Quantitative Analysis and IBM® SPSS® Statistics: A Guide for Business and Finance 1st ed. 2016 [Hardback]

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, Bri

tish 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.

1 Getting Started with SPSS 1.1 Creation of an SPSS data file 1.1.1 The SPSS Data Editor 1.1.2 Entering the data 1.1.3 Saving the data file 1.2 Descriptive statistics 1.2.1 Some commonly used statistics 1.2.2 Levels of measurement 1.2.3 Descriptive statistics in SPSS 1.2.4 A discussion of results 1.3 Creation of a chart 1.4 Basic editing of a chart and saving it in a file  2 Graphics and Introductory Statistical Analysis of Data 2.1 The boxplot 2.2 The histogram 2.3 The spread-level plot 2.4 Bar charts 2.5 Pie charts 2.6 Pareto charts 2.7 The drop-line chart 2.8 Line charts 2.9 Applying paneling to graphs 2.10 Data exploration via the EXPLORE routine  3 Frequencies and Crosstabulations 3.1 Univariate frequencies 3.2 Crosstabulation of two variables 3.3 Customising tables  4 Coding, Missing Values, Conditional and Arithmetic Operations 4.1 Coding of data 4.2 Arithmetic operations 4.3 Conditional transforms 4.4 The Autorecode facility 5 Hypothesis Tests Concerning Means 5.1 A rev

iew of hypothesis testing 5.2 The paired t test 5.3 The two sample t test 5.4 The one-way analysis of variance  6 Nonparametric Hypothesis Tests 6.1 The sign test 6.2 The Mann Whitney test 6.3 The Kruskal Wallis one way ANOVA 7 Bivariate Correlation and Regression 7.1 Bivariate correlation 7.2 Linear least squares regression for bivariate data 7.3 Bivariate correlation and regression in SPSS 8 Multivariate Regression 8.1 The assumptions underlying regression 8.2 Selecting the regression equation 8.3 Multivariate regression in SPSS 8.4 The Cochrane-Orcutt procedure for tackling autocorrelation 9 Logistic Regression 9.1 Binary Logistic Regression 9.2 The logistic model in SPSS 9.3 A financial application of the logistic model 9.4 Multinomial Logistic Regression