目次

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 residuals1.2 Selecting the regression equation 1.3 Multivariate regression in IBM SPSS Statistics 1.4 The Cochrane-Orcutt procedure2 Further Regression Models2.1 Logistic regression    2.1.1 Logistic regression in IBM SPSS Statistics    2.1.2 Further comments about logistic regression2.2 Multinomial logistic regression2.3 Dummy regression3 The Box-Jenkins Methodology3.1 The property of stationarity3.2 The ARIMA model3.3 Autocorrelation3.4 ARIMA models in IBM SPSS Statistics4 Factor Analysis4.1 The correlation matrix4.2 The terminology and logic of factor analysis4.3 Rotation and naming of factors4.4 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 models6.2 Methods of obtaining proximities6.3 Flying mileages in IBM SPSS Statistics6.4 Methods of computing proximities6.5 Weighted multidimensional scaling in IBM SPSS Statistics7 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 model7.4 Hierarchical model7.5 Backward elimination