I'm looking for a single book that provides a deep, yet readable
introduction to applied data analysis for general readers.

I'm looking for coverage on things like understanding randomness, "natural
experiments", confounding, causality and correlation, data cleaning and
transforms, lagging, residuals, exploratory graphics, curve fitting,
descriptive stats.... Preferably with examples/case studies that illustrate
the art and craft of data analysis. No proofs or heavy math.

What have you got?

	[[alternative HTML version deleted]]

