Applied statistics and multivariate data analysis for business and economics : a modern approach using SPSS, Stata, and Excel /

Applied statistics and multivariate data analysis for business and economics : a modern approach using SPSS, Stata, and Excel /

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Bibliographic Details
Author / Creator:	Cleff, Thomas, author.
Imprint:	Cham, Switzerland : Springer, [2019]
Description:	1 online resource
Language:	English
Subject:	Multivariate analysis. Multivariate analysis.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11930775

Hidden Bibliographic Details
ISBN:	303017767X 9783030177676 3030177661 9783030177669
Notes:	Includes bibliographical references and index. Online resource; title from digital title page (viewed on August 23, 2019).
Summary:	This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it demonstrates the methods of univariate, bivariate, and multivariate statistical analysis. The textbook covers a range of topics, from data collection and scaling to the presentation and simple univariate analysis of quantitative data, while also providing advanced analytical procedures for assessing multivariate relationships. Accordingly, it addresses all topics typically covered in university courses on statistics and advanced applied data analysis. In addition, it does not limit itself to presenting applied methods, but also discusses the related use of Excel, SPSS, and Stata.
Other form:	Print version: Cleff, Thomas. Applied statistics and multivariate data analysis for business and economics. Cham, Switzerland : Springer, [2019] 3030177661 9783030177669
Standard no.:	10.1007/978-3-030-17

Table of Contents:

Statistics and Empirical Research
From Disarray to Dataset
Univariate Data Analysis
Bivariate Association
Classical Measurement Theory.-Calculating Probability
Random Variables and Probability Distributions
Parameter Estimation
Hypothesis Testing
Regression Analysis
Time Series and Indices
Cluster Analysis
Factor Analysis.