Data analysis using regression and multilevel/hierarchical models / Andrew Gelman and Jennifer Hill.
Material type: TextSeries: Analytical methods for social researchPublication details: Cambridge : Cambridge University Press, 2007.Description: xxii, 625 p. : ill. 26 cmISBN:- 9780521867061 (hbk.) :
- 9780521686891 (pbk.) :
- 9780521686891 (pbk.)
- 0521867061
- 9780521867061
- 052168689X (pbk.)
- 519.536 GEL
- HA31.3 .G45 2006
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|
Long Loan | TUS: Midlands, Main Library Athlone General Lending | 519.536 GEL (Browse shelf(Opens below)) | 1 | Available | 00211185 |
Includes bibliographical references.
1.Why? -- 2.Concepts and methods from basic probability and statistics -- Part 1A: Single-level regression -- 3.Linear regression: the basics -- 4.Linear regression: before and after fitting the model -- 5.Logistic regression -- 6.Generalized linear models -- Part 1B: Working with regression inferences -- 7.Simulation of probability models and statistical inferences -- 8.Simulation for checking statistical procedures and model fits -- 9.Casual inference using regression on the treatment variable -- 10.Casual inference using mode advanced models -- Part 2.Multivlevel structures -- 12.Multilevel linear models: the basics -- 13.Multilevel linear models: varying slops, non-rest models and other complexities -- 14. Multilevel logistic regression -- 15.Multilevel generalized linear models - Part 2B: Fitting multilevel models -- 16.Multilevel modeling in Bugs and R: the basics -- 17.Fitting multilevel linear and generalized linear models in Bugs and R -- 18.Likelihood and Bayesian inference and computation 19.Debugging and speeding convergence -- Part 3: From data collection to model understanding to model checking -- 20.Sample size and power calculations -- 21.Understanding and summarizing the fitted models -- 22.Analysis of variance -- 23.Casual inference using multilelvel models -- 24.Model checking and comparison --25.Missing-data imputation.
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