SAS System for regressionMaterial type: TextPublication details: Cary, N.C. : SAS Institute, 2000Edition: 3rd edDescription: viii, 236 pages : illustrationsISBN: 9781580257251 ; 1580257259 ; 9781580257251; 0471416649 9780471416647Subject(s): SAS (Computer file) | Regression analysisDDC classification: 519.536 Online resources: Click here to access online | Click here to access online
|Item type||Current library||Collection||Call number||Status||Date due||Barcode||Item holds|
|Reference Books||Pampaimadu Reference||Reference||519.536 FRE (Browse shelf(Opens below))||Available||013394|
|Reference Books||Pampaimadu Reference||Reference||519.536 FRE (Browse shelf(Opens below))||Available||012909|
1. Regression concepts --
2. Using the REG procedures --
3. Observations --
4. Multicollinearity: Detection and remedial measures --
5. Curve fitting --
6. Special applications of linear models --
7. Nonlinear models --
8. Using SAS/INSIGHT software 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 include performing linear regression analyses using PROC REG and 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 SAS are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set.