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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).

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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).

        Utilizes the popular and accessible IBM SPSS Statistics software package to teach data analysis for business and finance in a step-by-step approach
        A comprehensive, in-depth guide?especially relative to the competition
        Explains the statistical assumptions and rationales underpinning application of the IBM SPSS for Statistics package, instead of simply presenting techniques 
        More than 100 color graphs, screenshots, and figures
        Includes directed download of the software, IBM SPSS Statistics 24 [current version]

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).

“This is an excellent book for learning SPSS and a long awaited addition for teaching statistics in business and finance studies. The emphasis is on the effective use of SPSS and on correctly applying and interpreting results. A wonderful guide I have found so far.”

- Dr Yacine Belghitar - Cranfield University

“Dr Aljandali's book fills an important gap in the area of applied economics by making econometric concepts easier to grasp and apply using SPSS software, which makes it an invaluable handbook for students, researchers and practitioners.”

- Dr Mete Feridun - University of Cambridge

정보제공 :

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

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