It's a strong statement to declare something the "bible" of a particular subject, but this text comes close with statistics. It touches on almost all major topics in general linear modeling, and covers some topics in experimental design as well. This book was used in my graduate-level linear regression course, but this text would be perfectly suitable for a two-semester undergraduate course as well. Aimed at practitioners, statisticians, data scientists, and engineers, it's highly readable, with provided datasets for the exercises, and a rigorous treatment of the topics that doesn't wander too far into the abstract. The text even is supplemented with both an instructor and a student solutions manual, which makes this an excellent self-study book. It takes the time to discuss linear model diagnostics, remedial actions, and building the model for simple linear regression, multiple linear regression, and regression involving both quantitative and qualitative predictors. In particular, I like that it discusses extra sums of squares tests for whether or not several terms in the regression model can be dropped, discussing the issue with stepwise model selection (though not in the detail I'd prefer). It touches on experimental design and neural networks, but from the analysis perspective rather than the design perspective. Occasionally, derivations are done, such as the regression coefficients using the normal equations, but there aren't really many proofs. The text is meant to develop a statistical sense, and the ability to deal with data. I consider it a necessary read for anyone looking to become or currently working as a data scientist, as the nuances of seemingly simple general linear regression can become lost with the ease of packaged software and implementation.