SBI

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64 discussions

by Nathan Tintle

SBI listserv participants, We are happy to announce three new posts for you to enjoy: - "Teaching computation as an argument for simulation-based inference <https://www.causeweb.org/sbi/?p=971>" by Mine Cetinkaya-Rundel in our series on 'Why use simulated based methods?" - "Reflections after two years of simulation-based inference in AP statistics <https://www.causeweb.org/sbi/?p=954>" by Andrew Walter in our series on 'Incorporating simulation-based methods in the HS classroom" - "Using simulation-based methods in New Zealand <https://www.causeweb.org/sbi/?p=976>" by Stephanie Budgett in our new series on "Simulation-based methods around the world" We hope that these posts will challenge and encourage you. These posts and more are available at http://www.causeweb.org/sbi Feel free to continue to suggest other potential topics for the blog, post your questions/comments directly to the blog and/or send your thoughts using this listserv. If you aren't aware of it already, encourage your students with excellent class projects to consider submitting them to the USPROC competition which has prizes for all levels of student projects (intro stat and beyond; see www.causeweb.org/usproc for details) Nathan -- 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

8 years, 4 months

Placement criteria

by Mark Mills

Statistics education colleagues, We currently have two introductory stats courses here at Central College. * Applied Statistics (MATH 215) is intended for more mathematically/technically mature/advanced students. We use Rossman and Chance's ISCAM book, and students learn how to use Minitab to do statistical tests and intervals. * Intro to Statistics (MATH 105) is intended for students who are not as sophisticated mathematically/technically. We use Tintle's Introduction to Statistical Investiations book, and students use the accompanying applets to do much of the work for them. We are struggling with finding an effective prerequisite to use to put students in these two courses. Does anyone have ideas? The biggest difference between the two courses is how fast and how deep we can go with the material. In Applied Stats, we go faster and make some deeper connections with the material. We also expect that students can fill in some of the gaps as we go along. But in Intro to Stats, we go slower and spend more time filling in gaps and making connections for the students. We are struggling with finding an effective prerequisite to use to put students in these two courses. In particular, we'd like to have an enforceable prerequisite for each course that would keep good students from just taking the easy road with Intro to Stats. Does anyone have ideas? Currently, we simply use math placement results to decide this. Students placing at or above Calc I or having completed (at least) Precalculus are not eligible to enroll in MATH 105. However, as strange as this seems, our current registration system cannot check/enforce this prereq, so any student can actually enroll in either course. So we end up having to police these criteria ourselves--usually removing students from Intro to Stats and encouraging them to enroll in Applied Stats. (Not a happy job...) Lately, we have begun to wonder if using a math placement result that is based on a scale from College Algebra to Multivariable Calculus is really the best way to measure what will make a student successful in a particular stats class. We have done some analysis of a number of different possible predictors of student success, and the one that seemed to be the strongest was cumulative GPA. A GPA of 2.7 seemed to be the low end for students who successfully completed Applied Stats. So we proposed a pre-req of GPA <= 2.7 for Intro to Stats and GPA >= 2.7 for Applied Stats, but our Registrar doesn't like it and has asked us to consider a different pre-req. We've talked a lot about this as a department, and we really don't know where to go. If anyone out there with a similar situation of having two intro courses has an easy, effective, and enforceable way to determine student placement, then I would enjoy hearing from you. Please just reply directly to me and not to the SBI mailing list. Thanks! Mark Dr. MARK A. MILLS Professor of Mathematics | Central College 812 University Street | Campus Box 06 | Pella, Iowa 50219

8 years, 4 months

Re: [SBI] Placement criteria

by Nathan Tintle

Mark- Thanks for your email. Here's what we're doing here at Dordt---but I, too, would love to hear more. 1. We have two intro courses. The AP stat equivalent which uses the ISI book you are using and another 'accelerated' intro stats which does the ISI book in half a semester and then does a half semester of multivariable applied statistics. 2. Our placement criteria for the second course is (a) taken calculus (even though we don't use any), or (b) prior experience with statistics (e.g., AP statistics or some other college or HS course) 3. We do have some students take the 'easier' intro course instead of the longer one, but not that many so it's not an issue. When we get overbooked in the regular intro course these better students are the first to go... :) Again, curious what others are doing as well. Nathan On Fri, Dec 4, 2015 at 2:55 PM, Mark Mills <MillsM(a)central.edu> wrote: > Statistics education colleagues, > > > > We currently have two introductory stats courses here at Central College. > > - Applied Statistics (MATH 215) is intended for more mathematically/ > technically mature/advanced students. We use Rossman and Chance's > ISCAM book, and students learn how to use Minitab to do statistical tests > and intervals. > - Intro to Statistics (MATH 105) is intended for students who are not > as sophisticated mathematically/technically. We use Tintle's *Introduction > to Statistical Investiations* book, and students use the accompanying > applets to do much of the work for them. > > We are struggling with finding an effective prerequisite to use to put > students in these two courses. Does anyone have ideas? > > > > The biggest difference between the two courses is how fast and how deep we > can go with the material. In Applied Stats, we go faster and make some > deeper connections with the material. We also expect that students can > fill in some of the gaps as we go along. But in Intro to Stats, we go > slower and spend more time filling in gaps and making connections for the > students. > > > > We are struggling with finding an effective prerequisite to use to put > students in these two courses. In particular, we'd like to have an > enforceable prerequisite for each course that would keep good students from > just taking the easy road with Intro to Stats. Does anyone have ideas? > > > > Currently, we simply use math placement results to decide this. Students > placing at or above Calc I or having completed (at least) Precalculus are > not eligible to enroll in MATH 105. However, as strange as this seems, our > current registration system cannot check/enforce this prereq, so any > student can actually enroll in either course. So we end up having to > police these criteria ourselves--usually removing students from Intro to > Stats and encouraging them to enroll in Applied Stats. (Not a happy job...) > > > > Lately, we have begun to wonder if using a math placement result that is > based on a scale from College Algebra to Multivariable Calculus is really > the best way to measure what will make a student successful in a particular > stats class. > > > > We have done some analysis of a number of different possible predictors of > student success, and the one that seemed to be the strongest was cumulative > GPA. A GPA of 2.7 seemed to be the low end for students who successfully > completed Applied Stats. So we proposed a pre-req of GPA <= 2.7 for Intro > to Stats and GPA >= 2.7 for Applied Stats, but our Registrar doesn't like > it and has asked us to consider a different pre-req. > > > > We've talked a lot about this as a department, and we really don't know > where to go. > > > > If anyone out there with a similar situation of having two intro courses > has an easy, effective, and enforceable way to determine student placement, > then I would enjoy hearing from you. Please just reply directly to me and > not to the SBI mailing list. > > > > Thanks! > > > > Mark > > > > *Dr. MARK A.** MILLS* > Professor of Mathematics | Central College > 812 University Street | Campus Box 06 | Pella, Iowa 50219 > -- 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

8 years, 4 months

Invitation to participate in assessment project

by Nathan Tintle

Instructors, As you may know, we recently received NSF funding (DUE-1323210) to facilitate assessment of (algebra-based) introductory statistics courses, with a focus on gaining a better understanding of potential differences in student learning between “traditional” and simulation/randomization-based introductory statistics courses. As such, we are asking you to consider having your students participate in the assessment project *regardless of how much (if any) simulation- and randomization-based inference methods you use in your course*. As a thank you for your participation, we are happy to offer a $100 stipend and a customized report on your students’ performance in your class. If you are interested in participating, please fill out this short survey, as soon as possible, but early enough to allow time to set up individualized links for your class before your term starts: https://www.surveymonkey.com/s/9SYS8H3. Instructors are signing up now for classes which begin after January 1. Some brief details follow, with answers to some commonly asked questions here <http://homepages.dordt.edu/ntintle/faqs.pdf>. 1. Students in your introductory statistics course (undergraduate or high school level) will take a pre-test (preferably before the course starts, but no later than the first week of classes). The “test” contains multiple choice questions that assess conceptual understanding and student attitudes toward statistics. Most students take approximately 45 minutes to complete the test. The test is administered completely online (we will provide you the link). Students can complete the test either inside or outside of class. See the FAQ for more information on encouraging student participation and IRB including opt-out options. After the closing date you specify, we will send you the student names and individual performance data. At the end of your course, students will take a single multiple choice, online post-test about attitudes and concepts (or, for the post-test only, separated concepts and attitudes tests). Again, we’ll provide you the individualized link. 2. Assuming your sections attain at least 75% participation rates, and you fill out a brief (<30 minute) survey about your course (e.g., size, pedagogy, classroom technology, etc.) at the conclusion of the course, you will receive the stipend and customized report. We anticipate publishing a series of articles on the data gathered as part of this project. Neither student-nor instructor-level information will be reported individually (only in aggregate) in these articles. If you are interested in participating but have questions about your institution’s IRB, please contact us as well. Again, if you are interested in participating, please fill out this short survey, as soon as possible, but early enough to allow time to set up links for your class before your semester starts: https://www.surveymonkey.com/s/9SYS8H3 . After we receive your survey responses, you will be contacted directly by us with more information/details. Please direct additional questions to either Cindy Nederhoff (assessment administrator: cindy.nederhoff(a)dordt.edu) or Nathan Tintle (project director: nathan.tintle(a)dordt.edu). Thanks for considering this, Nathan Tintle (on behalf of the PIs: Nathan Tintle, Beth Chance, Dennis Pearl, Soma Roy and Todd Swanson) -- 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

8 years, 5 months

archived listing of webinars and e-workshops now available

by Nathan Tintle

SBI users, We have archived SBI related webinar and e-conference presentations on the blog here: https://www.causeweb.org/sbi/?p=947 This is a great way to hear more about the use of SBI. Have a great weekend! Nathan -- 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

8 years, 6 months

1e now available/upcoming workshop at JMM

by Nathan Tintle

SBI colleagues, I wanted to draw your attention to three things. 1. On behalf of my co-authors and Wiley, we are happy to announce that the 1e of "Introductions to Statistical Investigations" is now available. See the email below for an official announcement from Wiley. 2. We still have room in our January workshop (Robin Lock, Todd Swanson and Nathan Tintle presenting, Tuesday prior to JMM in Seattle). Please share this workshop with your colleagues who may be 'on the fence' about SBI or sign-up yourself. While there is no official deadline, sign-ups for this free workshop are preferred by Nov 1 if possible. https://www.causeweb.org/workshop/jmm16_investigation/ 3. If you have any ideas for the SBI blog (www.causeweb.org/sbi) please email me so that we can consider your idea for future writing pieces. If you haven't visited in a while, go take a look at some of the great posts on a variety of topics including why people made the switch, using technology, the hardest thing about getting started and more. Thanks, Nathan ------------------------------ 1e announcement email On behalf of Wiley and the entire author team, we are pleased to announce that *Introduction to Statistical Investigations* 1st Edition (ISI) by Tintle, Chance, Cobb, Rossman, Roy, Swanson, and VanderStoep, is now available and accompanied by WileyPLUS Learning Space. WilyePLUS Learning Space is a brand-new technology that will enhance your students’ learning experience. Concept Check questions will be included with each learning objective and our 200 author-created videos, as well as our 1,130-plus exercises, will now be assignable. With WileyPLUS Learning Space, your students will be able to collaborate directly with group projects. So whether you teach in a face-to-face, hybrid, or online model, you can still assign the Investigations and Explorations as group projects. For spring classes, you will have the ability to adopt ISI, with WileyPLUS Learning Space. If you would like AN INSTRUCTORS REVIEW COPY OR to find out more about WileyPLUS Learning Space, please click here to contact your representative. <http://professor.wiley.com/CGI-BIN/LANSAWEB?PROCFUN+PROF1+PRFFN15> Take a look inside. <https://www.dropbox.com/s/q70p9kjefofeck3/Tintle%201e%20pgs%2022-23.pdf?dl=0> Best regards, John LaVacca III *John LaVacca III* Marketing Manager, Mathematics & Statistics Learning Solutions -- 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

8 years, 6 months

Announcing R/Shiny web app for Simulation Based Inference in Intro stats.

by Jim Robison-Cox

Announcing SpIntro-Stats Web Applets /Jim Robison-Cox and Allison Theobold, Montana State University/ /October 13, 2015/ Software applications are an essential component of any simulation based inference (SBI) statistics course. At least two suites of web applets for introductory statistics currently exist (http://lock5stat.com/Statkey) and (http://rossmanchance.com/applets) and each allows public access. With the increasing interest in the simulation approach, we think it time for an open source software project focusing on SBI for introductory statistics which uses the interactive capabilities of web applications. We have begun a framework for such apps based on the Shiny web interface to R. We offer these to the SBI community for comment, feature requests, and collaboration. The repository for the code is https://github.com/MTstateIntroStats/SpIntro-Stats.git Running Spintro-Stats There are several ways to run the web apps: 1. To check features without installing you can view it in this web page: http://shiny.math.montana.edu/jimrc/IntroStatShinyApps/ Please do not send students to this site, as we can’t handle hordes of users. 2. To test on an individual computer running R (free and open source software): * Install packages: ggplot2, gridExtra, shiny, shinythemes and their dependencies. * Enter these commands into the R console: |library(shiny) runGitHub("MTstateIntroStats/SpIntro-Stats") ## Be patient. A browser window will appear with the app inside. ## Use control-C in the R console to exit. | 3. If you want to use these apps in a classroom setting, you’ll need some IT expertise. Specifically, a “Shiny server” which is free for academic purposes, from https://www.rstudio.com/products/shiny/ Be sure git is installed, and clone the code as in: |git clone https://github.com/MTstateIntroStats/SpIntro-Stats.git | Then you will have your own site to serve your own students. 4. To get your own copy of the code, you need “git” installed and a github account. Clone the repository https://github.com/MTstateIntroStats/SpIntro-Stats.git. See a git tutorial for more instructions: https://help.github.com/ Basic layout To run the apps, the user has to choose the /Type/ of data to analyze: * One Categorical, * One Quantitative, * Two Categorical, * Two Quantitative, or * One of Each – One Categorical & One Quantitative. The first option under each variable type is [Enter/Describe Data] which must be run before one can proceed to the next choices under each Variable Type: [Test] or [Estimate]. * Testing is performed by sampling from the null model (One Categorical) or via permutation test (Two Categorical or One-of-Each) or by bootstrap (Two Quantitative). The “One Quantitative” test of a single mean slides the data over to have the hypothesized mean, then resamples from the shifted distribution. * Confidence intervals are all based on bootstrap resamples. Plots show percentile intervals, but summary statistics are displayed for those who want to build /Estimate ± Multiplier * SE/ intervals (We do both). The apps also include * a demo of confidence interval coverage (under “One Categ”) * an animated demo of the bootstrap process (under “One Quant”), * demos of the effect of randomization on a “lurking” variable (under “One Categ” and “One Quant”) * “spinner” and “mixer” (balls drawn from a box) apps for generating random categorical variates (under “One Categ”), and * z and t probability and quantile “look up” pages. Contributing To correct errors or suggest features please use the google group: https://groups.google.com/forum/#!forum/spintro-stats Suggestions are welcome, but given that this is a volunteer project, it might take some time to implement feature requests. If you would like to help with the project, the level of R programming needed is moderate. However, some knowledge of shiny, of html, and possibly of JavaScript is necessary. Related Materials We have also developed activities which utilize the web apps for a one semester statistics intro course. See our other github repository: https://github.com/MTstateIntroStats/IntroStatActivities and https://groups.google.com/forum/#!forum/mt-state-intro-stat-activities Contact: Jim Robison-Cox: jimrc(a)math.montana.edu

8 years, 6 months

free one-day workshop in Seattle on Tues Jan 5

by Allan Rossman

Hello Simulation-Based Inference (SBI) folks, Nathan Tintle, Robin Lock, and Todd Swanson will be leading a (free) one-day workshop in Seattle on Tuesday, January 5, 2016, immediately prior to the Joint Mathematics Meetings. We would greatly appreciate your helping to spread the word about this workshop to your colleagues who might be interested in learning about teaching introductory courses with an SBI approach. Please point them to the following link for more information and a registration form: https://www.causeweb.org/workshop/jmm16_investigation/ Thanks, and have a good weekend, Allan -- Allan J. Rossman Professor and Chair Statistics Department Cal Poly San Luis Obispo, CA 93407 arossman(a)calpoly.edu http://statweb.calpoly.edu/arossman/ "There's no limit to what you can accomplish in life if you don't care who gets the credit."

8 years, 6 months

upcoming webinar; soliciting questions

by Nathan Tintle

Folks interested in simulation-based inference, 1. We are offering a free webinar on Tuesday involving professors who recently made the switch to simulation-based inference and their reflections on making the switch. Sign-up here: https://www.causeweb.org/webinar/teaching/2015-09/ 2. Remember that there are many excellent articles related to teaching with simulation-based inference on the blog here: www.causeweb.org/sbi 3. We are in the process of developing a series of additional articles for the blog. If you have suggestions on what you'd like to see on the blog (questions answered, more on a particular topic), please email me those ideas directly. Thanks! -- 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

8 years, 7 months

CAUSE offering two workshops prior to JMM in Seattle (Jan 4-5, 2016)

by Allan Rossman

Hi folks, CAUSE is offering two free workshops prior to the Joint Mathematics Meetings in Seattle in January, one of which addresses the topic of simulation-based inference: Monday, January 4, 2016: Bringing Passion to Your Introductory Statistics Classroom: A Supportive, Multi-Disciplinary, Project-Based Approach, presented by Lisa Dierker and Dennis Pearl Tuesday, January 5, 2016: Teaching the Statistical Investigation Process with Randomization-Based Inference, presented by Nathan Tintle, Todd Swanson, and Robin Lock More information and registration forms are available at: https://www.causeweb.org/workshop/ Best wishes, 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/

8 years, 7 months

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