In this course, you will :
- Learn about the "parallel slopes models" class of linear models. There is one numeric explanatory variable and one categorical explanatory variable.
- Learn how to compare models so you can choose the best one.
- Discover the meaning of interaction terms in linear models.
- how to include two, three, or even more numerical explanatory variables in a linear model
- Learn how to predict a binary outcome and classify observations using logistic regression, a generalised linear model (GLM).
- Investigate the relationship between price and the quality of food, service, and decor in New York City's Italian restaurants.
- Parallel Slopes
- Evaluating and extending parallel slopes model
- Multiple Regression
- Logistic Regression
- Case Study: Italian restaurants in NYC