01 / Reading Guide
Reading Guide
Who this book is for
For business analytics students who have completed a serious statistics sequence and want to evaluate models rather than only operate software. The author targets advanced undergraduates. This is a demanding continuation after an introductory business statistics book, with examples drawn from a wider range of applications than business alone.
Prerequisites
Know probability, mathematical statistics, linear regression, linear algebra and multivariable calculus. Be able to read and write simple R functions.
What it covers
Regression extensions sit beside resampling, latent structure, graphical causal models and dependent data. The contents provide routes into smoothing, generalized models, trees and time series. Select chapters around an analytical question; the text is broader than a forecasting manual or a survey of spreadsheet procedures.
How to use it
Fit a simple reference model first, reserve observations for evaluation and explain why an extension might help. Reproduce an R example, then change the data or assumptions and write a short report on the difference. Keep prediction performance separate from claims about causation. Review the regression prerequisites whenever a derivation becomes opaque.
Edition and sources
The author page supplies the complete draft, data and R files. It describes a forthcoming book, so no published Cambridge edition, publication year or ISBN is assigned here. Free reading does not grant redistribution rights. The Spring 2025 course gives related preparation and exercises; no fixed draft version is claimed.
