Editing Statistics and Statistical Programming (Winter 2017)
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:'''Instructor:''' [http://mako.cc/academic/ Benjamin Mako Hill] ([http://www.com.washington.edu/hill/ University of Washington]) | :'''Instructor:''' [http://mako.cc/academic/ Benjamin Mako Hill] ([http://www.com.washington.edu/hill/ University of Washington]) | ||
:'''Course Websites''': | :'''Course Websites''': | ||
:* We will use Canvas for [https://canvas.uw.edu/courses/ | :* We will use Canvas for [https://canvas.uw.edu/courses/1124086/announcements announcements], [https://canvas.uw.edu/courses/1124086/assignments turning in assignments], and [https://canvas.uw.edu/courses/1124086/discussion_topics discussion] (if you choose to use them) | ||
:* Everything else will be linked on this page. | :* Everything else will be linked on this page. | ||
:'''Course Catalog Description:[https://www.washington.edu/students/crscat/com.html#com521]''' | :'''Course Catalog Description:[https://www.washington.edu/students/crscat/com.html#com521]''' | ||
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== Overview and Learning Objectives == | == Overview and Learning Objectives == | ||
This course is the second course in a two-quarter quantitative methods sequence in the University of Washington's Department of Communication MA/PhD program. The first course (COM 520) is | This course is the second course in a two-quarter quantitative methods sequence in the University of Washington's Department of Communication MA/PhD program. The first course (COM 520) is a first introduction to quantitative social science in communication and focuses primarily on what you might think of the "soft skills" associated with doing social science: the conceptualization, operationalization of quantifiable variables and the design of quantitative analyses. That course introduces some univariate and bivariate statistics at the end and briefly touches on linear regression. That said, all of the statistical work in that course this is done using the tools that students already know (e.g. with spreadsheet software like LibreOffice, Google Sheets or Microsoft Excel). This class assumes that students have taken COM 520 and that they understand what is involved in describing and testing social scientific theories with data and that basic terminology of quantitative social science is going to be familiar. | ||
This course (COM 521) is focused on technical skill-building and aims to be a get-your-hands-dirty introduction to statistics and statistical programming. The point of the course is to give you the mathematical and technical tools to carry out your own statistical analyses. Through the process, we're going to try to help you become more sophisticated consumers of quantitative research. | This course (COM 521) is focused on technical skill-building and aims to be a get-your-hands-dirty introduction to statistics and statistical programming. The point of the course is to give you the mathematical and technical tools to carry out your own statistical analyses. Through the process, we're going to try to help you become more sophisticated consumers of quantitative research. | ||
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Although we'll be doing some math in the course, this is not a math class. I am going to assume you're familiar with basic algebra and arithmetic. This course will not require knowledge of calculus. In general we're not going to cover the math behind the techniques we'll be covering. Unlike many statistics classes, I'm definitely not going to be doing proofs on the board. Instead, the class is unapologetically focused on ''the application of statistic methodology''. In that sense, the goal of the is course is to create ''informed consumers'' of quantitative methodology, not producers of new types of methods. My goal is to train producers of social scientific research that use statistics as a means toward an end. | Although we'll be doing some math in the course, this is not a math class. I am going to assume you're familiar with basic algebra and arithmetic. This course will not require knowledge of calculus. In general we're not going to cover the math behind the techniques we'll be covering. Unlike many statistics classes, I'm definitely not going to be doing proofs on the board. Instead, the class is unapologetically focused on ''the application of statistic methodology''. In that sense, the goal of the is course is to create ''informed consumers'' of quantitative methodology, not producers of new types of methods. My goal is to train producers of social scientific research that use statistics as a means toward an end. | ||
This course does not seek to be the last stats class you take. I started grad school having not taken a math class since high school (basically) and took 12 different statistics and math courses over the course of my time in graduate school. Honestly, I wish I had done more. What this class seeks to do is give you a solid basis on which to build statistical knowledge. Anyone who finishes this class should feel comfortable moving on to take advance classes in CSSS | This course does not seek to be the last stats class you take. I started grad school having not taken a math class since high school (basically) and took 12 different statistics and math courses over the course of my time in graduate school. Honestly, I wish I had done more. What this class seeks to do is give you a solid basis on which to build statistical knowledge. Anyone who finishes this class should feel comfortable moving on to take advance classes in CSSS and to start building toward a Concentration in Statistics in Communication certificate. | ||
We'll cover theses basic statistical techniques: t-tests; chi-squared tests; ANOVA, MANOVA, and related methods; linear regression; and end with logistic regression. | We'll cover theses basic statistical techniques: t-tests; chi-squared tests; ANOVA, MANOVA, and related methods; linear regression; and end with logistic regression. | ||
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* Feel comfortable reading papers that use basic statistical techniques. | * Feel comfortable reading papers that use basic statistical techniques. | ||
* Feel comfortable and prepared enrolling in future statistics courses in CSSS. | * Feel comfortable and prepared enrolling in future statistics courses in CSSS. | ||
== Note About This Syllabus == | == Note About This Syllabus == | ||
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# Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If I don't fill in a "To Be Determined" one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first. | # Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If I don't fill in a "To Be Determined" one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first. | ||
# Closely monitor your email or [https://canvas.uw.edu/courses/ | # Closely monitor your email or [https://canvas.uw.edu/courses/1124086/announcements the announcements section on the course website on Canvas]. When I make changes, these changes will be recorded in [http://wiki.communitydata.cc/index.php?title=Statistics_and_Statistical_Programming_(Winter_2017)&action=history the history of this page] so that you can track what has changed and I will summarize these changes in an announcement on Canvas that will be emailed to everybody in the class. | ||
# I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback. | # I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback. | ||
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Diez, Barr, and Çetinkaya-Rundel's is a free, and freely-licensed, online statistics textbook. Over the last seven years, the book has also developed a large online community of students and teachers who have shared other resources. The book, lectures notes, and more are all freely licensed which has allowed the text to be adapted in a series of different fields. The book is excellent and it has been adopted extraordinarily widely. You can buy versions from Amazon in either [https://www.openintro.org/redirect.php?go=amazon_os3_hardcover&referrer=/stat/textbook.php full color hardcover] ($19.99) or in [https://www.openintro.org/redirect.php?go=createspace_os3&referrer=/stat/textbook.php black and white paperback] ($7.60). I haven't purchased a paper copy so I can't speak to the quality of either. | Diez, Barr, and Çetinkaya-Rundel's is a free, and freely-licensed, online statistics textbook. Over the last seven years, the book has also developed a large online community of students and teachers who have shared other resources. The book, lectures notes, and more are all freely licensed which has allowed the text to be adapted in a series of different fields. The book is excellent and it has been adopted extraordinarily widely. You can buy versions from Amazon in either [https://www.openintro.org/redirect.php?go=amazon_os3_hardcover&referrer=/stat/textbook.php full color hardcover] ($19.99) or in [https://www.openintro.org/redirect.php?go=createspace_os3&referrer=/stat/textbook.php black and white paperback] ($7.60). I haven't purchased a paper copy so I can't speak to the quality of either. | ||
Verzani's book is an introduction to the R programming language. It's designed to be used as a companion to a basic introductory statistics textbook (like OpenIntro). It's a poor stand-alone text but it will provide good resources for the material we're covering in the course and it should act as a good reference going forward. The book is available online for about $50. | Verzani's book is an introduction to the R programming language. It's designed to be used as a companion to a basic introductory statistics textbook (like OpenIntro). It's a poor stand-alone text but it will provide a good resources for the material we're covering in the course and it should act as a good reference going forward. The book is available online for about $50. ''I'd recommend holding off on purchasing the book until after the first class.'' | ||
Although it's not required for the course, I want to point you to these two books. When I was learning R, these both were very useful references: | Although it's not required for the course course, I want to point you to these two books. When I was learning R, these both were very useful references: | ||
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/UW Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon]) | * Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/UW Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon]) | ||
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/UW Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]) | * Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/UW Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]) | ||
There are also two non-textbook resources I wanted to point you | There are also two non-textbook resources I wanted to point you two that are invaluable: | ||
* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — When I was learning R, I ''literally'' took a similar reference card with me everywhere and looked at | * [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — When I was learning R, I ''literally'' took a similar reference card with me everywhere and looked at id dozens of times a day. | ||
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow | * [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. | ||
== Assignments == | == Assignments == | ||
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=== Weekly Problem Sets and Participation === | === Weekly Problem Sets and Participation === | ||
Each week I will post a problem set with a list of questions. Some of these will be drawn from the textbooks and some will | Each week I will post a problem set with a list of questions. Some of these will be drawn from the textbooks and some will ones I design or write. The questions will cover: | ||
* '''Statistics questions''' — These will | * '''Statistics questions''' — These will questions about statistics from the OpenIntro sections as well as any empirical papers that are listed as required for that that day. | ||
* '''Programming challenges''' | * '''Programming challenges''' -- These will be R programming problems that cover material from the Verzani text that was listed as required from the previous session. | ||
I won't be grading these assignment and I won't be asking you to turn in anything for the ''statistics questions'' portion of the weekly assignment. That said, we will spend a good chunk of class each day going through the answers to the questions due on that day. | I won't be grading these assignment and I won't be asking you to turn in anything for the ''statistics questions'' portion of the weekly assignment. That said, we will spend a good chunk of class each day going through the answers to the questions due on that day. | ||
Because randomness is an extremely important concept in statistics, I will use a small R program to '''randomly cold call''' on students in the class to walk through your "answer" to each question and explain your reasoning to the class. We'll then have an opportunity to discuss the different approaches as a group. I don't promise to ask all of these questions in class (especially if | Because randomness is an extremely important concept in statistics, I will use a small R program to '''randomly cold call''' on students in the class to walk through your "answer" to each question and explain your reasoning to the class. We'll then have an opportunity to discuss the different approaches as a group. I don't promise to ask all of these questions in class (especially if clear that folks get the point). Although I might ask them, I won't cold call for questions that are not on the list. | ||
For the programming challenges, I will ask that everybody shares code for any solutions to programming problems before class so we can walk through in class. If you get completely stuck on a problem and cannot "solve" it, that's OK, but share the code that you do have so that you can walk us through what you did and what you were thinking. | For the programming challenges, I will ask that everybody shares code for any solutions to programming problems before class so we can walk through in class. If you get completely stuck on a problem and cannot "solve" it, that's OK, but share the code that you do have so that you can walk us through what you did and what you were thinking. | ||
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I'm not going to form groups for you but it's totally fine with me if you want to work on these problem sets in small groups. | I'm not going to form groups for you but it's totally fine with me if you want to work on these problem sets in small groups. | ||
The "Participation Rubric" section of [https://mako.cc/teaching/assessment.html my page on assessment] gives the details on how I | The "Participation Rubric" section of [https://mako.cc/teaching/assessment.html my detailed page on assessment] gives the details on how I evaluating participation in most of my classes. That said, I don't generally use discussed problems sets in the way we'll be doing in this class. As a result, if you sense an conflict between material in this section and material on this page, you can safely assume that this page takes precedence. | ||
=== Research Project === | === Research Project === | ||
As a demonstration of your learning in this course, you will design and | As a demonstration of your learning in this course, you will design and carrying out a quantitative research project, start to finish. This means you will all: | ||
* '''Design and describe a social scientific study''' — You should all have experience doing this at least once in COM520. The study you design should involves quantitative analysis and should | * '''Design and describe a social scientific study''' — You should all have experience doing this at least once in COM520. The study you design should involves quantitative analysis and should something you can complete at least a first pass at over the course of this quarter. | ||
* '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a re-analysis of a previously collected dataset. | * '''Find a dataset''' — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a re-analysis of a previously collected dataset. | ||
* '''Engage in descriptive data analysis''' — Use R to create descriptive statistics and visualization to describe your data. | * '''Engage in descriptive data analysis''' — Use R to create descriptive statistics and visualization to describe your data. | ||
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* '''Ensure replicability''' — I'll expect you all to provide code and data for your analysis in a way that makes your work replicable by other researchers. | * '''Ensure replicability''' — I'll expect you all to provide code and data for your analysis in a way that makes your work replicable by other researchers. | ||
Although it's not required, I ''strongly urge each of you'' to take this opportunity to produce a document that will further your academic career outside of the class. There are many ways that this can happen but the obvious ones are that the paper is something you can submit for publication to a journal or conference, that provides primarily analysis for or acts | Although it's not required, I ''strongly urge each of you'' to take this opportunity to produce a document that will further your to academic career outside of the class. There are many ways that this can happen but the obvious ones are that the paper is something you can submit for publication to a journal or conference, that provides primarily analysis for or acts a pilot analysis that you can report in a grant proposal or thesis proposal, and/or that serves as part of your masters thesis or dissertation. | ||
==== Project and Dataset Identification ==== | ==== Project and Dataset Identification ==== | ||
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* A one paragraph abstract of the proposed study and research question, theory, community, and/or groups you plan to study. | * A one paragraph abstract of the proposed study and research question, theory, community, and/or groups you plan to study. | ||
* A short description of how the project will | * A short description of how the project will fits into your career trajectory. | ||
* An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain when you will have access to the data. | * An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain when you will have access to the data. | ||
==== Final Project | ==== Final Project ==== | ||
;Outline Due Date: February 21 | ;Outline Due Date: February 21 | ||
;Maximum outline length: 5 pages | ;Maximum outline length: 5 pages | ||
;Paper Due Date: March 19 | ;Paper Due Date: March 19 | ||
;Maximum length: 6000 words (~20 pages) | ;Maximum outline length: 6000 words (~20 pages) | ||
;Presentation Date: March | ;Presentation Date: March 7 | ||
;All Deliverables: Turn in in Canvas | ;All Deliverables: Turn in in Canvas | ||
I'm expecting you to produce a draft of a short research paper that, after some additional work, you could consider submitting for publication. I'm also very open to the project being a part of a dissertation. I don't expect the papers to be ''publication ready'' but I do expect them to have well considered drafts of all the necessary pieces in terms of quantitative methodology. | I'm expecting you to produce a draft of a short research paper that, after some additional work, you could consider submitting for publication. I'm also very open to the project being a part of a dissertation. I don't expect the papers to be ''publication ready'' but I do expect them to have well considered drafts of all the necessary pieces in terms of quantitative methodology. | ||
Because the emphasis in this class is on statistics and methodology and because I'm not an expert in each of your areas or fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is so important. Instead of providing all of | Because the emphasis in this class is on statistics and methodology and because I'm not an expert in each of your areas or fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is so important. Instead of providing all of this details, instead feel free to start with a brief summary of the purpose and importance of this research, and an introduction of your research questions or hypotheses. If your provide more detail, that's fine, but I won't give you detailed feedback on this parts. | ||
I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class. | I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class. | ||
'''''Details Forthcoming:''''' ''Although this material is still somewhat thin, I'll be posting many additional details about the expectations for the final paper as we move forward through the quarter.'' | |||
=== Grading === | === Grading === | ||
I have put together a very detailed page that describes [https://mako.cc/teaching/assessment.html grading rubric] I will be using in this course. Please read it carefully I will assign grades for each of | I have put together a very detailed page that describes [https://mako.cc/teaching/assessment.html grading rubric] I will be using in this course. Please read it carefully I will assign grades for each of following items on the UW 4.0 grade scale according to the weights below: | ||
* Participation: 40% | * Participation: 40% | ||
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* Final Presentation: 10% | * Final Presentation: 10% | ||
* Final Paper: 40% | * Final Paper: 40% | ||
== Structure of Class == | == Structure of Class == | ||
I expect everybody to come to class, every week, with their laptop and a power cord, being ready to answer any question on the problem set and having uploaded and shared code to the code related questions. The class is listed as nearly 4 hours long and, with the exception of a few short breaks, I intend to use the entire period | I expect everybody to come to class, every week, with their laptop and a power cord, being ready to answer any question on the problem set and having uploaded and shared code to the code related questions. The class is listed as nearly 4 hours long and, with the exception of a few short breaks, I intend to use the entire period most days. | ||
Although structure of class will vary, it will generally include the following parts. | Although structure of class will vary, it will generally include the following parts. | ||
# Quick updates about assignments | # Quick updates about assignments. | ||
# Discussion of '''programming challenges''' due that day. | # Discussion of '''programming challenges''' due that day. | ||
# [''Possibly/Sometimes''] Short lecture and/or Q&A about new material in Diez, Barr, and Çetinkaya-Rundel | # [''Possibly/Sometimes''] Short lecture and/or Q&A about new material in Diez, Barr, and Çetinkaya-Rundel | ||
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=== Week 1: Tuesday January 3: Introduction, Setup, and Data and Variables === | === Week 1: Tuesday January 3: Introduction, Setup, and Data and Variables === | ||
'''Assignment before class:''' | |||
* [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 1]] | |||
'''Required Readings:''' | '''Required Readings:''' | ||
* Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data) | * Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data) | ||
* Verzani: §1 (Getting Started), §2 (Univariate data) | * Verzani: §1 (Getting Started), §2 (Univariate data) | ||
* Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Available through UW libraries]] | * Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Available through UW libraries]] | ||
'''Optional Readings:''' | '''Optional Readings/Resources:''' | ||
* Verzani: §A (Programming) | * Verzani: §A (Programming) | ||
* [https://www.openintro.org/download.php?file=os3_slides_01&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's Openintro §1 Lecture Notes] | |||
* [https://www.openintro.org/download.php?file=os3_slides_01&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including some for §1 | * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including some for §1 | ||
=== Week 2: Tuesday January 10: Probability and Visualization === | === Week 2: Tuesday January 10: Probability and Visualization === | ||
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* Diez, Barr, and Çetinkaya-Rundel: §2 (Probability) | * Diez, Barr, and Çetinkaya-Rundel: §2 (Probability) | ||
* Verzani: §3.1-2 (Bivariate data), §4 (Multivariate data), §5 (Multivariate graphics) | * Verzani: §3.1-2 (Bivariate data), §4 (Multivariate data), §5 (Multivariate graphics) | ||
* | * ''Empirical Paper TBD'' | ||
=== Week 3: Tuesday January 17: Distributions === | |||
'''Required Readings:''' | '''Required Readings:''' | ||
* Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4 | * Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4 | ||
* Verzani: §6 (Populations) | * Verzani: §6 (Populations) | ||
* ''Empirical Paper TBD'' | |||
* | |||
=== Week 4: Tuesday January 24: Statistical significance and hypothesis testing === | === Week 4: Tuesday January 24: Statistical significance and hypothesis testing === | ||
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* Diez, Barr, and Çetinkaya-Rundel: §4 (Foundations for inference) | * Diez, Barr, and Çetinkaya-Rundel: §4 (Foundations for inference) | ||
* Verzani: §7 (Statistical inference), §8 (Confidence intervals) | * Verzani: §7 (Statistical inference), §8 (Confidence intervals) | ||
* ''Empirical Paper TBD'' | |||
* | |||
=== Week 5: Tuesday January 31: Continuous Numeric Data & ANOVA === | === Week 5: Tuesday January 31: Continuous Numeric Data & ANOVA === | ||
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* Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical data) | * Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical data) | ||
* Verzani: §9 (significance tests), §12 (Analysis of variance) | * Verzani: §9 (significance tests), §12 (Analysis of variance) | ||
* | * ''Empirical Paper TBD'' | ||
=== Week 6: Tuesday February 7: Categorical data === | === Week 6: Tuesday February 7: Categorical data === | ||
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* Diez, Barr, and Çetinkaya-Rundel: §6 (Inference for categorical data) | * Diez, Barr, and Çetinkaya-Rundel: §6 (Inference for categorical data) | ||
* Verzani: §3.4 (Bivariate categorical data); §10.1-10.2 (Goodness of fit) | * Verzani: §3.4 (Bivariate categorical data); §10.1-10.2 (Goodness of fit) | ||
* | * ''Empirical Paper TBD'' | ||
=== Week 7: Tuesday February 14: Simple Linear Regression === | |||
=== Week 7: Tuesday February 14: Linear Regression === | |||
'''Required Readings:''' | '''Required Readings:''' | ||
* Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression) | * Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression) | ||
* Verzani: §11.1-2 (Linear regression), | * Verzani: §11.1-2 (Linear regression), | ||
* | * ''Empirical Paper TBD'' | ||
=== Week 8: Tuesday February 21: Multiple and Logistic Regression === | |||
=== Week 8: Tuesday February 21: | |||
'''Required Readings:''' | '''Required Readings:''' | ||
* Diez, Barr, and Çetinkaya-Rundel: §8 (Multiple and logistic regression) | |||
* Diez, Barr, and Çetinkaya-Rundel: §8 | |||
* Verzani: §11.3 (Linear regression), §13.1 (Logistic regression) | * Verzani: §11.3 (Linear regression), §13.1 (Logistic regression) | ||
* | * ''Empirical Paper TBD'' | ||
=== Week 9: Tuesday February 28: Consulting Meetings === | === Week 9: Tuesday February 28: Consulting Meetings === | ||
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We won't meet as a group. Instead, you will each meet on-on-one with me to work through challenges and issues with your analysis. | We won't meet as a group. Instead, you will each meet on-on-one with me to work through challenges and issues with your analysis. | ||
=== Week 10: Tuesday March 7 | === Week 10: Tuesday March 7: Final Presentations === | ||
== Administrative Notes == | == Administrative Notes == | ||
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=== Credit and Notes === | === Credit and Notes === | ||
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. In the sense that he used the same two textbooks, I also drew some inspiration and confidence from Tom S. Clark's [http://www.tomclarkphd.com/teaching/POLS508F14.pdf syllabus for POLS 508: Data Analysis in Fall 2014]. | This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. In the sense that he used the same same two textbooks, I also drew some inspiration and confidence from Tom S. Clark's [http://www.tomclarkphd.com/teaching/POLS508F14.pdf syllabus for POLS 508: Data Analysis in Fall 2014] in that he I saw he was using the same two textbooks. |