Exploratory data analysis

Multiple regression can be an effective technique for understanding how a response variable changes as a result of changes to more than one explanatory variable. But it is not magic -- understanding the relationships among the explanatory variables is also necessary, and will help us build a better model. This process is often called exploratory data analysis (EDA) and is covered in another DataCamp course.

One quick technique for jump-starting EDA is to examine all of the pairwise scatterplots in your data. This can be achieved using the pairs() function. Look for variables in the nyc data set that are strongly correlated, as those relationships will help us check for multicollinearity later on.

Which pairs of variables appear to be strongly correlated?

Possible Answers

Case and Decor.
Restaurant and Price.
Price and Food.
Price and East.

Exercise

Exploratory data analysis

Instructions

Possible Answers