Statistical Data Analysis Using Your Personal Computer
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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Hardware and Software Needs
Basic Appearance of SAS and SPSS
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analysis of variance ANOVA Array ASCII basic bres canonical correlation Chapter clusters coefficients column component compute compute compute cond Corr correlation matrix covariance covariance matrix create criterion data set Data=A database defined denote descriptive statistics deviates in cells distribution Drag effect eigenanalysis eigenvalues eigenvector end file end input program end loop Enter equation error term example factor analysis follows function graph INTERCEP ITEM3 latent variables logistic function loop i=1 maximum likelihood menu multivariate nobs normal distribution Note number of observations obtain option output parameter estimates population predictors present Proc ANOVA Proc Catmod Proc Corr Proc Factor Proc Means profiles random normal deviates rannor err remaining residual response sampling SAS Institute SAS TO SIMULATE scores spreadsheet SPSS equivalent SPSS TO SIMULATE sqrt standard deviation statement statistics structure sums of squares Twoc uniform random number values variance vector weights