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]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.