• Produktbild: SPSS for Starters and 2nd Levelers
  • Produktbild: SPSS for Starters and 2nd Levelers

SPSS for Starters and 2nd Levelers

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Lieferung nach Hause

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.10.2016

Abbildungen

XXV, 148 illus., 30 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

375

Maße (L/B/H)

23,5/15,5/2,2 cm

Gewicht

610 g

Auflage

Second Edition 2016

Sprache

Englisch

ISBN

978-3-319-34250-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.10.2016

Abbildungen

XXV, 148 illus., 30 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

375

Maße (L/B/H)

23,5/15,5/2,2 cm

Gewicht

610 g

Auflage

Second Edition 2016

Sprache

Englisch

ISBN

978-3-319-34250-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: [email protected]

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  • Produktbild: SPSS for Starters and 2nd Levelers
  • Produktbild: SPSS for Starters and 2nd Levelers
  •                                       PrefaceChapter 1:   Introduction            Part 1 Continuous outcome data Chapter 2:   One sample continuous data       Chapter 3:   Paired continuous outcome data normality assumed     Chapter 4:   Paired continuous outcome data nonnormality accounted        Chapter 5:   Paired continuous outcome data with predictors Chapter 6:   Unpaired continuous outcome data normality assumed Chapter 7:   Unpaired continuous outcome data nonnormality accounted Chapter 8:   Linear regression for continuous outcome data             Chapter 9:   Recoding for categorical predictor data            Chapter 10: Repeated-measures-analysis of variance normality assumed                            Chapter 11: Repeated-measures-analysis of variance nonnormality accounted                              Chapter 12: Doubly-repeated-measures-analysis of variance Chapter 13: Multilevel modeling with mixed linear models   Chapter 14: Random multilevel modeling with generalized mixed linear models Chapter 15: One-way-analysis of variance normality assumed                                Chapter 16: One-way-analysis of variance nonnormality accounted                                   Chapter 17: Trend tests of continuous outcome data                                               Chapter 18: Multistage regression                                                     Chapter 19: Multivariate analysis with path statistics                                   Chapter 20: Multivariate analysis of variance                                               Chapter 21: Average-rank-testing for multiple outcome variables and categorical predictors Chapter 22: Missing data imputation                                                                                               Chapter 23: Meta-regression   Chapter 24: Poisson regression including a weight variable (time of observation) for rates  Chapter 25: Confounding Chapter 26: Interaction                                   Chapter 27: Curvilinear analysis                                                        Chapter 28: Loess and spline modeling for nonlinear data, where curvilinear models lack fit  Chapter 29: Monte Carlo analysis, the easy alternative for continuous outcome data         Chapter 30: Artificial intelligence as a distribution free alternative for nonlinear data        Chapter 31: Robust tests for data with large outliers                                                           Chapter 32: Nonnegative outcome data using the gamma distribution Chapter 33: Nonnegative outcome data with a big spike at zero using the Tweedie distribution Chapter 34: Polynomial analysis for continuous outcome data with a sinusoidal pattern   Chapter 35: Validating quantitative diagnostic tests                                    Chapter 36: Reliability assessment of quantitative diagnostic tests              Part 2 Binary outcome data Chapter 37: One sample binary data              Chapter 38: Unpaired binary data                                                                 Chapter 39: Binary logistic regression with a binary predictor                                             Chapter 40: Binary logistic regression with categorical predictors  Chapter 41: Binary logistic regression with a continuous predictor                         Chapter 42: Trend tests of binary data                                             Chapter 43: Paired binary outcome data without predictors             Chapter 44: Paired binary outcome data with predictors Chapter 45: Repeated measures binary data              Chapter 46: Multinomial logistic regression for outcome categories                                    Chapter 47: Multinomial logistic regression with random intercepts for both categorical                    outcome and predictor data.Chapter 48: Comparing the performance of diagnostic tests                                               Chapter 49: Poisson regression for binary outcome data                                                     Chapter 50: Loglinear models for the exploration of multidimensional contingency tables Chapter 51: Probit regression for binary outcome data reported as response rates   Chapter 52: Monte Carlo analysis, the easy alternative for binary outcomes                       Chapter 53: Validating qualitative diagnostic tests                           Chapter 54: Reliability assessment of qualitative diagnostic tests    Part 3 Survival and longitudinal data Chapter 55: Log rank tests                                                                Chapter 56: Cox regression Chapter 57: Cox regression with time-dependent variables              Chapter 58: Segmented Cox regression                                            Chapter 59: Assessing seasonality                                                     Chapter 60: Probability assessment of survival with interval censored data analysis Index