000			02480nam a22003977a 4500
999			_c132139 _d132139
003			phtghnu
005			20240430151718.0
007			ta
008			201110s2021 cau 000 0 eng
010			_a 2020949998
020			_a9781526424174 _q(paperback)
040			_beng _erda _cHNU
042			_apcc
082			_223 _3GC _a519.536 M36 _b2021
100	1		_aMartin, Peter, _eauthor.
245	1	0	_aLinear regression: an introduction to statistical models / _cPeter Martin.
263			_a2203
264		1	_aThousand Oaks, California : _bSAGE Publications Inc., _c2021.
300			_a xxii, 178 pages : _billustrations ; _c 24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490			_a The SAGE quantitative research kit
504			_aIncludes bibliographical references and index.
505			_aWhat is a statistical model? Simple linear regression Assumptions and transformations Multiple linear regression: A model for multivariate relationships Multiple linear regression: Inference, assumptions, and standardization Where to go from here
520			_aPart of The SAGE Quantitative Research Kit, this text helps you make the crucial steps towards mastering multivariate analysis of social science data, introducing the fundamental linear and non-linear regression models used in quantitative research. Peter Martin covers both the theory and application of statistical models, and illustrates them with illuminating graphs, discussing: • Linear regression, including dummy variables and predictor transformations for curvilinear relationships • Binary, ordinal and multinomial logistic regression models for categorical data • Models for count data, including Poisson, negative binomial, and zero-inflated regression • Checking model assumptions and the dangers of overfitting
521			_aCoED _bBachelor of Secondary Education major in Mathematics
546			_aIn English
650			_aRegression analysis.
650			_aMathematical models.
650			_aQuantitative research.
906			_a0 _bibc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc _cBK _h500-599