000			01115nam a22001937a 4500
999			_c1839 _d1839
005			20220325111740.0
008			220325b ||||| |||| 00| 0 eng d
020			_a9781506302768
082			_a519.536 _bOSB
100			_aOsborne, Jason W. _94750
245			_aRegression and linear modeling: best practices and modern methods
260			_bSage Publications, Inc. _aCalifornia _c2017
300			_axxv, 457 p.
365			_aUSD _b100.00
520			_aIn 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. The author 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.
650			_aRegression analysis _92357
650			_aLinear models (Statistics) _96115
942			_2ddc _cBK