Course schedule
Math 214 meets Mondays and Wednesdays, 10:10 to 12:05. The Wednesday session is split between a classroom session and a computer lab session.
Mondays will be dedicated to theory, Wednesdays to practice.
Expected workload
A full time semester is 12 credits; this means that a 4 credit course is about 33% of full time.
Out of 40 hours, these 33% makes out 13h20m in a week. We meet 4h in class. You should be spending about 9h every week out of class working with our material.
It has been shown in research that “Effective Time on Task” is the most important predictor of how much you learn. I will assign homework to train the most recent techniques, but I strongly encourage you to work through as many of the book’s exercises as you can.
Topic sequence
| Week | Lecture | Date | Topic | Chapter | Homework |
|---|---|---|---|---|---|
| Descriptive Statistics | |||||
| 1 | 1 | Jan 30 | What is data? | 1.1-4, 3.1-4 | Get the book |
| 1 | 2 | Feb 1 | The RStudio platform |
Make sure you can access RStudio |
|
| 2 | 3 | Feb 6 | Where does data come from? | 1.5-6, 3.5, 3.7-8 | |
| 2 | 4 | Feb 8 | How do we access data? | ||
| 3 | 5 | Feb 15 | How do we present data? | 4.1-5 | Deadline for choosing a dataset |
| 4 | 6 | Feb 22 | Presenting data? Summary statistics | 1.7, 2.4 | |
| 5 | 7 | Feb 27 | A first look at relationships | 4.6-10, 6.1-3, 7.1-4 | |
| 5 | 8 | Mar 1 | Plotting summary statistics and distributions | ||
| 6 | 9 | Mar 6 | Data cleaning | 3.9, 5.8 | |
| 6 | 10 | Mar 8 | Data cleaning | ||
| 7 | 11 | Mar 13 | Ethics in Statistics | Read ASA Ethical Guidelines, Mapping Police Violence backstory, Open Data and Open Methods. | Think about the questions: Is it always right to collect data? Is it always right to analyze data? What makes a data collection or analysis plan wrong? When should one refuse to work as a statistician? |
| 7 | 12 | Mar 15 | Ethics in Statistics; Visit from MPV | ||
| Inferential Statistics | |||||
| 8 | 13 | Mar 20 | 2.1-3, 2.5-6, 5.1-4 | First report deadline | |
| 8 | 14 | Mar 22 | Introduction to inference, one-sample mean tests | ||
| 9 | 15 | Mar 27 | Assumptions in data, normality tests | 5.1-6 | Peer feedback deadline |
| 9 | 16 | Mar 29 | Assumptions in data, equal variances tests | Hypothesis registration deadline | |
| 10 | 17 | Apr 3 | Correlation and effect size | 6.4-9 | |
| 10 | 18 | Apr 5 | |||
| 11 | 19 | Apr 19 | Linear regressions | 7.5-9 | |
| 11 | 20 | Apr 20 | |||
| 12 | 21 | Apr 24 | Two-sample means comparisons | 9.1-7, 5.7 | |
| 12 | 22 | Apr 26 | |||
| 13 | 23 | May 1 | Categorical data | 18.1-6 | |
| 13 | 24 | May 3 | |||
| 14 | 25 | May 8 | Several means (ANOVA) | 10.1-4 | |
| 14 | 26 | May 10 | |||
| 15 | 27 | May 15 | ANOVA continued | 10.5-8 | Deadline, inferential report |
| 15 | 28 | May 17 | |||
| May 23 | Final Exam |
Project 1
Pick a dataset from our collection, write a report describing the dataset comprehensively. Your report should contain appropriately chosen graphs, descriptions of observed descriptive statistics of the available variables in the data, and an evaluation of the experiment and sampling design in the data collection.
Project 2
Based on the report from Project 1, suggest a number of hypotheses for what the dataset could indicate. With the larger sample from the same source made available for Project 2, write a report evaluating your hypotheses and testing them. Report your inferential results both with confidence intervals and with significance levels.
Final Examination
The final will be a computer lab session focusing on using and interpreting results from RStudio. There will be an opportunity in the final that allow you to raise your grade by up to two minor grade steps: for instance, raise from a B to a B+ or an A-.