Multivariate Methods and Forecasting with IBM (R) SPSS (R) Statistics

Multivariate Methods and Forecasting with IBM (R) SPSS (R) Statistics

Aljandali, Abdulkader

Springer International Publishing AG

08/2018

178

Mole

Inglês

9783319859224

15 a 20 dias

Descrição não disponível.
1 Multivariate Regression1.1 The assumption underlying regression 1.1.1 Multicollinearity 1.1.2 Homoscedasticity of residuals 1.1.3 Normality of residuals 1.1.4 Independence of residuals 1.2 Selecting the regression equation 1.3 Multivariate regression in IBM SPSS Statistics 1.4 The Cochrane-Orcutt procedure 2 Further Regression Models2.1 Logistic regression 2.1.1 Logistic regression in IBM SPSS Statistics 2.1.2 Further comments about logistic regression 2.2 Multinomial logistic regression2.3 Dummy regression 3 The Box-Jenkins Methodology3.1 The property of stationarity 3.2 The ARIMA model3.3 Autocorrelation3.4 ARIMA models in IBM SPSS Statistics 4 Factor Analysis4.1 The correlation matrix4.2 The terminology and logic of factor analysis4.3 Rotation and naming of factors 4.4 Factor scores in IBM SPSS Statistics 5 Discriminant Analysis5.1 The Methodology of discriminant analysis5.2 Discriminant analysis in IBM SPSS Statistics5.3 Results of applying the SPSS discrimination procedure6 Multidimensional Scaling6.1 Multidimensional scaling models 6.2 Methods of obtaining proximities6.3 Flying mileages in IBM SPSS Statistics6.4 Methods of computing proximities6.5 Weighted multidimensional scaling in IBM SPSS Statistics 7 Hierarchical Log-Linear Analysis7.1 The logic and terminology of log-linear analysis7.2 IBM SPSS Statistics commands for the saturated model7.3 The independence model 7.4 Hierarchical model7.5 Backward elimination
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