All slide decks

Fifteen revealjs presentations, in teaching order

Author

Eduard (Eddie) Bukin

Every deck opens as a full-screen presentation. Useful keys inside a deck: ? shows all shortcuts, o opens the slide overview, f goes full screen, and e switches to print view so you can save the deck as a PDF from your browser.

The Source link opens the .qmd behind each deck β€” the R code that produced every figure and table is in there.

1 Β· Introduction

Deck What it covers Source
πŸ–₯ Introduction. Organisation. Setup. Course organisation, software setup, ceteris paribus, selection bias .qmd

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2 Β· Data wrangling

Deck What it covers Source
πŸ–₯ Workflow, tidy data, wrangling The analysis workflow and what makes data tidy .qmd
πŸ–₯ Wrangling with dplyr Extended version of the deck above, with the full dplyr verb set .qmd
πŸ–₯ Import data Reading CSV and Excel with readr and readxl; cleaning names with janitor .qmd

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3 Β· Describing data

Deck What it covers Source
πŸ–₯ Exploring numerical data Distributions, summaries, histograms, boxplots, scatterplots .qmd
πŸ–₯ Correlation Correlation types, interpreting strength, and where correlation misleads .qmd

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4 Β· Simple regression

Deck What it covers Source
πŸ–₯ Simple regression One predictor, least squares, interpreting coefficients .qmd

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5 Β· Multiple regression

Deck What it covers Source
πŸ–₯ Multiple linear regression Several predictors, and holding other things equal in practice .qmd
πŸ–₯ Linearity Diagnosing non-linearity; fixing it with transformations .qmd
πŸ–₯ Omitted variable bias What leaving a variable out does to the estimates you keep .qmd
πŸ–₯ Collinearity When predictors carry the same information; VIF and diagnostics .qmd
πŸ–₯ Heteroscedasticity Non-constant error variance and robust standard errors .qmd
πŸ–₯ Hedonic prices model A full applied example decomposing prices into characteristics .qmd

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6 Β· Panel regression

Deck What it covers Source
πŸ–₯ Panel regression analysis Fixed and random effects; what repeated observations buy you .qmd

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7 Β· Instrumental variables

Deck What it covers Source
πŸ–₯ Instrumental variable Identification with an endogenous regressor; returns to schooling .qmd

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