A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression.
The intervention for low - birth - weight children is described by Brooks - Gunn , Liaw , and Klebanov ( 1992 ) and Hill , Brooks - Gunn , and Waldfogel ( 2003 ) . Imbalance plots such as Figure 10.3 are commonly used ; see Hansen ...
Part I of the book presents a large selection of activities for introductory statistics courses and combines chapters such as, 'First week of class', with exercises to break the ice and get students talking; then 'Descriptive statistics' , ...
Rosenbaum , P. , and D.B. Rubin ( 1983a ) “ Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study With a Binary Outcome . ” Journal of the Royal Statistical Society , Series B 45 : 212-218 .
This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations.
Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics.
This book considers regression models that are appropriate when the dependent variable is censored, truncated, binary, ordinal, nominal, or count. I refer to these variables as categorical and limited dependent variables (hereafter CLDVs).
Johnson, N.L., Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions, 1, 2nd edn. New York, USA: Wiley. Johnson, N.L., Kotz, S. and Balakrishnan, N. (1995). Continuous Univariate Distributions, 2, 2nd edn.
For contingency tables, this is known as Pearson's residual: Observed√ Predicted . Res = Predicted − Pearson's residual tells us about overprediction and underprediction within each cell of the table. To summarize across groups, ...