Professional Sas Programmer S Pocket Reference
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Professional SAS Programmer's Pocket Reference
Author: Rick Aster
language: en
Publisher: Breakfast Communications Corporation
Release Date: 1998
Table of ContentsChapter 1 System overviewChapter 2 ProgrammingChapter 3 General rulesChapter 4 Global statementsChapter 5 Data step statementsChapter 6 Proc step statementsChapter 7 ExpressionsChapter 8 Macro LanguageChapter 9 System optionsChapter 10 FilesChapter 11 Descriptive statisticsChapter 12 Display managerChapter 13 InformatsChapter 14 FormatsChapter 15 FunctionsChapter 16 CALL routinesChapter 17 Base SAS procsChapter 18 Products
SAS System for Regression
SAS® System for Regression Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include performing linear regression analyses using PROC REG diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. New for this edition The Third Edition includes revisions, updated material, and new material. You’ll find new information on using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
Data Preparation for Analytics Using SAS
Text addresses such tasks as: viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, and using SAS procedures for scoring.