<電子ブック>
Advanced Statistics for the Behavioral Sciences : A Computational Approach with R / by Jonathon D. Brown
版 | 1st ed. 2018. |
---|---|
出版者 | Cham : Springer International Publishing : Imprint: Springer |
出版年 | 2018 |
本文言語 | 英語 |
大きさ | XXI, 526 p. 244 illus., 207 illus. in color : online resource |
著者標目 | *Brown, Jonathon D author SpringerLink (Online service) |
件 名 | LCSH:Social sciences -- Statistical methods
全ての件名で検索
LCSH:Statistics LCSH:Psychometrics LCSH:Psychology FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistical Theory and Methods FREE:Psychometrics FREE:Behavioral Sciences and Psychology |
一般注記 | Linear Equations -- Least Squares Estimation -- Linear Regression -- Eigen Decomposition -- Singular Value Decomposition -- Generalized Least Squares Estimation -- Robust Regression -- Model Selection and Biased Estimation -- Cubic Splines and Additive Models -- Nonlinear Regression and Optimization -- Generalized Linear Models -- Survival Analysis -- Time Series Analysis -- Mixed Effects Models. This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics. Jonathon D. Brown is a social psychologist at the University of Washington. Since receiving his Ph.D. from UCLA in 1986, he has written three books, authored more than 75 journal articles and chapters, received a Presidential Young Investigator Award from the National Science Foundation, and been recognized as one of social psychology's most frequently-cited authors. HTTP:URL=https://doi.org/10.1007/978-3-319-93549-2 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
|
Springer eBooks | 9783319935492 |
|
電子リソース |
|
EB00226436 |
類似資料
この資料の利用統計
このページへのアクセス回数:1回
※2017年9月4日以降