Multivariate methods and forecasting with IBM SPSS Statistics
著者
書誌事項
Multivariate methods and forecasting with IBM SPSS Statistics
（Statistics and econometrics for finance / series editors, D. Ruppert ... [et al]）
Springer, c2017
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注記
Includes bibliographical references (p. 173) and index
内容説明・目次
内容説明
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naive techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).
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 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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