Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Normal view MARC view ISBD view

Quantitative Analysis and IBM® SPSS® Statistics [electronic resource] : A Guide for Business and Finance / by Abdulkader Aljandali.

By: Aljandali, Abdulkader [author.].
Contributor(s): SpringerLink (Online service).
Material type: TextTextSeries: Statistics and Econometrics for Finance: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XXI, 184 p. 143 illus., 119 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319455280.Subject(s): Statistics | Mathematical statistics | Big data | Business enterprises-Finance | Finance | Statistics for Business/Economics/Mathematical Finance/Insurance | Statistical Theory and Methods | Big Data/Analytics | Business Finance | Quantitative FinanceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 330.015195 Online resources: Click here to access online
Contents:
1 Getting Started with SPSS -- 2 Graphics and Introductory Statistical Analysis of Data -- 3 Frequencies and Crosstabulations -- 4 Coding, Missing Values, Conditional and Arithmetic Operations -- 5 Hypothesis Tests Concerning Means -- 6 Nonparametric Hypothesis Tests -- 7 Bivariate Correlation and Regression -- 8 Multivariate Regression -- 9 Logistic Regression.
In: Springer eBooksSummary: This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This 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 Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics 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, GSK, TfL, the NHS, Shell, Unilever, and W.H.S. Although the emphasis in this guide is on applications of IBM SPSS Statistics, 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. 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

1 Getting Started with SPSS -- 2 Graphics and Introductory Statistical Analysis of Data -- 3 Frequencies and Crosstabulations -- 4 Coding, Missing Values, Conditional and Arithmetic Operations -- 5 Hypothesis Tests Concerning Means -- 6 Nonparametric Hypothesis Tests -- 7 Bivariate Correlation and Regression -- 8 Multivariate Regression -- 9 Logistic Regression.

This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This 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 Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics 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, GSK, TfL, the NHS, Shell, Unilever, and W.H.S. Although the emphasis in this guide is on applications of IBM SPSS Statistics, 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. 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.

There are no comments for this item.

Log in to your account to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in


Visitor Counter