Multivariate Methods and Forecasting with IBM® SPSS® Statistics

  • Abdulkader Aljandali

Part of the Statistics and Econometrics for Finance book series (SEFF)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Forecasting Models

    1. Front Matter
      Pages 1-1
    2. Abdulkader Aljandali
      Pages 3-25
    3. Abdulkader Aljandali
      Pages 27-57
    4. Abdulkader Aljandali
      Pages 59-79
    5. Abdulkader Aljandali
      Pages 81-93
  3. Multivariate Methods

    1. Front Matter
      Pages 95-95
    2. Abdulkader Aljandali
      Pages 97-106
    3. Abdulkader Aljandali
      Pages 107-116
    4. Abdulkader Aljandali
      Pages 117-133
    5. Abdulkader Aljandali
      Pages 135-149
  4. Research Methods

    1. Front Matter
      Pages 151-151
    2. Abdulkader Aljandali
      Pages 153-158
    3. Abdulkader Aljandali
      Pages 159-166
    4. Abdulkader Aljandali
      Pages 167-172
  5. Back Matter
    Pages 173-178

About this book

Introduction

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).

        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

Keywords

data analysis correlation forecast diagnostic univariate frequencies SPSS Mann-Whitney test Kruskal-Wallis test logistic regression multivariate regression big data

Authors and affiliations

  • Abdulkader Aljandali
    • 1
  1. 1.Accounting, Finance and Economics DepartmentRegent’s University LondonLondonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-56481-4
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-56480-7
  • Online ISBN 978-3-319-56481-4
  • Series Print ISSN 2199-093X
  • Series Online ISSN 2199-0948
  • About this book