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Statistical Data Analysis Using Your Personal Computer

by Ira H. Bernstein

Learning statistical analysis through simulated data

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Paperback New English

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AlbakiReads Editorial

Learning statistical analysis through simulated data

Ira H. Bernstein’s guide connects statistical concepts with the output produced by SPSS, SAS, and Microsoft Excel. It begins with research basics and software guidance, then uses simulated data to demonstrate univariate, bivariate, and multivariate analysis. Topics include correlation, exploratory and confirmatory factor analysis, multidimensional scaling, regression, classification, discriminant analysis, and MANOVA. The chapter problems provide opportunities to practise the procedures, while step-by-step directions and hints address practical tasks such as moving data between programs. The book also points to a website with SAS and SPSS programs and sample data, making its structure a blend of statistical explanation and hands-on software instruction.

Good fit for readers who enjoy

  • People learning to interpret statistical software output.
  • Students or practitioners seeking worked examples across common statistical methods.
  • Users of SPSS, SAS, or Excel who want guided practice with simulated data.

Themes

Statistical softwareData simulationInterpreting statistical outputApplied statistical methods

Based on publisher information and book metadata.

Book overview

What should you see when you′re analyzing real data using one of the major statistical packages, such as SPSS, SAS or Microsoft Excel? This book will show you, and will walk you through the output from a variety of statistical outcomes, such as data reflecting a single common factor. Through the use of actual demonstrations, the authors supply readers with the computer programs necessary to simulate data sets with the statistical properties (usually multivariate) that are often assumed of real data. The reader is then shown how to analyze these data sets and how to interpret the results. The book begins with a general introduction to doing research and tips for using the three statistical packages. The authors next explore how to create data structures and perform univariate, bivariate, and multivariate simulations. They then show how to use the simulations to understand common statistical algorithms and their outputs when doing a basic correlation analysis, exploratory factor analysis, confirmatory factor analysis, multidimensional scaling, multiple regression, discriminate analysis, classification analysis and MANOVA. Throughout the book, the authors provide the reader with helpful guides, such as: *Hint boxes to give readers tips for executing particular techniques using the statistical software packages. *Steps that show each stage of a procedure, such as importing an Excel file into SAS. *Problems end each chapter so the reader can practice the techniques described. *Web Site with the SAS and SPSS programs and sample data.
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