > Gobbler's Knob with my future bride before sunrise on February 2, but I > won't be up so early or anywhere near Punxsutawney this year!) > > My introductory students and I in STAT 217-09 at Cal Poly will begin the > fifth week of our ten week term on February 2 by finishing up a discussion > of principles of well-designed experiments. We’ll discuss a study > conducted at Harvard about whether students spend $50 differently depending > on whether they’re told that it’s a “tuition rebate” or “bonus income.” Then > we’ll consider one of the first studies of the drug AZT for reducing > mother-to-child transmission of HIV. We’ll culminate this discussion by > collecting some in-class data on a very simple randomized experiment > investigating whether grouping of letters can affect memory. All students > will receive the same 30 letters in the same order, but some will find > convenient, recognizable three-letter groupings and others will see more > irregular groupings of letters. > > > > Then I expect to have time to introduce a study about whether swimming > with dolphins is beneficial to patients who suffer from clinical > depression. We'll discuss the design of the study and do a quick > exploration of the 2x2 table of results, setting the stage for simulating a > randomization test to assess whether the difference between success > proportions in two treatment groups is statistically significant. Carrying > out this simulation in class, using cards and then an applet, will have to > wait until February 3 when the excitement of the momentous day has passed. > (Or who knows, perhaps my students and I will find when we awake on Tuesday > that we are destined to magically relive Monday again and again...) > > Please indulge me in this fanciful exercise by replying to this > Simulation-Based Inference listserv with a description of what happened, or > will happen, in your introductory statistics class on Groundhog Day 2015. > Maybe we statistics teachers will learn something interesting by exchanging > this information and reflecting on the variety of responses. Even if > not, we can honor the grand tradition of Groundhog Day by engaging in a > substantially less grand but only marginally more silly (oops, I mean > whimsical) one. > > With best wishes for the special day and for an early spring (to those of > you who must endure winter), > > Allan Rossman > > > -- > Allan J. Rossman > Professor and Chair > Statistics Department > Cal Poly > San Luis Obispo, CA 93407arossman@calpoly.eduhttp://statweb.calpoly.edu/arossman/ > > -- Nathan Tintle, Ph.D. Associate Professor of Statistics and Dept. Chair Director for Research and Scholarship Dordt College Sioux Center, IA 51250 nathan.tintle(a)dordt.edu Phone: (712) 722-6264 Office: SB1612

9 years, 2 months

Re: [SBI] SBI Digest, Vol 3, Issue 1

by Todd Swanson

“Okay, campers, rise and shine, and don't forget your booties 'cause it's cooooold out there today.” Similar to Bill Murray’s Groundhog Day, we had a winter storm blow through Michigan the day before Groundhog Day. It was bad enough for all the schools in the area to close, except, of course, Hope College. So we met and discussed the benefits of random assignment in well run experiments. At the beginning of class, I had all the students flip a coin. I then passed out a sheet of paper and they wrote down whether they got heads or tails, their height, and their sex. I lectured for a short bit about the benefits of random assignment, mostly in the context of the Physician’s Health Study and briefly talked about blocking. Then I put the data collected in class into an applet so we could compare the distribution of heights and the distribution of sex between the heads and the tails group. It worked very well since each pair of distributions were quite similar. We then opened up an applet that simulated the same thing we just did, but could look at a distribution of the outcomes of many such random assignments. Finally we worked on a short exploration discussing how students that write in cursive on the SAT essay scored significantly higher than those that print. The exploration also discussed an experiment where identical essays, one printed and one written in cursive, were given randomly assigned to graders. The graders tended to grade the one’s written in cursive higher. So far this semester we have focused on a single proportion and looked at tests of significance, confidence intervals, generalizing to a population (random sampling), and now causation with random assignment. We are now ready to start inference involving two variables. Also, like Bill Murray’s Groundhog Day, once I did this with one section of my introductory statistics course, I did in all over again with my second. “They say we’re young and we don’t know …” Todd On Mon, Feb 2, 2015 at 6:55 AM, <sbi-request(a)causeweb.org> wrote: > Send SBI mailing list submissions to > sbi(a)causeweb.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://www.causeweb.org/mailman/listinfo/sbi > or, via email, send a message with subject or body 'help' to > sbi-request(a)causeweb.org > > You can reach the person managing the list at > sbi-owner(a)causeweb.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of SBI digest..." > > > Today's Topics: > > 1. Happy Groundhog Day! What happened in your introductory > statistics class today? (Allan Rossman) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sun, 01 Feb 2015 21:34:10 -0800 > From: Allan Rossman <arossman(a)calpoly.edu> > To: Simulation-Based Inference <sbi(a)causeweb.org> > Subject: [SBI] Happy Groundhog Day! What happened in your introductory > statistics class today? > Message-ID: <54CF0C52.3010905(a)calpoly.edu> > Content-Type: text/plain; charset="utf-8"; Format="flowed" > > Happy Groundhog Day! > > I continue to find it inexplicable that neither private colleges nor > public universities see fit to cancel classes out of respect for this > august occasion. But this year I've decided to try to make the best of > this lamentable oversight, and I need your help! > > I think it might be fun to ask introductory statistics teachers to > compare notes on what's happening in their classes on one particular > day. What better day than Groundhog Day for revisiting the same > question over and over, and over and over, and over and over, from > multiple perspectives? > > I'm writing this after Groundhog Day has officially begun in > Punxsutawney, Pennsylvania, but it's shortly after 9pm on Super Bowl > Sunday here in California. So, to get the ball rolling on this > whimsical idea (I strongly prefer the word "whimsical" to "silly" in > this context), I'll use future tense to anticipate what will happen in > my class on Monday. I plan to be sound asleep when Punxsutawney Phil > makes his celebrated prognostication. (Too much information: Thirty > years ago I did indeed make the trek to Gobbler's Knob with my future > bride before sunrise on February 2, but I won't be up so early or > anywhere near Punxsutawney this year!) > > My introductory students and I in STAT 217-09 at Cal Poly will begin the > fifth week of our ten week term on February 2 by finishing up a > discussion of principles of well-designed experiments.We?ll discuss a > study conducted at Harvard about whether students spend $50 differently > depending on whether they?re told that it?s a ?tuition rebate? or ?bonus > income.?Then we?ll consider one of the first studies of the drug AZT for > reducing mother-to-child transmission of HIV.We?ll culminate this > discussion by collecting some in-class data on a very simple randomized > experiment investigating whether grouping of letters can affect memory. > All students will receive the same 30 letters in the same order, but > some will find convenient, recognizable three-letter groupings and > others will see more irregular groupings of letters. > > Then I expect to have time to introduce a study about whether swimming > with dolphins is beneficial to patients who suffer from clinical > depression. We'll discuss the design of the study and do a quick > exploration of the 2x2 table of results, setting the stage for > simulating a randomization test to assess whether the difference between > success proportions in two treatment groups is statistically > significant. Carrying out this simulation in class, using cards and > then an applet, will have to wait until February 3 when the excitement > of the momentous day has passed. (Or who knows, perhaps my students and > I will find when we awake on Tuesday that we are destined to magically > relive Monday again and again...) > > Please indulge me in this fanciful exercise by replying to this > Simulation-Based Inference listserv with a description of what happened, > or will happen, in your introductory statistics class on Groundhog Day > 2015. Maybe we statistics teachers will learn something interesting by > exchanging this information and reflecting on the variety of > responses.Even if not, we can honor the grand tradition of Groundhog Day > by engaging in a substantially less grand but only marginally more silly > (oops, I mean whimsical) one. > > With best wishes for the special day and for an early spring (to those > of you who must endure winter), > > Allan Rossman > > > -- > Allan J. Rossman > Professor and Chair > Statistics Department > Cal Poly > San Luis Obispo, CA 93407 > arossman(a)calpoly.edu > http://statweb.calpoly.edu/arossman/ > >

9 years, 2 months

Groundhog Day

by Tim Erickson

Okay, I’ll bite! Today with be our fourth class session in this one-semester intro stats course. We meet twice a week for fairly long sessions (now 1:45, but it was only 1:15 when the semester started), and are using the nearly-current (January 2015) version of the ISCAM text (Chance and Rossman). Since we meet Tuesdays and Thursdays, we don’t meet today, but tomorrow I’ll introduce confidence intervals for the proportion, which appears in ISCAM as “Investigation 1.5.” There is time then to practice that or apply it to different setting (the “Reese’s Pieces” activity, estimating p(orange)) or to let that percolate while we introduce another topic, TBD. There are several possibilities if we don’t go for the sugar, chocolate, and peanut butter: * The book has a very nice introduction to power at this point, but I’m not sure I want to go there just yet, and besides, it benefits from Spring Training having started; * I can do some of the probability that’s in the list of topics I was given for this course; I introduced binomial probabilities last time and it couldn’t hurt to devise a task that let us all see if they understood anything; * I could leap ahead to a contrasting topic, and introduce sampling, bias, and numerical data in an inference context. ISCAM’s first dip into that is a little later in the first large chapter, an activity about word lengths in the Declaration of Independence. I also have handouts I made for another context of the opening of the first Harry Potter, almost 1000 words. … Tim Erickson Mathematics and Computer Science Mills College terickson(a)mills.edu

9 years, 2 months

Re: [SBI] Explaining motivation for theory-based models (Ann Cannon)

by Olson Hunt, Megan

Thank you both very much for your responses, Ann and Beth. It's reassuring. I think the transition argument is a good one. "This is where we're going, but this is where we've been and that approach will still be around for quite some time." I too am the only statistics faculty (and in my first year, at that) and it feels like a lot of pressure to make the "right" decision about course content, so it's helpful to hear others' takes on issues. Thanks again, Megan Megan J. Olson Hunt, PhD Assistant Professor, Statistics University of Wisconsin-Green Bay -----Original Message----- From: sbi-bounces(a)causeweb.org [mailto:sbi-bounces@causeweb.org] On Behalf Of sbi-request(a)causeweb.org Sent: Friday, January 02, 2015 1:27 PM To: sbi(a)causeweb.org Subject: SBI Digest, Vol 2, Issue 5 Send SBI mailing list submissions to sbi(a)causeweb.org To subscribe or unsubscribe via the World Wide Web, visit https://www.causeweb.org/mailman/listinfo/sbi or, via email, send a message with subject or body 'help' to sbi-request(a)causeweb.org You can reach the person managing the list at sbi-owner(a)causeweb.org When replying, please edit your Subject line so it is more specific than "Re: Contents of SBI digest..." Today's Topics: 1. Explaining motivation for theory-based models (Olson Hunt, Megan) 2. Re: Explaining motivation for theory-based models (Ann Cannon) 3. Re: Explaining motivation for theory-based models (Beth Chance) ---------------------------------------------------------------------- Message: 1 Date: Fri, 2 Jan 2015 19:13:05 +0000 From: "Olson Hunt, Megan" <olsonhum(a)uwgb.edu> To: "'sbi(a)causeweb.org'" <sbi(a)causeweb.org> Subject: [SBI] Explaining motivation for theory-based models Message-ID: <8CBC5C8F1ACF1C4692AB0C048EA4F07724561D09(a)MAILBXB.uwgb.edu> Content-Type: text/plain; charset="iso-8859-1" I'm working on transitioning our undergrad introductory statistics course to one that involves simulation-based ideas alongside parametric, theory-based models. At the risk of sounding na?ve, I would like your opinion on the following: With the advent of computers that make simulation-based p-value calculations fast and easy, and for those of you that teach this idea in parallel with theory-based p-values, how do you explain the motivation for even using theory-based p-values to your students? In other words, parametric models require assumptions that may not be met. If we can use simulation to obtain a reliable p-value without worrying about these assumptions, then why bother with a t-test (e.g.) for a p-value at all? It seems like a historical argument ("It's been done and probably will continue to be done for quite some time") is one, but... I'm left feeling like I have to tell my students to conduct these theory-based tests "just because." Thanks for your thoughts. Megan

9 years, 3 months

Explaining motivation for theory-based models

by Olson Hunt, Megan

I'm working on transitioning our undergrad introductory statistics course to one that involves simulation-based ideas alongside parametric, theory-based models. At the risk of sounding naïve, I would like your opinion on the following: With the advent of computers that make simulation-based p-value calculations fast and easy, and for those of you that teach this idea in parallel with theory-based p-values, how do you explain the motivation for even using theory-based p-values to your students? In other words, parametric models require assumptions that may not be met. If we can use simulation to obtain a reliable p-value without worrying about these assumptions, then why bother with a t-test (e.g.) for a p-value at all? It seems like a historical argument ("It's been done and probably will continue to be done for quite some time") is one, but... I'm left feeling like I have to tell my students to conduct these theory-based tests "just because." Thanks for your thoughts. Megan

9 years, 4 months

Re: [SBI] New blog posts

by Nathan Tintle

A quick follow-up on Beth's comment re: assessment. 1. We are offering a $100 stipend for participating. 2. We'd love to have others at your institution participate also (especially non-users) so feel free to share the link. 3. The pre-post tests are multiple choice, offered online and are a mix of concepts and attitudes questions. We'll provide you a custom report on your students performance when the semester ends. 4. There is an option to include some additional questions on your exams as well if you'd like. 5. You are welcome to participate again this semester, even if you did in the fall. As Beth says, read more here: http://homepages.dordt.edu/ntintle/assessment_intro.pdf On Fri, Jan 2, 2015 at 12:33 AM, Beth Chance <bchance(a)calpoly.edu> wrote: > Hi everyone and happy new year! > > > > Just wanted to let you know about two new topics with postings on the SBI > blog (https://www.causeweb.org/sbi/) awaiting your comments. > > - Should we teach bootstrapping > > - Incorporating student projects > > > > We are also still looking for individuals willing to give some of our common assessment items (multiple choice pre/post, a few open ended), ESPECIALLY if you are NOT doing much with simulation-based inference J Full details, including a link to sign up, are located here at http://homepages.dordt.edu/ntintle/assessment_intro.pdf. > > > > Thanks, > > Beth > > > > > -- Nathan Tintle, Ph.D. Associate Professor of Statistics and Dept. Chair Director for Research and Scholarship Dordt College Sioux Center, IA 51250 nathan.tintle(a)dordt.edu Phone: (712) 722-6264 Office: SB1612

9 years, 4 months

Simulation methods in an AP course

by Robert Peterson

Does anyone have some insight on how to blend simulation methods concepts with the AP curriculum. Also I will be starting inference soon, so I would love to hear ideas and experiences. My problem is I really love the ISI material, but at the same time my students must know the formulas and conditions from the theory based methods.

9 years, 4 months

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