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001			321934
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008			160111s2016 gw | s |||| 0|eng d
020			_a9783319206004 _9978-3-319-20600-4
035			_avtls000421668
039		9	_y201601111002 _zstaff
050		4	_aR-RZ
100	1		_aCleophas, Ton J, _eautor. _9308040
245	1	0	_aSpss for starters and 2nd levelers / _cTon J. Cleophas, Aeilko H. Zwinderman.
250			_a2nd ed. 2016.
264		1	_aCham : _bSpringer International Publishing : _bSpringer, _c2016.
300			_axxv, 375 páginas : _b148 ilustraciones, 30 ilustraciones en color.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aPreface.- Introduction -- I Continuous outcome data -- One sample continuous data --  Paired continuous outcome data normality assumed -- Paired continuous outcome data nonnormality accounted -- Paired continuous outcome data with predictors -- Unpaired continuous outcome data normality assumed -- Unpaired continuous outcome data nonnormality accounted -- Linear regression for continuous outcome data -- Recoding for categorical predictor data -- Repeated-measures-analysis of variance normality assumed.- Repeated-measures-analysis of variance nonnormality accounted --  Doubly-repeated-measures-analysis of variance -- Multilevel modeling with mixed linear models. Random multilevel modeling with generalized mixed linear models -- One-way-analysis of variance normality assumed -- One-way-analysis of variance nonnormality accounted -- Trend tests of continuous outcome data -- Multistage regression -- Multivariate analysis with path statistics -- Multivariate analysis of variance.- Average-rank-testing for multiple outcome variables and categorical predictors -- Missing data imputation -- Meta-regression -- Poisson regression including a weight variable (time of observation) for rates -- Confounding -- Interaction -- Curvilinear analysis -- Loess and spline modeling for nonlinear data, where curvilinear models lack fit -- Monte Carlo analysis, the easy alternative for continuous outcome data -- Artificial intelligence as a distribution free alternative for nonlinear data -- Robust tests for d ata with large outliers -- Nonnegative outcome data using the gamma distribution -- Nonnegative outcome data with a big spike at zero using the Tweedie distribution -- Polynomial analysis for continuous outcome data with a sinusoidal pattern -- Validating quantitative diagnostic tests -- Reliability assessment of quantitative diagnostic tests -- II Binary outcome data -- One sample binary data -- Unpaired binary data -- Binary logistic regression with a binary predictor --  Binary logistic regression with categorical predictors -- Binary logistic regression with a continuous predictor -- Trend tests of binary data -- Paired binary outcome data without predictors -- Paired binary outcome data with predictors -- Repeated measures binary data -- Multinomial logistic regression for outcome categories -- Multinomial logistic regression with random intercepts for both categorical outcome and predictor data -- Comparing the performance of diagnostic tests -- Poisson regression for binary outcome data -- Loglinear models for the exploration of multidimensional contingency tables -- Probit regression for binary outcome data reported as response rates -- Monte Carlo analysis, the easy alternative for binary outcomes -- Validating qualitative diagnostic tests -- Reliability assessment of qualitative diagnostic tests. III Survival and longitudinal data -- Log rank tests -- Cox regression -- Cox regression with time-dependent variables -- Segmented Cox regression -- Assessing seasonality -- Probability assessment of survival with interval censored data analysis -- Index.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aZwinderman, Aeilko H, _eautor. _9308041
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783319205991
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-20600-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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