Handbook of regression analysis with applications in R:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2020
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagwörter: | |
| Abstract: | "Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors' thorough treatments of "classic" regression analysis, this book covers two important and more advanced topics of time-to-event survival data and longitudinal and clustered data. Further, methods that have become prominent in the last 15-30 years that are designed for analyses on often-large data sets and can take advantage of exibility in modeling were not covered, including smoothing, tree- based, and regularization methods, all of which are increasingly becoming part of the data analysis toolkit. Examples are drawn from a wide variety of application areas using real data sets and all of the R code is provided. The book will be of interest to data scientists as well as in regression analysis courses at the graduate and undergraduate level. Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables -- that is, the average value of the dependent variable when the independent variables are fixed. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning"-- |
| Beschreibung: | Aus dem Vorwort zur zweiten Auflage, S. xvii: "A final small change from the first edition to the second edition is the title, as it now includes the phrase 'with applications in R'." |
| Umfang: | xxii, 349 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9781119392378 1119392373 |
Internformat
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| 240 | 1 | 0 | |a Handbook of regression analysis |
| 245 | 1 | 0 | |a Handbook of regression analysis with applications in R |c Samprit Chatterjee (New York University, New York, USA), Jeffrey S. Simonoff (New York University, New York, USA) |
| 250 | |a Second edition | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2020 | |
| 300 | |a xxii, 349 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 500 | |a Aus dem Vorwort zur zweiten Auflage, S. xvii: "A final small change from the first edition to the second edition is the title, as it now includes the phrase 'with applications in R'." | ||
| 520 | 3 | |a "Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors' thorough treatments of "classic" regression analysis, this book covers two important and more advanced topics of time-to-event survival data and longitudinal and clustered data. Further, methods that have become prominent in the last 15-30 years that are designed for analyses on often-large data sets and can take advantage of exibility in modeling were not covered, including smoothing, tree- based, and regularization methods, all of which are increasingly becoming part of the data analysis toolkit. Examples are drawn from a wide variety of application areas using real data sets and all of the R code is provided. The book will be of interest to data scientists as well as in regression analysis courses at the graduate and undergraduate level. Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables -- that is, the average value of the dependent variable when the independent variables are fixed. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning"-- | |
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Datensatz im Suchindex
| _version_ | 1818987359167840256 |
|---|---|
| any_adam_object | |
| author | Chatterjee, Samprit 1938- Simonoff, Jeffrey S. 1955- |
| author_GND | (DE-588)172018978 (DE-588)17080867X |
| author_facet | Chatterjee, Samprit 1938- Simonoff, Jeffrey S. 1955- |
| author_role | aut aut |
| author_sort | Chatterjee, Samprit 1938- |
| author_variant | s c sc j s s js jss |
| building | Verbundindex |
| bvnumber | BV046946630 |
| classification_rvk | QH 234 |
| ctrlnum | (OCoLC)1143839175 (DE-599)BVBBV046946630 |
| discipline | Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV046946630 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:05:26Z |
| institution | BVB |
| isbn | 9781119392378 1119392373 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032355253 |
| oclc_num | 1143839175 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-188 |
| owner_facet | DE-19 DE-BY-UBM DE-188 |
| physical | xxii, 349 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Chatterjee, Samprit 1938- Verfasser (DE-588)172018978 aut Handbook of regression analysis Handbook of regression analysis with applications in R Samprit Chatterjee (New York University, New York, USA), Jeffrey S. Simonoff (New York University, New York, USA) Second edition Hoboken, NJ Wiley 2020 xxii, 349 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Aus dem Vorwort zur zweiten Auflage, S. xvii: "A final small change from the first edition to the second edition is the title, as it now includes the phrase 'with applications in R'." "Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors' thorough treatments of "classic" regression analysis, this book covers two important and more advanced topics of time-to-event survival data and longitudinal and clustered data. Further, methods that have become prominent in the last 15-30 years that are designed for analyses on often-large data sets and can take advantage of exibility in modeling were not covered, including smoothing, tree- based, and regularization methods, all of which are increasingly becoming part of the data analysis toolkit. Examples are drawn from a wide variety of application areas using real data sets and all of the R code is provided. The book will be of interest to data scientists as well as in regression analysis courses at the graduate and undergraduate level. Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables -- that is, the average value of the dependent variable when the independent variables are fixed. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning"-- R Programm (DE-588)4705956-4 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regression analysis / Handbooks, manuals, etc R (Computer program language) Regression analysis Handbooks and manuals Regressionsanalyse (DE-588)4129903-6 s R Programm (DE-588)4705956-4 s DE-604 Simonoff, Jeffrey S. 1955- Verfasser (DE-588)17080867X aut Erscheint auch als Online-Ausgabe, PDF 978-1-119-39247-7 Erscheint auch als Online-Ausgabe, EPUB 978-1-119-39248-4 |
| spellingShingle | Chatterjee, Samprit 1938- Simonoff, Jeffrey S. 1955- Handbook of regression analysis with applications in R R Programm (DE-588)4705956-4 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4129903-6 |
| title | Handbook of regression analysis with applications in R |
| title_alt | Handbook of regression analysis |
| title_auth | Handbook of regression analysis with applications in R |
| title_exact_search | Handbook of regression analysis with applications in R |
| title_full | Handbook of regression analysis with applications in R Samprit Chatterjee (New York University, New York, USA), Jeffrey S. Simonoff (New York University, New York, USA) |
| title_fullStr | Handbook of regression analysis with applications in R Samprit Chatterjee (New York University, New York, USA), Jeffrey S. Simonoff (New York University, New York, USA) |
| title_full_unstemmed | Handbook of regression analysis with applications in R Samprit Chatterjee (New York University, New York, USA), Jeffrey S. Simonoff (New York University, New York, USA) |
| title_short | Handbook of regression analysis with applications in R |
| title_sort | handbook of regression analysis with applications in r |
| topic | R Programm (DE-588)4705956-4 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | R Programm Regressionsanalyse |
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