Multivariate Methods and Forecasting with IBM® SPSS® Statistics

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 suchas 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 guideespecially 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, screen shots, and figures Includes directed download of the software, IBM SPSS Statistics 24 [current version] Includes supplementary material: sn.pub/extras

Autorentext

Abdulkader Aljandali, Ph.D., is a Senior Lecturer in Quantitative Finance and Business Forecasting at Regent's University London. He acts as a visiting professor at overseas institutions in Canada, France, and Morocco.

Inhalt
1 Multivariate Regression.- 2 Other Useful Topics in Regression.- 3 The Box-Jenkins Methodology.- 4 Exponential Smoothing and Naïve Models.- 5 Factor Analysis.- 6 Discriminant Analysis.- 7 Multidimensional Scaling.- 8 Hierarchical Log-Linear Analysis.- 9 Testing for Independence.- 10 Testing for Differences Between Groups.- 11 Current and Constant Prices.