By SAS Publishing
SAS/STAT software program offers vast statistical features with instruments for either really good and enterprise-wide analytical wishes. it could assist you research facts and make proficient judgements for study, engineering, production, clinical, and enterprise purposes. This user's advisor offers the newest, specified reference fabric for the techniques in SAS/STAT, together with research of variance, regression, specific info research, multivariate research, survival research, psychometric research, cluster research, nonparametric research, and survey information research. additionally incorporated are syntax and utilization info, examples, and a dialogue of using the Output supply method with the statistical software program. New chapters for SAS 9.1 describe software program for energy and pattern measurement computations, powerful regression, a number of imputation, crosstabulations, desk research, and logistic regression for survey information. Many different chapters were up to date with SAS 9.1 improvements. This name serves as a reference consultant for either amateur and specialist clients of SAS/STAT software program. This name is usually on hand loose on-line from SAS Publishing.
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Additional info for SAS/STAT 9.1 User's Guide, Volumes 1-7
Multivariate Tests . . . . . . . . 38 38 39 42 45 47 48 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 27 29 33 34 35 35 35 36 36 36 36 REFERENCES . . . . . . . . . . . . . . . . . 53 26 Chapter 2. Introduction to Regression Procedures Chapter 2 Introduction to Regression Procedures Overview This chapter reviews SAS/STAT software procedures that are used for regression analysis: CATMOD, GLM, LIFEREG, LOESS, LOGISTIC, NLIN, ORTHOREG, PLS, PROBIT, ROBUSTREG, REG, RSREG, and TRANSREG.
Response Surface Regression: The RSREG Procedure . . . . . . Partial Least Squares Regression: The PLS Procedure . . . . . . Regression for Ill-conditioned Data: The ORTHOREG Procedure . . . Local Regression: The LOESS Procedure . . . . . . . . . Robust Regression: The ROBUSTREG Procedure . . . . . . . Logistic Regression: The LOGISTIC Procedure . . . . . . . . Regression with Transformations: The TRANSREG Procedure . . . . Regression Using the GLM, CATMOD, LOGISTIC, PROBIT, and LIFEREG Procedures .
In comparing the results of two experiments on the same variables but with different ranges for the regressors, you should look at the standard error of prediction (root mean square error) rather than R2 . Whether a given R2 value is considered to be large or small depends on the context of the particular study. 98 to be small. 0 by including a large number of completely unrelated regressors in the equation. If the number of regressors is close to the sample size, R2 is very biased. In such cases, the adjusted R2 and related statistics discussed by Darlington (1968) are less misleading.
SAS/STAT 9.1 User's Guide, Volumes 1-7 by SAS Publishing