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151014s2016 gw | s |||| 0|eng d |
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|a 9783319206004
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|a 10.1007/978-3-319-20600-4
|2 doi
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|a Sistema de Bibliotecas del Tecnológico de Costa Rica
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|a Cleophas, Ton J.
|e author.
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|a SPSS for Starters and 2nd Levelers /
|c by Ton J. Cleophas, Aeilko H. Zwinderman.
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|a 2nd ed. 2016.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
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|a XXV, 375 p. 148 illus., 30 illus. in color. :
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a Preface -- 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 data 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.
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|a Medicine.
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|a Application software.
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|a Biometrics (Biology).
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|a Statistics .
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|a Biomedicine, general.
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|a Computer Applications.
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|a Biometrics.
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|a Statistical Theory and Methods.
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|a Statistics and Computing/Statistics Programs.
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|a Zwinderman, Aeilko H.
|e author.
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|a SpringerLink (Online service)
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|t Springer eBooks
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