LSR readings can be found in the free, online textbook, Learning Statistics with R by Danielle Navarro. For those interested in taking notes, I recommend the use of the Hypothes.is app to annotate webpages. I will note that the formatting of the book online is wonky in a few places. If this bothers you, or you prefer to work offline, you can download a PDF version of the book.
| Week | Date | Topic | Readings | Quiz | Homework |
|---|---|---|---|---|---|
| 1 | 1/04 | Correlation (statistical test, power) | Revelle Chapter 4 | ||
| - | 1/06 | Correlation II (correlation matrix, reliability) | |||
| - | 1/07 | Lab: Correlations | |||
| 2 | 1/11 | Univariate regression (equation, \(\hat{Y}\), \(e\)) | LSR Chapter 15 | Quiz 1 | |
| - | 1/13 | Univariate regression II (inferential tests) | |||
| - | 1/14 | Lab: Univariate regression | |||
| 3 | 1/18 | Univariate regression III (precision) | Quiz 2 | ||
| - | 1/20 | General linear model | |||
| - | 1/21 | Lab: General linear model | |||
| 4 | 1/25 | Partial correlations | Quiz 3 | ||
| - | 1/27 | Multiple regression (interpretation, model fit) | |||
| - | 1/28 | Lab: Two continuous predictors | HW 1 Due | ||
| 5 | 2/01 | MR: tolerance, model comparison | Quiz 4 | ||
| - | 2/03 | MR: Categorical predictors and one-way ANOVA | LSR Chapter 14 | ||
| - | 2/04 | Lab: Categorical predictors | |||
| 6 | 2/08 | Finish ANOVA; start assumptions | Lynam et al (2006) | Quiz 5 | |
| - | 2/10 | Assumptions and diagnostics | |||
| - | 2/11 | Lab: Diagnostics | |||
| 7 | 2/15 | Causal models | Rohrer (2018), Rohrer (2017) | Quiz 6 | |
| - | 2/17 | Interactions: Continuous x categorical | McCabe et al (2018) | ||
| - | 2/18 | Lab: Interactions | HW 2 Due | ||
| 8 | 2/22 | Interactions: Continuous predictors | LSR Chapter 16 | Quiz 7 | |
| - | 2/24 | Factorial ANOVA | |||
| - | 2/25 | Lab: ANOVA | |||
| 9 | 3/01 | Factorial ANOVA | McClelland & Judd (1993) | Quiz 8 | |
| - | 3/03 | Power and 3-way Interactions | |||
| - | 3/04 | Lab: papaja | |||
| 10 | 3/08 | Polynomials and Bootstrapping | Yarkoni & Westfall (2017) | ||
| - | 3/10 | Machine learning | Quiz 9 | ||
| - | 3/11 | Lab: Bootstrapping | HW 3 Due | ||
| Finals | 3/18 | Final Project Due |
Final: The final project is due at 9am on March 18th.
Your final grade is comprised of three components:
Quizzes are intended to assess your understanding of the theoretical principles underlying statistics. There will be a quiz every Tuesday, with the exception of the first week, when there will be no quiz, and the final week, when the quiz will be on Thursday.
Quizzes may be resubmitted with corrections and receive full credit. To resubmit a quiz, comment on the Canvas Quiz; for each question that was answered incorrectly, identify the correct answer and explain why this is the correct answer. Only if the explanation sufficiently conveys understanding of the theoretical principles will credit be given. There are no limits to the number of times a quiz may be resubmitted.
Homework assignments are intended to gauge your ability to apply the topics covered in class to the practice of data analysis. Homework assignments are to be done using R and RMarkdown; completed assignments should be submitted through Canvas. Be sure to include both the .Rmd file and the compiled HTML file.
Homework assignments are due at the time the first lab starts on the day the assignment is listed. Homework assignments may be resubmitted with corrections and receive full credit. Please note, however, that corrections can only be made to problems that were answered at initial submission. There is no limit to the number of times a homework assignment may be resubmitted. Please submit corrections using the Canvas assignment.
Late assignments will receive 50% of the points earned. For example, if you correctly answer questions totaling 14 points, the assignment will receive 7 points. If you resubmit this assignment with corrected answers (a total of 15 points), the assignment will receive 7.5 points.
You may discuss homework assignments with your classmates; however, it is important that you complete each assignment on your own and do not simply copy someone else’s code. If we believe one student has copied another’s work, both students will receive a 0 on the homework assignment and will not be allowed to resubmit the assignment for points.
The final project is due at 9am on the last day of finals week (March 18). Please note that, unlike quizzes and homeworks, there will not be an opportunity to re-attempt this assignment. Instructions for the final project can be found here.
We will be using R for all data wrangling, visualization, and analysis. You may not use another statistical program in this course. Students must have the latest version of R, which can be downloaded here. It is strongly recommended that students also download the RStudio GUI, available here. Both software programs are free.
You’ll also need to download and install some version of LaTeX. There are different software programs for different operating systems – you can find links to download them all here.
All reading assignments will be posted online.
Cheating and plagiarism. Any student caught cheating on an assignment, quiz, or exam will receive a 0 on that assignment. Frankly, you’re in graduate school, and the purpose of work is to create opportunities to learn and improve. Even if cheating helps you in the short term, you’ll quickly find yourself ill-prepared for the career you have chosen. If you find yourself tempted to cheat, please come speak to Dr. Weston about an extension and developing tools to improve your success.
Students with special needs. The UO works to create inclusive learning environments. If there are aspects of the instruction or design of this course that result in disability-related barriers to your participation, please notify me as soon as possible. You may also wish to contact Disability Services in 164 Oregon Hall at 541-346-1155 or disabsrv@uoregon.edu.
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