Description
Linear Models with R (2nd Ed.)
Chapman & Hall/CRC Texts in Statistical Science Series
Author: Faraway Julian J.
Language: EnglishSubject for Linear Models with R:
Keywords
FALSE FALSE FALSE; FALSE FALSE; Flexibility Of Linear Models; FALSE FALSE FALSE FALSE FALSE; Essential Data Analysis Topics; Pa Rti; Block Designs; Radical Prostatectomy; Qr Decomposition; Box Cox Method; Applications Of Prediction And Explanation; Pt Sd; Practice Of Linear Modeling; Non-constant Variance; Factorial Models; NA NA NA; Ggplot2 Graphics Package; NA NA; Elementary Notions Of Causality; Nonconstant Variance; Hands-On Way To Learning Data Analysis; Vice Versa; Residual Standard Error; Missing Values; Latin Square; Dummy Variables; Prediction Interval; Multiple Imputation; Permutation Test; Ra Te; Box Cox Transformation Method; Ridge Regression; Missing Data; Likelihood Ratio Testing Approach; B1 B10 B2 B3 B4
286 p. · 15.6x23.4 cm
Description
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A Hands-On Way to Learning Data Analysis
Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
New to the Second Edition
- Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality
- Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates
- Extensive use of the ggplot2 graphics package in addition to base graphics
Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
Introduction. Estimation. Inference. Prediction. Explanation. Diagnostics. Problems with the Predictors. Problems with the Error. Transformation. Model Selection. Shrinkage Methods. Insurance Redlining—A Complete Example. Missing Data. Categorical Predictors. One Factor Models. Models with Several Factors. Experiments with Blocks. Appendix: About R. Bibliography. Index.
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