Regression & linear modeling : best practices and modern methods
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TextPublication details: Los Angeles Sage 2017Description: xxv, 457 p. illustrationsISBN: 9781506302768 (hardcover : alk. paper); 1506302769 (hardcover : alk. paper)Subject(s): Regression analysis | Linear models (Statistics)DDC classification: 519.536 Summary: In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
| Item type | Current library | Call number | Status | Date due | Barcode |
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BK
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Stack | 519.536 OSB/R (Browse shelf (Opens below)) | Available | 50666 |
In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
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