http://scs.math.yorku.ca/index.php?title=Special:Contributions&feed=atom&limit=20&target=Georges&year=&month=Wiki1 - User contributions [en]2020-01-20T18:30:39ZFrom Wiki1MediaWiki 1.16.1http://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-12-16T05:23:41Z<p>Georges: /* Seminar on Friday, November 8, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
* Updated version of the cross-validation functions from John:<br />
** [http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.html A More General Cross Validation Function for R] ([http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.Rmd Rmd file])<br />
<br />
== Seminar on Friday, November 22, 2019 ==<br />
We will read and discuss Chapter 6 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
* [https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ 15 hours of videos by Tibshirani and Hastie on our textbook]<br />
<br />
== Seminar on Friday, January 10, 2020 ==<br />
We will read and discuss Chapter 7 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2020-2021 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/Ethical_PredictionEthical Prediction2019-11-25T15:12:40Z<p>Georges: Created page with "== Apple Card == (from Tino Ntentes:) The Apple Card prediction algorithm sets female’s credit limit lower than their husband’s even though the females have higher credit lim..."</p>
<hr />
<div>== Apple Card ==<br />
(from Tino Ntentes:) The Apple Card prediction algorithm sets female’s credit limit lower than their husband’s even though the females have higher credit limits than their male spouses (they’ve filed joint tax returns and everything for many years). Multiple couples have complained about this and now Apple and Goldman Sachs are being investigated for discrimination in New York State.<br />
<br />
https://www.google.com/amp/s/business.financialpost.com/technology/apple-card-investigated-after-gender-discrimination-complaints/amp<br />
<br />
== Working on Ethical Prediction ==<br />
Heather Krause runs an organization, [https://weallcount.com/ We All Count: a project for equity in data science], that is very concerned with ethical prediction.</div>Georgeshttp://scs.math.yorku.ca/index.php/Wiki_for_statistical_consultingWiki for statistical consulting2019-11-25T15:07:22Z<p>Georges: /* Statistical Topics */</p>
<hr />
<div>This is the wiki for the [http://www.yorku.ca/isr/scs/ Statistical Consulting Service] at York University. You can make [http://www.appointmentquest.com/provider/2000199121 appointments online] to see one of our consultants.<br />
<br />
Anyone can read the content of this wiki. If you are interested in contributing, please let Georges Monette [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki] know that you would like to have an account.<br />
== Hot topics ==<br />
<!--<br />
* Contribute to the list of [[Programs_in_Data_Sciences|programs in Data Sciences]].<br />
--><br />
* [[Sometimes Asked Questions]]<br />
* It would be a great contribution to compile a list of exemplary subject-matter papers reporting statistical methods, especially modern methods such as longitudinal data analysis, etc. <br />
__TOC__<br />
<br />
== Seminars ==<br />
* [[SCS Reads 2019-2020|SCS Reads 2019-2020 ????????]]<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015|SCS Reads 2014-2015 Frank Harrell's Regression Modeling Strategies]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]<br />
<br />
== Statistical Seminars in the Toronto Area ==<br />
* [http://qm.info.yorku.ca/ York Quantitative Methods Program in Psychology]<br />
* [http://www.math.yorku.ca/Who/Faculty/Rensburg/Colloquium/Colloquium2013.html York Department of Mathematics and Statistic]<br />
<br />
== Workshops and Courses ==<br />
* [http://blackwell.math.yorku.ca/ICPSR2017 ICPSR 2017 Course in Longitudinal Data Analysis with Mixed and Bayesian Models]<br />
* [[SCS_2017:_Longitudinal_and_Nested_Data|SCS 2017 Models and Analysis for Longitudinal and Nested Data]]<br />
* [[SCS 2014: Visualizing Regression]]<br />
* [[Mixed Models with R]]<br />
* [[SPIDA 2012: Mixed Models with R]]<br />
* [[SCS 2012: Mixed Models with R]]<br />
* [[SCS 2011: Statistical Analysis and Programming with R]]<br />
* [[SCS 2012: A Gentle Introduction to R]]<br />
* [[MATH 6627|MATH 6627 Practicum in Statistical Consulting]]<br />
* [[MATH 6643 Summer 2012 Applications of Mixed Models]]<br />
<br />
== Methods ==<br />
<br />
=== Data Analysis ===<br />
*[[Data Cleaning]]<br />
<br />
* [[Survival Analysis]]<br />
<br />
* [[Latent Variable Models]] (e.g. SEM, CFA, IRT, LCA)<br />
<br />
* [[Multilevel/Mixed Models]]<br />
<br />
* [[General Linear Models]] (e.g. multiple regression, ANOVA)<br />
<br />
* [[Categorical Data Analysis]] (e.g.contingency tables, chi-square, logistic regression)<br />
<br />
* [[Aggregate Data]] (e.g. meta-analysis)<br />
=== Displaying Data and Reporting===<br />
* Good papers on graphs:<br />
*:[http://www.ruf.rice.edu/~lane/papers/designing_better_graphs.pdf Lane, D.M., & Sandor, A. (2009). Designing better graphs by including distributional information and integrating words, numbers, and images. ''Psychological Methods, 14,'' 239-257.]<br />
*:[http://euclid.psych.yorku.ca/www/lab/psy6140/papers/kastellec-using-graphs.pdf Kastellec, J. P. and Leoni, E. L. (2007) Using Graphs Instead of Tables in ''Political Science, Perspectives on Politics'', 5, 755--771]. <br />
*:See also the related web site for Kastellec & Leoni, [http://tables2graphs.com/doku.php Using Graphs Instead of Tables].<br />
<br />
* A [http://biostat.mc.vanderbilt.edu/wiki/Main/ManuscriptChecklist checklist for statistical reporting]<br />
<br />
* [http://www.statlit.org/pdf/2001SchieldBusOfComm.pdf Describing Rates and Percentages in Tables]<br />
<br />
=== Statistical Topics ===<br />
*[[Causality]]<br />
*[[Ethical Prediction]]<br />
*[[Model Selection]]<br />
*[[Programs in Data Sciences]]<br />
*[[Professional Accreditation]]<br />
*[[Statistics]]<br />
*[[Statistics in the News]]<br />
<br />
=== Statistical Consulting Support ===<br />
* [http://wiki.math.yorku.ca/index.php/MATH_6627_2007-08 York's Statistical Consulting Practicum Wiki 2007-2008]<br />
<br />
* [http://scs.math.yorku.ca/index.php/MATH_6627_2010-11_Practicum_in_Statistical_Consulting Statistical Consulting Practicum Wiki 2011]<br />
<br />
* [http://www.stat.columbia.edu/~cook/movabletype/archives/2008/01/rindskopfs_rule.html David Rindskopf's Rules for Consultants]<br />
<br />
* [http://www.rci.rutgers.edu/~cabrera/sc/ Javier Cabrera's Statistical Consulting Course]<br />
<br />
* [http://www.statsci.org/smyth/pubs/training.html Gordon Smyth on Training Students to be Consultants]<br />
<br />
* [http://www.amstat.org/sections/cnsl/ The American Statistical Association's Consulting Section] <br />
<br />
* Janice Derr on the Qualities of an Effective Statistical Consultant [[File: Janice_Derr_on_Consulting.pdf]]<br />
<br />
* [http://www.stat.purdue.edu/scs/help/notes_for_consultants.html Purdue University's Guide for Statistical Consultants]<br />
<br />
* [[Links to other Statistical Consulting Services]]<br />
<br />
== Software ==<br />
* [[R]]<br />
* [[SAS]]<br />
* [[SPSS/PASW]]<br />
* [http://www.gnu.org/software/pspp/ PSPP: open-source package inspired by SPSS]<br />
* [http://www.statmodel.com MPlus]<br />
* How to access software remotely from York's WebFAS system: <br />
** [[Media:Webfas_handout_SAS.pdf|SAS version]]<br />
=== Local R packages ===<br />
The 'spida' and 'p3d' packages are now available through github with<br />
* devtools::install_github('gmonette/spida2') and<br />
* devtools::install_github('gmonette/p3d')<br />
respectively.<br />
<br />
== SCS Administration ==<br />
* Information for SCS TAs<br />
** [[SCS TAships: Allocation of time]]<br />
** Your first term as a TA: <br />
**: [http://scs.math.yorku.ca/images/8/8f/Guidelines_for_new_TAs.pdf Using AppointmentQuest to attend consulting sessions (prepared by Gabriela Gonzalez)]<br />
<!--<br />
* [[SCS Staff Meetings, 2011]]<br />
--><br />
<br />
== Interesting Links ==<br />
=== Statistical consulting services at other universities === <br />
*[http://cscar.research.umich.edu/about/ University of Michigan]<br />
* [[Slides]]<br />
* [[Blogs]]<br />
* [[Books]]<br />
*[[Statistics-specific Job Boards]]<br />
<br />
'''Did you know?'''<br />
<br />
http://www12.statcan.gc.ca/census-recensement/index-eng.cfm</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-25T14:58:33Z<p>Georges: </p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
* [http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.html A More General Cross Validation Function for R] ([http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.Rmd Rmd file])<br />
<br />
== Seminar on Friday, November 22, 2019 ==<br />
We will read and discuss Chapter 6 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
* [https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ 15 hours of videos by Tibshirani and Hastie on our textbook]<br />
<br />
== Seminar on Friday, January 10, 2020 ==<br />
We will read and discuss Chapter 7 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2020-2021 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-25T14:55:32Z<p>Georges: /* Seminar on Friday, November 8, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
* [http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.html A More General Cross Validation Function for R] ([http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.Rmd Rmd file])<br />
<br />
== Seminar on Friday, November 22, 2019 ==<br />
We will read and discuss Chapter 6 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
* [https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ 15 hours of videos by Tibshirani and Hastie on our textbook]<br />
<br />
== Seminar on Friday, November 22, 2019 ==<br />
<br />
<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-25T14:52:06Z<p>Georges: /* Seminar on Friday, November 8, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
* [http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.html A More General Cross Validation Function for R] ([http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.Rmd Rmd file])<br />
== Seminar on Friday, November 22, 2019 ==<br />
<br />
<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-01T03:16:55Z<p>Georges: /* Seminar on Friday, November 8, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
* [http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.html A More General Cross Validation Function for R] ([http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.Rmd Rmd file])<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-01T03:14:02Z<p>Georges: /* Seminar on Friday, October 25, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-01T03:10:45Z<p>Georges: /* Seminar on Friday, October 25, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
[http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.html html]<br />
[http://blackwell.math.yorku.ca/wiki_uploads/Cross-Validation.Rmd Rmd]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-01T03:07:13Z<p>Georges: /* Seminar on Friday, October 25, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
[http://blackwell.math.yorku.ca/wiki_uploads/Cross-validation.html html]<br />
[http://blackwell.math.yorku.ca/wiki_uploads/Cross-validation.Rmd Rmd]<br />
<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-11-01T03:05:16Z<p>Georges: /* Seminar on Friday, October 25, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
[[Media:ISLR-eqn4.24.pdf|Deriving Equation 4.24 on p. 151]]<br />
== Seminar on Friday, November 8, 2019 ==<br />
We will read and discuss Chapter 5 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-10-18T22:04:35Z<p>Georges: /* Seminar on Friday, October 4, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
== Seminar on Friday, October 25, 2019 ==<br />
We will read and discuss Chapter 4 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br><br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/Wiki_for_statistical_consultingWiki for statistical consulting2019-10-03T16:08:33Z<p>Georges: /* SCS Administration */</p>
<hr />
<div>This is the wiki for the [http://www.yorku.ca/isr/scs/ Statistical Consulting Service] at York University. You can make [http://www.appointmentquest.com/provider/2000199121 appointments online] to see one of our consultants.<br />
<br />
Anyone can read the content of this wiki. If you are interested in contributing, please let Georges Monette [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki] know that you would like to have an account.<br />
== Hot topics ==<br />
<!--<br />
* Contribute to the list of [[Programs_in_Data_Sciences|programs in Data Sciences]].<br />
--><br />
* [[Sometimes Asked Questions]]<br />
* It would be a great contribution to compile a list of exemplary subject-matter papers reporting statistical methods, especially modern methods such as longitudinal data analysis, etc. <br />
__TOC__<br />
<br />
== Seminars ==<br />
* [[SCS Reads 2019-2020|SCS Reads 2019-2020 ????????]]<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015|SCS Reads 2014-2015 Frank Harrell's Regression Modeling Strategies]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]<br />
<br />
== Statistical Seminars in the Toronto Area ==<br />
* [http://qm.info.yorku.ca/ York Quantitative Methods Program in Psychology]<br />
* [http://www.math.yorku.ca/Who/Faculty/Rensburg/Colloquium/Colloquium2013.html York Department of Mathematics and Statistic]<br />
<br />
== Workshops and Courses ==<br />
* [http://blackwell.math.yorku.ca/ICPSR2017 ICPSR 2017 Course in Longitudinal Data Analysis with Mixed and Bayesian Models]<br />
* [[SCS_2017:_Longitudinal_and_Nested_Data|SCS 2017 Models and Analysis for Longitudinal and Nested Data]]<br />
* [[SCS 2014: Visualizing Regression]]<br />
* [[Mixed Models with R]]<br />
* [[SPIDA 2012: Mixed Models with R]]<br />
* [[SCS 2012: Mixed Models with R]]<br />
* [[SCS 2011: Statistical Analysis and Programming with R]]<br />
* [[SCS 2012: A Gentle Introduction to R]]<br />
* [[MATH 6627|MATH 6627 Practicum in Statistical Consulting]]<br />
* [[MATH 6643 Summer 2012 Applications of Mixed Models]]<br />
<br />
== Methods ==<br />
<br />
=== Data Analysis ===<br />
*[[Data Cleaning]]<br />
<br />
* [[Survival Analysis]]<br />
<br />
* [[Latent Variable Models]] (e.g. SEM, CFA, IRT, LCA)<br />
<br />
* [[Multilevel/Mixed Models]]<br />
<br />
* [[General Linear Models]] (e.g. multiple regression, ANOVA)<br />
<br />
* [[Categorical Data Analysis]] (e.g.contingency tables, chi-square, logistic regression)<br />
<br />
* [[Aggregate Data]] (e.g. meta-analysis)<br />
=== Displaying Data and Reporting===<br />
* Good papers on graphs:<br />
*:[http://www.ruf.rice.edu/~lane/papers/designing_better_graphs.pdf Lane, D.M., & Sandor, A. (2009). Designing better graphs by including distributional information and integrating words, numbers, and images. ''Psychological Methods, 14,'' 239-257.]<br />
*:[http://euclid.psych.yorku.ca/www/lab/psy6140/papers/kastellec-using-graphs.pdf Kastellec, J. P. and Leoni, E. L. (2007) Using Graphs Instead of Tables in ''Political Science, Perspectives on Politics'', 5, 755--771]. <br />
*:See also the related web site for Kastellec & Leoni, [http://tables2graphs.com/doku.php Using Graphs Instead of Tables].<br />
<br />
* A [http://biostat.mc.vanderbilt.edu/wiki/Main/ManuscriptChecklist checklist for statistical reporting]<br />
<br />
* [http://www.statlit.org/pdf/2001SchieldBusOfComm.pdf Describing Rates and Percentages in Tables]<br />
<br />
=== Statistical Topics ===<br />
*[[Causality]]<br />
*[[Model Selection]]<br />
*[[Programs in Data Sciences]]<br />
*[[Professional Accreditation]]<br />
*[[Statistics]]<br />
*[[Statistics in the News]]<br />
<br />
=== Statistical Consulting Support ===<br />
* [http://wiki.math.yorku.ca/index.php/MATH_6627_2007-08 York's Statistical Consulting Practicum Wiki 2007-2008]<br />
<br />
* [http://scs.math.yorku.ca/index.php/MATH_6627_2010-11_Practicum_in_Statistical_Consulting Statistical Consulting Practicum Wiki 2011]<br />
<br />
* [http://www.stat.columbia.edu/~cook/movabletype/archives/2008/01/rindskopfs_rule.html David Rindskopf's Rules for Consultants]<br />
<br />
* [http://www.rci.rutgers.edu/~cabrera/sc/ Javier Cabrera's Statistical Consulting Course]<br />
<br />
* [http://www.statsci.org/smyth/pubs/training.html Gordon Smyth on Training Students to be Consultants]<br />
<br />
* [http://www.amstat.org/sections/cnsl/ The American Statistical Association's Consulting Section] <br />
<br />
* Janice Derr on the Qualities of an Effective Statistical Consultant [[File: Janice_Derr_on_Consulting.pdf]]<br />
<br />
* [http://www.stat.purdue.edu/scs/help/notes_for_consultants.html Purdue University's Guide for Statistical Consultants]<br />
<br />
* [[Links to other Statistical Consulting Services]]<br />
<br />
== Software ==<br />
* [[R]]<br />
* [[SAS]]<br />
* [[SPSS/PASW]]<br />
* [http://www.gnu.org/software/pspp/ PSPP: open-source package inspired by SPSS]<br />
* [http://www.statmodel.com MPlus]<br />
* How to access software remotely from York's WebFAS system: <br />
** [[Media:Webfas_handout_SAS.pdf|SAS version]]<br />
=== Local R packages ===<br />
The 'spida' and 'p3d' packages are now available through github with<br />
* devtools::install_github('gmonette/spida2') and<br />
* devtools::install_github('gmonette/p3d')<br />
respectively.<br />
<br />
== SCS Administration ==<br />
* Information for SCS TAs<br />
** [[SCS TAships: Allocation of time]]<br />
** Your first term as a TA: <br />
**: [http://scs.math.yorku.ca/images/8/8f/Guidelines_for_new_TAs.pdf Using AppointmentQuest to attend consulting sessions (prepared by Gabriela Gonzalez)]<br />
<!--<br />
* [[SCS Staff Meetings, 2011]]<br />
--><br />
<br />
== Interesting Links ==<br />
=== Statistical consulting services at other universities === <br />
*[http://cscar.research.umich.edu/about/ University of Michigan]<br />
* [[Slides]]<br />
* [[Blogs]]<br />
* [[Books]]<br />
*[[Statistics-specific Job Boards]]<br />
<br />
'''Did you know?'''<br />
<br />
http://www12.statcan.gc.ca/census-recensement/index-eng.cfm</div>Georgeshttp://scs.math.yorku.ca/index.php/Wiki_for_statistical_consultingWiki for statistical consulting2019-10-03T16:07:18Z<p>Georges: /* SCS Administration */</p>
<hr />
<div>This is the wiki for the [http://www.yorku.ca/isr/scs/ Statistical Consulting Service] at York University. You can make [http://www.appointmentquest.com/provider/2000199121 appointments online] to see one of our consultants.<br />
<br />
Anyone can read the content of this wiki. If you are interested in contributing, please let Georges Monette [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki] know that you would like to have an account.<br />
== Hot topics ==<br />
<!--<br />
* Contribute to the list of [[Programs_in_Data_Sciences|programs in Data Sciences]].<br />
--><br />
* [[Sometimes Asked Questions]]<br />
* It would be a great contribution to compile a list of exemplary subject-matter papers reporting statistical methods, especially modern methods such as longitudinal data analysis, etc. <br />
__TOC__<br />
<br />
== Seminars ==<br />
* [[SCS Reads 2019-2020|SCS Reads 2019-2020 ????????]]<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015|SCS Reads 2014-2015 Frank Harrell's Regression Modeling Strategies]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]<br />
<br />
== Statistical Seminars in the Toronto Area ==<br />
* [http://qm.info.yorku.ca/ York Quantitative Methods Program in Psychology]<br />
* [http://www.math.yorku.ca/Who/Faculty/Rensburg/Colloquium/Colloquium2013.html York Department of Mathematics and Statistic]<br />
<br />
== Workshops and Courses ==<br />
* [http://blackwell.math.yorku.ca/ICPSR2017 ICPSR 2017 Course in Longitudinal Data Analysis with Mixed and Bayesian Models]<br />
* [[SCS_2017:_Longitudinal_and_Nested_Data|SCS 2017 Models and Analysis for Longitudinal and Nested Data]]<br />
* [[SCS 2014: Visualizing Regression]]<br />
* [[Mixed Models with R]]<br />
* [[SPIDA 2012: Mixed Models with R]]<br />
* [[SCS 2012: Mixed Models with R]]<br />
* [[SCS 2011: Statistical Analysis and Programming with R]]<br />
* [[SCS 2012: A Gentle Introduction to R]]<br />
* [[MATH 6627|MATH 6627 Practicum in Statistical Consulting]]<br />
* [[MATH 6643 Summer 2012 Applications of Mixed Models]]<br />
<br />
== Methods ==<br />
<br />
=== Data Analysis ===<br />
*[[Data Cleaning]]<br />
<br />
* [[Survival Analysis]]<br />
<br />
* [[Latent Variable Models]] (e.g. SEM, CFA, IRT, LCA)<br />
<br />
* [[Multilevel/Mixed Models]]<br />
<br />
* [[General Linear Models]] (e.g. multiple regression, ANOVA)<br />
<br />
* [[Categorical Data Analysis]] (e.g.contingency tables, chi-square, logistic regression)<br />
<br />
* [[Aggregate Data]] (e.g. meta-analysis)<br />
=== Displaying Data and Reporting===<br />
* Good papers on graphs:<br />
*:[http://www.ruf.rice.edu/~lane/papers/designing_better_graphs.pdf Lane, D.M., & Sandor, A. (2009). Designing better graphs by including distributional information and integrating words, numbers, and images. ''Psychological Methods, 14,'' 239-257.]<br />
*:[http://euclid.psych.yorku.ca/www/lab/psy6140/papers/kastellec-using-graphs.pdf Kastellec, J. P. and Leoni, E. L. (2007) Using Graphs Instead of Tables in ''Political Science, Perspectives on Politics'', 5, 755--771]. <br />
*:See also the related web site for Kastellec & Leoni, [http://tables2graphs.com/doku.php Using Graphs Instead of Tables].<br />
<br />
* A [http://biostat.mc.vanderbilt.edu/wiki/Main/ManuscriptChecklist checklist for statistical reporting]<br />
<br />
* [http://www.statlit.org/pdf/2001SchieldBusOfComm.pdf Describing Rates and Percentages in Tables]<br />
<br />
=== Statistical Topics ===<br />
*[[Causality]]<br />
*[[Model Selection]]<br />
*[[Programs in Data Sciences]]<br />
*[[Professional Accreditation]]<br />
*[[Statistics]]<br />
*[[Statistics in the News]]<br />
<br />
=== Statistical Consulting Support ===<br />
* [http://wiki.math.yorku.ca/index.php/MATH_6627_2007-08 York's Statistical Consulting Practicum Wiki 2007-2008]<br />
<br />
* [http://scs.math.yorku.ca/index.php/MATH_6627_2010-11_Practicum_in_Statistical_Consulting Statistical Consulting Practicum Wiki 2011]<br />
<br />
* [http://www.stat.columbia.edu/~cook/movabletype/archives/2008/01/rindskopfs_rule.html David Rindskopf's Rules for Consultants]<br />
<br />
* [http://www.rci.rutgers.edu/~cabrera/sc/ Javier Cabrera's Statistical Consulting Course]<br />
<br />
* [http://www.statsci.org/smyth/pubs/training.html Gordon Smyth on Training Students to be Consultants]<br />
<br />
* [http://www.amstat.org/sections/cnsl/ The American Statistical Association's Consulting Section] <br />
<br />
* Janice Derr on the Qualities of an Effective Statistical Consultant [[File: Janice_Derr_on_Consulting.pdf]]<br />
<br />
* [http://www.stat.purdue.edu/scs/help/notes_for_consultants.html Purdue University's Guide for Statistical Consultants]<br />
<br />
* [[Links to other Statistical Consulting Services]]<br />
<br />
== Software ==<br />
* [[R]]<br />
* [[SAS]]<br />
* [[SPSS/PASW]]<br />
* [http://www.gnu.org/software/pspp/ PSPP: open-source package inspired by SPSS]<br />
* [http://www.statmodel.com MPlus]<br />
* How to access software remotely from York's WebFAS system: <br />
** [[Media:Webfas_handout_SAS.pdf|SAS version]]<br />
=== Local R packages ===<br />
The 'spida' and 'p3d' packages are now available through github with<br />
* devtools::install_github('gmonette/spida2') and<br />
* devtools::install_github('gmonette/p3d')<br />
respectively.<br />
<br />
== SCS Administration ==<br />
* Information for SCS TAs<br />
** [[SCS TAships: Allocation of time]]<br />
** Your first term as a TA: [http://scs.math.yorku.ca/images/8/8f/Guidelines_for_new_TAs.pdf Using AppointmentQuest to attend consulting sessions (prepared by Gabriela Gonzalez)]<br />
<!--<br />
* [[SCS Staff Meetings, 2011]]<br />
--><br />
<br />
== Interesting Links ==<br />
=== Statistical consulting services at other universities === <br />
*[http://cscar.research.umich.edu/about/ University of Michigan]<br />
* [[Slides]]<br />
* [[Blogs]]<br />
* [[Books]]<br />
*[[Statistics-specific Job Boards]]<br />
<br />
'''Did you know?'''<br />
<br />
http://www12.statcan.gc.ca/census-recensement/index-eng.cfm</div>Georgeshttp://scs.math.yorku.ca/index.php/File:Guidelines_for_new_TAs.pdfFile:Guidelines for new TAs.pdf2019-10-03T16:06:10Z<p>Georges: </p>
<hr />
<div></div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-09-26T13:53:54Z<p>Georges: /* Seminar on Friday, September 20, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
== Seminar on Friday, October 4, 2019 ==<br />
We will read and discuss Chapter 3 of [http://faculty.marshall.usc.edu/gareth-james/ISL/ ''An Introduction to Statistical Learning''].<br>Data for the exercises are available by installing the ''ISLR'' package in R.<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-09-13T20:30:31Z<p>Georges: /* Seminar on Friday, September 20, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We will then decide what to do next.<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-09-13T20:29:52Z<p>Georges: /* Seminar on Friday, September 20, 2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' which can be downloaded from [http://faculty.marshall.usc.edu/gareth-james/ISL/ this link]. We plan to then decide what to do next.<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-09-13T20:28:29Z<p>Georges: /* New candidates topics for 2019-2020 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== Seminars 2019-20 ==<br />
<br />
== Seminar on Friday, September 20, 2019 ==<br />
At our meeting on September 13, we agreed to read and discuss the first two chapters of ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/]. We plan to then decide what to do next.<br />
<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
* "Statistical learning": We could read either ''An Introduction to Statistical Learning'' [http://faculty.marshall.usc.edu/gareth-james/ISL/] or ''The Elements of Statistical Learning'' [https://web.stanford.edu/~hastie/ElemStatLearn/]; free .pdfs are available for both.<br />
"The books are based on the concept of “statistical learning,” a mashup of stats and machine learning. The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs. This is important in areas like medicine, where a researcher doesn’t just want to know whether a medicine worked, but also why it worked. Statistical learning is meant to take the best ideas from machine learning and computer science, and explain how they can be used and interpreted through a statistician’s lens." See [https://getpocket.com/explore/item/these-are-the-best-books-for-learning-modern-statistics-and-they-re-all-free]<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-09-02T17:05:39Z<p>Georges: /* Candidates from 2018-2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance. For an overview, see the [https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913 editorial published in the special issue].<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georgeshttp://scs.math.yorku.ca/index.php/SCS_Reads_2019-2020SCS Reads 2019-20202019-09-02T17:02:33Z<p>Georges: /* Candidates from 2018-2019 */</p>
<hr />
<div>* A topic need not occupy the entire academic year and we could plan to consider more than one topic.<br />
* To get an account to edit this wiki send a message to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette].<br />
== New candidates topics for 2019-2020 ==<br />
* '''Add suggestions here or send them to [mailto:georges@yorku.ca?subject=Account%20on%20SCS%20wiki Georges Monette] who can add them for you.'''<br />
<br />
== Candidates from 2018-2019==<br />
* '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. <br />
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226]<br />
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter<br />
** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-values and the concept of statistical significance.<br />
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]<br />
* Some statistical methods topics, not yet clearly articulated:<br />
** Clustering methods [RC: Could this fall under the Big Data label?]<br />
** Robustness<br />
** Mediation<br />
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]<br />
* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]<br />
* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. <br />
* '''Evidence-based medicine''': Ideas and implications<br />
* '''Machine Learning, AI, Deep Learning''': An Overview <br />
* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?]<br />
* '''Missing Data'''<br />
* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]<br />
** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment.<br />
<br />
== Links to past episodes of SCS Reads ==<br />
* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]<br />
* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]<br />
* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]<br />
* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]<br />
* [[SCS Reads 2014-2015]]<br />
* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]<br />
* [[SCS Reads -- past seminars]]</div>Georges