Introduction to Statistics Using R
Administrivia
- Course number: Psychology 9041B
- Instructor: Paul Gribble; pgribble [at] uwo [dot] ca; office: WIRB 4122
- TA: Kathleen Lyons ; kylons8 [at] uwo [dot] ca
- Lectures: Tuesdays 2:00–3:30pm, in WIRB 1130
- Labs: Thursdays 1:30pm–3:00pm, in WIRB 1130
- course syllabus
Schedule
| Class | Date | Topic | Assignment | |
|---|---|---|---|---|
| 1 | Jan 08 | 0 | no class | |
| 2 | Jan 10 | Introduction | ||
| 3 | Jan 15 | 1 | Probability | A01 (due Jan 24) |
| 4 | Jan 17 | Lab: using R, ggplot, & R Markdown (Kathleen) | PSYCH9041_R_Lab.R chapter_1_table_1.csv | |
| 3 | Jan 22 | 2 | Hypothesis Testing | A02 (Jan 31) |
| 4 | Jan 24 | Lab: Hypothesis Testing | ||
| 5 | Jan 29 | 3 | One Way ANOVA | A03 (Feb 7) |
| 6 | Jan 31 | Lab: One Way ANOVA | ||
| 7 | Feb 05 | 4 | Multiple Comparisons & Type-I Error | A04 (Feb 14) |
| 8 | Feb 07 | Lab: data wrangling & ggplotting (Kathleen) | UsingR_Lab2_Feb62019.R | |
| 9 | Feb 12 | 5 | Statistical Power & Effect Size | A05 (Feb 21) |
| 10 | Feb 14 | Lab: multiple comparisons & post-hoc tests | ||
| 11 | Feb 19 | - | reading week—no class | A06 (Feb 28) |
| 12 | Feb 21 | reading week—no class | ||
| 11 | Feb 26 | 6 | Two and Three Factor ANOVA | |
| 12 | Feb 28 | Lab | ||
| 13 | Mar 05 | 7 | Repeated Measures ANOVA & Split-Plot ANOVA | Midterm Exam (Mar 14) |
| 14 | Mar 07 | Lab : ezANOVA | ||
| 15 | Mar 12 | 8 | ANCOVA | A07 (Mar 21) |
| 16 | Mar 14 | [Lab Cancelled] | ||
| 17 | Mar 19 | 9 | ANCOVA lab | A08 (Mar 28) |
| 18 | Mar 21 | Multiple Regression lab (Kathleen) | ||
| 19 | Mar 26 | 10 | Bootstrapping & Resampling | A09 (Apr 4) |
| 20 | Mar 28 | Lab | ||
| 21 | Apr 02 | 11 | Maximum Likelihood Estimation | A10 (Apr 11) |
| 22 | Apr 04 | Lab | Final Exam (Apr 25) |
*note: The Midterm Exam and Final Exam are take-home exams
Textbook
- Designing Experiments and Analysing Data: A Model Comparison Perspective (3rd Edition) by Scott E. Maxwell, Harold D. Delaney and Ken Kelley. Routledge (2017). ISBN: 978-1138892286
- https://designingexperiments.com
- https://cran.r-project.org/web/packages/AMCP/
R resources
- https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf
- https://rstudio.com/resources/cheatsheets/
- R for Data Science (online book by Hadley Wickham & Garrett Grolemund about using R for data analysis)
- Data Visualization (online book by Kieran Healy about using R and ggplot)
- bbplot, a mod of ggplot by the BBC to produce publication-quality graphics in R
- Learning Statistics With R (book/course by Danielle Navarro)
Grades
- Assignments: 7 points each x 10 = 70 %
- Midterm Exam: 15 points = 15 %
- Final Exam: 15 points = 15 %