Applied Regression Analysis: A Research Tool
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1998
|
| Ausgabe: | Second Edition |
| Schriftenreihe: | Springer Texts in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course. Applied Regression Analysis emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet |
| Beschreibung: | 1 Online-Ressource (XVIII, 660 p) |
| ISBN: | 9780387227535 9780387984544 |
| ISSN: | 1431-875X |
| DOI: | 10.1007/b98890 |
Internformat
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Datensatz im Suchindex
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| discipline | Mathematik |
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| edition | Second Edition |
| format | Electronic eBook |
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| isbn | 9780387227535 9780387984544 |
| issn | 1431-875X |
| language | English |
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| spelling | Rawlings, John O. Verfasser aut Applied Regression Analysis A Research Tool edited by John O. Rawlings, Sastry G. Pantula, David A. Dickey Second Edition New York, NY Springer New York 1998 1 Online-Ressource (XVIII, 660 p) txt rdacontent c rdamedia cr rdacarrier Springer Texts in Statistics 1431-875X Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course. Applied Regression Analysis emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet Statistics Mathematical statistics Statistical Theory and Methods Statistik Schätztheorie (DE-588)4121608-8 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s 1\p DE-604 Schätztheorie (DE-588)4121608-8 s 2\p DE-604 Pantula, Sastry G. Sonstige oth Dickey, David A. Sonstige oth https://doi.org/10.1007/b98890 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rawlings, John O. Applied Regression Analysis A Research Tool Statistics Mathematical statistics Statistical Theory and Methods Statistik Schätztheorie (DE-588)4121608-8 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4121608-8 (DE-588)4129903-6 |
| title | Applied Regression Analysis A Research Tool |
| title_auth | Applied Regression Analysis A Research Tool |
| title_exact_search | Applied Regression Analysis A Research Tool |
| title_full | Applied Regression Analysis A Research Tool edited by John O. Rawlings, Sastry G. Pantula, David A. Dickey |
| title_fullStr | Applied Regression Analysis A Research Tool edited by John O. Rawlings, Sastry G. Pantula, David A. Dickey |
| title_full_unstemmed | Applied Regression Analysis A Research Tool edited by John O. Rawlings, Sastry G. Pantula, David A. Dickey |
| title_short | Applied Regression Analysis |
| title_sort | applied regression analysis a research tool |
| title_sub | A Research Tool |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistik Schätztheorie (DE-588)4121608-8 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistik Schätztheorie Regressionsanalyse |
| url | https://doi.org/10.1007/b98890 |
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