Applied regression modeling
Material type: TextPublication details: Hoboken, NJ Wiley, 2012Edition: 2nd edDescription: xx, 325 p illISBN: 9781118097281 (hardback)Subject(s): Regression analysis | Statistics | Mathematics -Probability & Statistics-Regression AnalysisDDC classification: 519.536 Summary: "This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests)"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Kannur University Central Library Stack | Stack | 519.536 PAR/A (Browse shelf (Opens below)) | Available | 36840 |
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519.536 HIL/L Logistic regression models | 519.536 HOS/A Applied logistic regression | 519.536 OGO/A Adaptive tests of significance using permutations of residuals with R and SAS / | 519.536 PAR/A Applied regression modeling | 519.536 SCH/U Understanding regression analysis : | 519.5360285513 DEB/L Linear models and regression with R : an integrated approach | 519.538 FAR/L Linear models with R |
"This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests)"--
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