Multiple and Logistic Regression in R

Description

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.

Syllabus :

• Parallel Slopes
• Evaluating and extending parallel slopes model
• Multiple Regression
• Logistic Regression
• Case Study: Italian restaurants in NYC