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Accounting, Finance and Economics Department Regent’s University London, UK
Textbook:Quantitative Analysis and IBM® SPSS® Statistics: A Guide for Business and Finance
Author(s): Abdulkader Aljandali
Course description:
This course aims to provide a gentle introduction to the IBM SPSS Statistics software for students starting out with the package, although it is recognized that the latter group would probably be familiar with the content presented here. A second course text building on this material will be beneficial to professionals working in the areas of practical business forecasting or market research data analysis. This coursebook would doubtlessly be more sympathetic to the readership than the manuals supplied by IBM SPSS Inc.
A lot of information can be gleaned about the characteristics of collected data by graphical means, for example, many statistical routines require data to be normally distributed. The first chapter of Part II expands on the graphics facilities in IBM SPSS Statistics. Similarly, frequency tables and cross-tabulations of variables assist in detecting data characteristics, and these are the subject matter of Chap. 3. Chapter
4 discusses the coding of data entry into a computer package. In many data-gathering exercises, there are missing values. IBM SPSS Statistics offers a very simple procedure for declaring missing values and, more generally, for labelling individual
variables and their values. Sometimes, variables have to be transformed into other variables, e.g. the conversion of one currency into another. These features of IBM SPSS Statistics conclude Part II.
Part III introduces and describes hypothesis tests. After a review of hypothesis testing, major parametric (Chap. 5) and nonparametric methods (Chap. 6) are described and illustrated by application. Parametric methods make more rigid assumptions about the distributional form of the gathered data than do nonparametric methods. However, it must be recognised that parametric methods are more powerful when the assumptions underlying them are met.
Part IV introduces elementary forecasting methods. Two-variable regression and correlation are illustrated in Chap. 7, and the assumptions underlying the regression method are stressed. Many of these assumptions may be assessed graphically by any methods previously described in Part II. Chapter 8 describes and illustrates two methods of time series analysis—seasonal decomposition and one-parameter exponential smoothing. The practical utility of both time series methods is discussed.
Part V comprises a chapter that presents other features of IBM SPSS Statistics that are likely to be useful, once the user is familiar with the basics of the package. The user is encouraged to access the IBM SPSS Statistics Help system. This part also introduces primary and secondary data in addition to various sources that a student in Business, Finance or Marketing course might need as part of their curriculum learning.
Quantitative Analysis and IBM® SPSS® Statistics_ A Guide for Busin.pdf
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