Part 4 | Bivariate GLM
// build simple models of relationships //Part 4 introduces the workhorse of empirical economics: regression analysis. In Part 3, we developed the simplest statistical model of samples with unknown population parameters. This model allowed us to test simple hypotheses about the population parameters. But economic relationships are rarely so simple. The General Linear Model not only lets us test simple hypotheses, but also lets us handle relationships between variables of different types - continuous, categorical, and their interactions - while maintaining our core tools of visualization and residual analysis. These tools are the foundation of modern science.
Part 4.1 ~ Numerical Predictors
Finding relationships between numerical variables.
Concept 4.1 // Numerical Predictors
The slope parameters follow a normal distribution.
Exercise 4.1 // Numerical Predictors
Relationship between happiness and GDP per capita.
Part 4.2 ~ Model Residuals
Checking the assumptions of the linear regression model using residuals.
Concept 4.2 // Model Residuals
Checking the assumptions of the linear regression model using residuals.
Exercise 4.2 // Model Residuals
Checking the assumptions of the linear regression model using residuals.
Part 4.3 ~ Categorical Predictors
Finding relationships between a categorical predictor and a continuous outcome.
Exercise 4.3 // Categorical Predictors
Finding relationships between categorical variables.
Part 4.4 ~ Timeseries Models
Building models of relationships between variables through time.
Exercise 4.4 // Timeseries Models
Four models of relationships between GDP and Unemployment through time.
Homework 4.4
Nothing due :)