Lectures
This page contains link to the lectures I give throughout the semester. Clicking the title of the week’s lecture will go to a PDF, embedded in the user’s browser, by default. The bottom left icons link to the Github directory for the lecture (), the R Markdown document for the lecture (), and a PDF, embedded on Github, for the lecture ().

(Theory and) Research Design
tl;dr: Research is a process that does not 'prove.' Theories are evaluated for (in)consistency with predictions. Everything has assumptions. Think of the quantitative research design as you would a spreadsheet.

Descriptive Statistics
tl;dr: Understanding the kind of information your variable communicates will condition how you can describe them, and never trust a summary statistic of a variable (or two variables) without looking at them first.

Probability and Sampling
tl;dr: There are very important rules of probability and distributions that underpin applied statistics. Here's a handful of those. Also: here's a crash course on the logic of random sampling and central limit theorem.

Hypothesis Testing
tl;dr: Applied hypothesis testing reduces to statements about the probability of the test statistic, given some assumed/fixed population parameter. It's not quite the question you're asking, but it's the answer you're getting.

Bivariate OLS
tl;dr: In the simple bivariate case, the OLS model is just a modified slopeintercept equation. Even when you extend it forward, it's still that. There are still major assumptions that underpin what you're doing, but try not to overthink it for now.

Extending the Linear Model: Fixed Effects, Controls, and Interactions
tl;dr: Multiple regression is just like bivariate regression, but with more independent variables that communicate partial effects. Fixed effects are a misleading name for 'categorical dummy variables'. *Be mindful when interacting things.*

Linear Model Diagnostics (and What to Do if You Flunk One)
tl;dr: The linear model and its OLS estimator have assumptions and you should look into them all for every model you run. Let the LINE mnemonic guide you.

BONUS: Causality and Ethics
tl;dr: This is a final lecturecut for timeabout 'causality', what are its (strong) implications, and what are some issues associated with this term and research in general. Budget cuts and time restraints mean I can only put this here for you to see. This won't be a lecture.