Schedule
1 Lectures
Please check often. Subject to change. New material is added approximately on a weekly basis.
Chapters refer to the assigned textbook (3rd edition). Corresponding chapters exist in the 2nd edition but may have a different number.
| Week | Dates | Topic | Recommended Readings |
|---|---|---|---|
| 1 | 9/3 | 1.1 Statistics & Samples | Ch. 1 |
| 2 | 9/8 | 1.2 Statistics & Samples | Ch.1, Interleaf 1: Correlation does not require causation |
| 2 | 9/10 | 1.3 Statistics & Samples | Ch.1, Interleaf 1: Correlation does not require causation |
| 3 | 9/15 | 1.4 Statistics & Samples | Interleaf 6: Controls in medical studies |
| 2.1 Displaying & Describing Data | Ch.2-3 | ||
| 3 | 9/17 | 2.2 Displaying & Describing Data (center) | Ch.2-3 |
| 4 | 9/22 | 2.3 Displaying & Describing Data (spread) | Ch.2-3 |
| 4 | 9/24 | 2.4 Displaying & Describing Data (plots) | Ch. 2-3 |
| 5 | 9/29 | 2.5 Displaying & Describing Data (plots) | Ch. 2-3 |
| 5 | 10/1 | 3.1 Probability | Ch.5 |
| 6 | 10/6 | 3.2 Probability | Ch.5 |
| 6 | 10/8 | 3.3 Probability | Ch.5 |
| 10/13 | No classes (Fall Break!) | ||
| 7 | 10/20 | 3.4. Probability | Ch.4 |
| 7 | 10/22 | 4.1 Estimation | Ch.4 |
| 8 | 10/27 | 4.2 Estimation | Ch.4 |
| 8 | 10/30 | 4.3 Estimation | Ch.4 & Interleaf 2 |
| 9 | 11/3 | 4.4 Estimation | Ch.4 & Interleaf 2 |
| 9 | 11/5 | 5.1 Hypothesis Testing | Ch. 6 & Interleaf 3 |
| 10 | 11/10 | 5.2 Hypothesis Testing | Ch. 6 & Interleaf 3 |
| 10 | 11/12 | 5.3 Hypothesis Testing 6.1 Proportions & Probability Models |
Ch. 6 & Interleaf 3, Optional readings1 Ch. 7 |
| 11 | 11/17 | 6.2 Proportions & Probability Models | Ch. 7 |
| 11 | 11/19 | 7.1. Goodness-of-fit tests | Ch.8 |
| 12 | 11/24 | - Moved to 11/25: 7.2 Goodness-of-fit tests 8.1 Contingency Analyses |
Ch.8 Ch.9 |
| 12 | 11/26 | No in-person class (recorded lecture posted on Moodle) | 8.2 Contingency Analyses |
| 13 | 12/1 | 9.1 Normal Distribution | Ch.10 |
| 13 | 12/3 | 9.2 Normal Distribution | Ch.10-11 |
| 14 | 12/8 | 9.3 Normal Distribution wrap-up (If there’s time, practice problems) |
Ch.11.1-11.4 |
| 14 | 12/10 | Review Session, In-class time for Course Evals |
2 Labs
| Week | Date | Topic | Prep |
|---|---|---|---|
| 1 | 2/9 | Course Logistics | - |
| 1 | 2/9 | Rstudio/R introduction | DC2 Introduction to R |
| 2 | 9/9 | Data Types | DC Introduction to R |
| 3 | 9/16 | Data Structures | DC Introduction to Importing Data With R |
| 4 | 9/23 | Describing Data: Summary Statistics & Plots | DC Data Manipulation with dplyr Introduction to the Tidyverse (Grouping and Summarizing; Data Wrangling; Data Visualization; Types of visualizations) |
| 5 | 9/30 | R markdown, Tidyverse, & Exploratory Data Analysis | DC Reporting with Rmarkdown (Getting Started; Adding Analyses and Visualizations) |
| 6 | 10/7 | Soft Intro to Loops and Sampling in R | DC Introduction to Data Visualization with ggplot2 DC Data Manipulation with dplyr |
| 10/14 | NO LAB | (fall break!) | |
| 7 | 10/21 | apply family and conditional statements | DC Categorical Data in the Tidyverse DC Intermediate R (Loops) Practice: DC Conditionals and Control Flow |
| 8 | 10/28 | NO LAB | (in class Midterm!) |
| 9 | 11/4 | Writing Functions in R! | DC Intermediate R: Functions |
| 10 | 11/10 | Bootstrapping and Hypothesis Testing | DC Sampling in R: Bootstrap distributions |
| 11 | 11/18 | Proportions and Binomials | |
| 12 | 11/25 | Lecture (make up for 11/24) | |
| 13 | 12/2 | In class walk-through R examples seen in lectures (Goodness of fit, Contingency Analyses, Normal distr.) | |
| 14 | 12/9 | In class time to work on CA2 |
3 Problem sets
| Problem set | Topic | Due Date | Difficulty | Type |
|---|---|---|---|---|
| 1 | Ch. 1-3 | Fri 10/3 | Easy/Medium | Formative |
| 2 | Probability | Wed 10/22 | Medium/Hard | Formative |
| 3 | Ch. 4 and Ch.6 | Thu 11/13 | Medium/Hard | Formative |
| 4-53 | Ch.7-? | W 12/11 | Medium/Hard | Formative |
4 Coding Assignments
| Coding Assignment | Topics | Due Date | Difficulty | Type |
|---|---|---|---|---|
| Coding Assignment 1 | TBD | Fri 11/7 | Hard | Summative |
| Coding Assignment 2 | TBD | Thu 12/11 | Hard | Summative |
5 Midterms
| Assessment | Topics | When/Where | Difficulty | Type |
|---|---|---|---|---|
| Midterm 1 | Week 1-7 | 10/28, Park 243 | Hard | Summative |
| Midterm 2 | Weeks 1-14 | 12/17, 2-5pm, Park 228 | Hard | Summative |
6 Office Hours
Please attend OH at least 2x during the semester (the more the merrier).
| Who | When4 | Where |
|---|---|---|
| Professor drop-in hours | Monday 2-3pm | Park 211 |
| Professor drop-in hours | Wednesday 2-3:30pm | Park 211 |
| Professor drop-in hours | Thursday 2:30-3:30pm | Zoom5 |
| TA | Thursday 4-6pm | Park 227 |
If you can’t attend any of the times above you can schedule an appointment with me using my calendly link.
Footnotes
Optional but recommended readings: (a) Halsey et al. “The fickle P value generates irreproducible results”. Nat Methods., (b) Nuzzo. “Scientific method: Statistical errors. Nature↩︎
DataCamp↩︎
We decided based on polling to merge PS4-5 which will be worth 2x as many points as other PSs.↩︎
Note: no drop-in hours on 9/8; 9/29; 10/13 and 10/15 (Fall break); 12/01↩︎
Please email if planning to attend. If I am also available in person, I will send an announcement.↩︎