Analyze the dataset auto.clasp in the folder Sample Data. Try to build a causal model like the one developed in Figures 2.14, 2.21, 2.26 of my book. That is, use EDA techniques to elucidate the relationships between all the variables, but try especially to explain how the variables effect Price. Another way to see the effects of several variables, simultaneously, is using partial correlations, discussed in my book. Many factors are measured.
Partial correlations hold some surprises.
You might also consider a multiple regression model, described in chapter 8. There are parallels between experimental control and statistical control, and there are important differences, too. Suppose you have three measured variables, a, b, and y. For example, for a database on graduate student performance we might collect GPA, GRE, number of publications, but not age. It might turn out that age has a strong effect on these other factors, but if we don't measure it, then this effect is latent, and age is called a latent variable.