Titlebook: Statistical Data Analysis Using SAS; Intermediate Statist Mervyn G. Marasinghe,Kenneth J. Koehler Textbook 2018Latest edition Springer Inte

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书目名称Statistical Data Analysis Using SAS影响因子(影响力)




书目名称Statistical Data Analysis Using SAS影响因子(影响力)学科排名




书目名称Statistical Data Analysis Using SAS网络公开度




书目名称Statistical Data Analysis Using SAS网络公开度学科排名




书目名称Statistical Data Analysis Using SAS被引频次




书目名称Statistical Data Analysis Using SAS被引频次学科排名




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书目名称Statistical Data Analysis Using SAS年度引用学科排名




书目名称Statistical Data Analysis Using SAS读者反馈




书目名称Statistical Data Analysis Using SAS读者反馈学科排名

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Mitigate

Introduction to the SAS Language,r writing elementary SAS statements. Details of the data step are covered including the creation of a SAS data set using external data and examples of important data step programming statements. The INPUT statement and the operation of the SAS data step are presented in detail. The SAS procedure ste

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Introduction to SAS Graphics,OT, SGPANEL, and SGSCATTER are discussed along with key statements available for producing many useful statistical graphics applications. Other SAS procedures such as UNIVARIATE, TTEST, and GLM that produce template-based graphics are also used in various illustrations.

Judicious

Statistical Analysis of Regression Models,anced topics such as residual analysis and multicollinearity. The REG procedure is used to compute basic fit statistics, diagnostic case statistics, as well as produce associated template-based graphical tools for fitting both simple and multiple linear regression models. Discussion on REG and GLMSE

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Tremor

Analysis of Variance: Random and Mixed Effects Models, two-way crossed and nested random effects models, and two-way mixed effects models. Applications include a variety of experiments with random effects including randomized blocks, nested factorials, and split-plot type experiments. Estimation and statistical inference associated with classical metho

Gossamer

Beyond Regression and Analysis of Variance,. Generalized linear models, a special type of non-linear models, are introduced and illustrated with applications to logistic and Poisson regression models using LOGISTIC and GENMOD procedures. A separate section covers methods to handle overdispersion, including negative binomial models. The final

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