Well, so you want to be a SAS Modeler. Or atleast get a job as a junior one , and then learn on the job (we all did). Here are some SAS Procs you need to brush up on-

**1) Proc Reg – **Continuous Regression.

**2) Proc Logistic –**Logistic Regression.

**3) Proc Probit –**Categorical regressors also included in this.

**4) Proc GLM –**General Linear Models based on OLS. PROC GLM handles models relating one or several continuous dependent variables to one or several independent variables. The independent variables may be either *classification* variables, which divide the observations into discrete groups, or *continuous* variables.Proc GLM is the preferred procedure for doing univariate analysis of variance , multivariate analysis of variance , and most types of regression. :Note there is a Proc Anova also.

**5) Proc Mixed –**The PROC MIXED was specifically designed to fit mixed effect models. It can model random and mixed effect data.PROC MIXED has three options for the method of estimation. They are: ML (Maximum Likelihood), REML (Restricted or Residual maximum likelihood, which is the default method) and MIVQUE0 (Minimum Variance Quadratic Unbiased Estimation). ML and REML are based on a maximum likelihood estimation approach. They require the assumption that the distribution of the dependent variable (error term and the random effects) is normal. ML is just the regular maximum likelihood method,that is, the parameter estimates that it produces are such values of the model parameters that maximize the likelihood function. REML method is a variant of maximum likelihood estimation; REML estimators are obtained not from maximizing the whole likelihood function, but only that part that is invariant to the fixed effects part of the linear model. In other words, if y = X*b* + Zu + e, where X*b* is the fixed effects part, Zu is the random effects part and e is the error term, then the REML estimates are obtained by maximizing the likelihood function of K’y, where K is a full rank matrix with columns orthogonal to the columns of the X matrix, that is, K’X* *= 0. I

**6) Proc Genmod-**PROC GENMOD uses a class statement for specifying categorical (classification) variables, so indicator variables do not have to be constructed in advance, as is the case with, for example, PROC LOGISTIC. Interactions can be fitted by specifying, for example, age*sex. The response variable or the explanatory variable can be character while PROC LOGISTIC requires explanatory variables to be numeric.

**7) Proc Corr-**CORR procedure computes correlation coefficients between variables. It can also produce covariances.

**8) Proc Anova-**PROC ANOVA handles only balanced ANOVA designs

**Required reading **–http://en.wikipedia.org/wiki/Regression_analysis

SAS Online Doc

**Additional Reading-**