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Multivariate Methods and Forecasting With IBM SPSS Statistics
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Multivariate Methods and Forecasting With IBM SPSS Statistics

Multivariate Methods and Forecasting With IBM SPSS Statistics

Abdulkader aljandali (author)

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178 pages, parution le 13/07/2017
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Résumé

Abdulkader Aljandali, Ph.D., is Senior Lecturer at Regent's University London. He currently leads the Business Forecasting and the Quantitative Finance module at Regent's in addition to acting as a Visiting Professor for various universities across the UK, Germany and Morocco. Dr Aljandali is an established member of the Higher Education Academy (HEA) and an active member of the British Accounting and Finance Association (BAFA).
1 Multivariate Regression
1.1 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 Models
2.1 Logistic regression
    2.1.1 Logistic regression in IBM SPSS Statistics
    2.1.2 Further comments about logistic regression
2.2 Multinomial logistic regression
2.3 Dummy regression

3 The Box-Jenkins Methodology
3.1 The property of stationarity
3.2 The ARIMA model
3.3 Autocorrelation
3.4 ARIMA models in IBM SPSS Statistics

4 Factor Analysis
4.1 The correlation matrix
4.2 The terminology and logic of factor analysis
4.3 Rotation and naming of factors
4.4 Factor scores in IBM SPSS Statistics

5 Discriminant Analysis
5.1 The Methodology of discriminant analysis
5.2 Discriminant analysis in IBM SPSS Statistics
5.3 Results of applying the SPSS discrimination procedure
6 Multidimensional Scaling
6.1 Multidimensional scaling models
6.2 Methods of obtaining proximities
6.3 Flying mileages in IBM SPSS Statistics
6.4 Methods of computing proximities
6.5 Weighted multidimensional scaling in IBM SPSS Statistics

7 Hierarchical Log-Linear Analysis
7.1 The logic and terminology of log-linear analysis
7.2 IBM SPSS Statistics commands for the saturated model
7.3 The independence model
7.4 Hierarchical model
7.5 Backward elimination
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 naïve techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).1st Edition 2017th editionIllustrationsHA32Social sciences - Statistical methods - Computer programs.1SwitzerlandCham9783319564814|9783319564821Abdulkader Aljandali.Statistics and Econometrics for Finance

Caractéristiques techniques

  PAPIER
Éditeur(s) Springer
Auteur(s) Abdulkader aljandali (author)
Parution 13/07/2017
Nb. de pages 178
Format 155 x 235
Poids 461g
EAN13 9783319564807

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