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1. Linear models with R [2015]
 Faraway, Julian James, author.
 Second edition.  Boca Raton, FL : CRC Press/Taylor & Francis Group, [2015]
 Description
 Book — xii, 274 pages : illustrations ; 25 cm.
 Summary

 Introduction Before You Start Initial Data Analysis When to Use Linear Modeling History Estimation Linear Model Matrix Representation Estimating b Least Squares Estimation Examples of Calculating b Example QR Decomposition GaussMarkov Theorem Goodness of Fit Identifiability Orthogonality Inference Hypothesis Tests to Compare Models Testing Examples Permutation Tests Sampling Confidence Intervals for b Bootstrap Confidence Intervals Prediction Confidence Intervals for Predictions Predicting Body Fat Autoregression What Can Go Wrong with Predictions? Explanation Simple Meaning Causality Designed Experiments Observational Data Matching Covariate Adjustment Qualitative Support for Causation Diagnostics Checking Error Assumptions Finding Unusual Observations Checking the Structure of the Model Discussion Problems with the Predictors Errors in the Predictors Changes of Scale Collinearity Problems with the Error Generalized Least Squares Weighted Least Squares Testing for Lack of Fit Robust Regression Transformation Transforming the Response Transforming the Predictors Broken Stick Regression Polynomials Splines Additive Models More Complex Models Model Selection Hierarchical Models TestingBased Procedures CriterionBased Procedures Summary Shrinkage Methods Principal Components Partial Least Squares Ridge Regression Lasso Insurance RedliningA Complete Example Ecological Correlation Initial Data Analysis Full Model and Diagnostics Sensitivity Analysis Discussion Missing Data Types of Missing Data Deletion Single Imputation Multiple Imputation Categorical Predictors A TwoLevel Factor Factors and Quantitative Predictors Interpretation with Interaction Terms Factors with More than Two Levels Alternative Codings of Qualitative Predictors One Factor Models The Model An Example Diagnostics Pairwise Comparisons False Discovery Rate Models with Several Factors Two Factors with No Replication Two Factors with Replication Two Factors with an Interaction Larger Factorial Experiments Experiments with Blocks Randomized Block Design Latin Squares Balanced Incomplete Block Design Appendix: About R Bibliography Index.
 (source: Nielsen Book Data)
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 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

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QA279 .F37 2015  Unknown On reserve at Li and Ma Science Library 2hour loan 
STATS20301
 Course
 STATS20301  Introduction to Regression Models and Analysis of Variance
 Instructor(s)
 Siegmund, David
2. Statistical models : theory and practice [2009]
 Freedman, David, 19382008.
 Rev. ed.  Cambridge [U.K.] ; New York : Cambridge University Press, 2009.
 Description
 Book — xiv, 442 p. : ill. ; 25 cm.
 Summary

 1. Observational studies and experiments
 2. The regression line
 3. Matrix algebra
 4. Multiple regression
 5. Multiple regression: special topics
 6. Path models
 7. Maximum likelihood
 8. The bootstrap
 9. Simultaneous equations
 10. Issues in statistical modeling.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online

 Cambridge Core Access limited to one user.
 Google Books (Full view)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA279 .F74 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
QA279 .F74 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
QA279 .F74 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
STATS20301
 Course
 STATS20301  Introduction to Regression Models and Analysis of Variance
 Instructor(s)
 Siegmund, David