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Author: Michael N. Mitchell
Paperback: 558 Pages
- Publisher: Stata Press; 1 Edition (April 19, 2012)
- Language: English
- 10 ISBN: 1,597,181,072
- ISBN 13: 978-1597181075
Michael Mitchell apos Interpreting and Visualizing Regression Models the Using Stata IS A the Clear treatment of How to Carefully Present Results from Model-Fitting in A Wide Variety of Settings. It IS A Boon to the anyone WHO has to Present at The Tangible meaning of A Complex Model in A the Clear fashion, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the practical meaning of interactions in nonlinear models such as logistic regression. The techniques presented in Mitchell's book make answering those questions easy. The overarching theme of the book is that graphs make interpreting even the most complicated models containing interaction terms, categorical variables, and other intricacies straightforward.
A using A the DataSet based ON at The General Social Survey, Mitchell Soho starts the with A Basic Linear Regression the with A SINGLE work of the Independent variable and the then illustrates How to Tabulate and Graph Predicted values. Mitchell focuses ON Stata apos margins and marginsplot Commands, Which Play A Central Role in at The Book and Which Greatly Simplify at The Calculation and Presentation of Results from Regression Models. the in Particular, through use of at The marginsplot Command, Mitchell Shows How you CAN Graphically the Visualize Every Model presented in at The Book. Gaining Insight INTO Results IS much Easier the when you CAN View Them in a graph rather than in a mundane table of results.
Mitchell then proceeds to more-complicated models where the effects of the independent variables are nonlinear. After discussing how to detect nonlinear effects, he presents examples using both standard polynomial terms (squares and cubes of variables) as well as fractional polynomial models, where independent Powers CAN BE raised to the Variables like -1 1/2 or the In All Cases, the uses at The Mitchell Again. marginsplot Command to Illustrate at The Changing Effect that has ON AN work of the Independent variable at The dependent variable Piecewise-Linear Models are presented AS Well;. THESE are Linear Models in Which at The Slope or Intercept IS allowed to Change DEPENDING ON at The the Range of AN work of the Independent variable Mitchell Also the uses at The. Contrast Command the when discussing categorical the Variables; AS at The name Suggests, the this Command android.permission you to Easily Contrast Predictions Made for Various Levels of the categorical variable.
Terms CAN BE Tricky to Interaction the Interpret, But Mitchell Shows How Graphs Produced by marginsplot Greatly Clarify Results. Individual Chapters are Devoted to Two- and Three-Way Interactions containing All the Continuous or All categorical and the include the Variables & PHARMACY MANY examples. Raw Regression Output Including Interactions CAN the Continuous and categorical the Variables of BE to the Interpret nigh Impossible, But Again Mitchell Makes A SNAP through the this at The judicious use of margins and marginsplot Commands in Subsequent Chapters.
The first two-thirds of the book is devoted to cross-sectional data, while the final third considers longitudinal data and complex survey data. A significant difference between this book and most others on regression models is that Mitchell spends quite some time on fitting and visualizing discontinuous models-models where the outcome can change value suddenly at thresholds. Such models are natural in settings such as education and policy evaluation, where graduation or policy changes can make sudden changes in income or revenue.
This book is a worthwhile addition to the library of anyone involved in statistical consulting, teaching, or collaborative applied statistical environments. Graphs greatly aid the interpretation of regression models, and Mitchell's book shows you how.
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