STAT 311: Elements of Statistical Methods
Undergraduate course, Univ of Washington, STAT 311, 2016
I was the lead instructor for Stat 311 at UW during the Summer of 2016. The course is designed to be an introductory statistics course for undergrad students in the physical or social sciences. Students are expected to develop statistical literacy and basic knowledge in experimental design, hypothesis testing, probability, and linear regression. We also introduce students to statistical computing via R in both the labs and homeworks.
The course materials are provided below. If you find them helpful you are welcome to use them, but please drop me a line to let me know.
- Lecture 1: Intro
- Lecture 2: Describing data
- Lecture 3: Quantitative data
- Lecture 4: Regression
- Lecture 5: Z-scores
- Lecture 6: Categorical data
- Lecture 7: Contingency tables
- Lecture 8: Gathering data
- Lecture 9: Gathering data (cont)
- Lecture 10: Probability
- Lecture 11: Probability (cont)
- Lecture 12: Combinatorics
- Lecture 13: Random variables
- Lecture 14: Continuous variables
- Lecture 15: Sampling distributions
- Lecture 16: Confidence intervals
- Lecture 17: Confidence intervals (cont)
- Lecture 18: Hypothesis testing
- Lecture 19: Hypothesis testing (cont)
- Lecture 20: Hypothesis testing for regression
- Lecture 21: Hypothesis testing for regression (cont)
- Lecture 22: Hypothesis testing contingency tables
- HW 1: Descriptive statistics
- HW 2: Regression and discrete data
- HW 3: Z-scores, simpsons paradox, specificity and sensitivity
- HW 4: Probability
- HW 5: Combinatorics and random variables
- HW 6: Confidence intervals
Each of the labs examines a data set, and provides R code for the students to run analyses. There are also questions throughout for them to discuss in the lab section.
- Lab 1: Examining data on bike crossings of the Fremont Bridge. Students should learn about subsetting, plotting, and exploratory analysis
- Lab 2a, Lab 2b: Examining data from previous US presidential elections and World Bank data on GDP per capita. Students should learn about calculating a linear regression and examining residuals.
- Lab 3a, Lab 3b: Examining data from election fraud, The Bachelorette, and the Ebola outbreak. Students should learn about contingency tables and sampling random variables.
- Lab 4: Students will simulate sampling distributions.
- Lab 5a, Lab 5b: Students will learn about random variables and be exposed to CDF’s and PDF’s
- Lab 6a, Lab 6b: Students will see a proof of the CLT via simulation and also examine frequentist CI’s via simulation.
- Lab 7, Lab 7: Examining data about deflated footballs, wine ratings, and stock vs weather data. Students will learn about two sample tests, hypothesis testing for regression coefficients, and multiple testing.