Quantitative Analysis and IBM (R) SPSS (R) Statistics

This guide for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods.§§ IBM SPSS offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The Windows version of SPSS is built around routines that have been developed, tested, and widely used for more than 20 years. As such, SPSS is extensively used in industry, commerce, banking, local and national governments, and education. Just a small subset of users of the package include the major clearing banks, the BBC, British Gas, British Airways, British Telecom, the Consumer Association, Eurotunnel, Glaxo, ICI, London Underground, the NHS, Plessey, Shell, Unilever, and W.H. Smith and Son.§§ Although the emphasis in this guide is on applications of SPSS for Windows, there is a need for users to be aware of the statistical assumptions and rationales underpinning correct and meaningful application of the techniques available in the package; therefore, such assumptions are discussed, and methods of assessing their validity are described. Also presented is the logic underlying the computation of the more commonly used test statistics in the area of hypothesis testing. Mathematical background is kept to a minimum.