SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

Designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis

The Business Analytics Program, which began in fall 2013 and is cosponsored by SAS, meets the demand of local businesses for data savvy professionals, now and in the future. This program helps marketers, business analysts, accountants, financial analysts, executives, small business owners and other non-IT professionals effectively analyze business data through the hands-on use of modeling and other techniques using popular software SAS

·         analysis of variance

·         linear and logistic regression

·         preparing inputs for predictive models

·         measuring model performance

Candidates who earn this credential will have earned a passing score on the SAS Statistical Business Analysis Using SAS 9: Regression and Modeling exam. This exam is administered by SAS and Pearson VUE.

·         60 scored multiple-choice and short-answer questions (must achieve score of 68% correct to pass)

·         In addition to the 60 scored items, there may be up to 5 unscored items.

·         2 hours to complete exam

·         Use exam ID A00-240; required when registering with Pearson VUE.

·          

·         Verify the assumptions of ANOVA

·         Analyze differences between population means using the GLM and TTEST procedures

·         Perform ANOVA post hoc test to evaluate treatment effect

·         Detect and analyze interactions between factors

·         Linear Regression

·         Fit a multiple linear regression model using the REG and GLM procedures

·         Analyze the output of the REG procedure for multiple linear regression models

·         Use the REG procedure to perform model selection

·         Assess the validity of a given regression model through the use of diagnostic and residual analysis

·         Perform logistic regression with the LOGISTIC procedure

·         Optimize model performance through input selection

·         Interpret the output of the LOGISTIC procedure

·         Score new data sets using the LOGISTIC and SCORE procedures

Prepare Inputs for Predictive Model Performance

Identify potential problems with input data

Use the DATA step to manipulate data with loops, arrays, conditional statements and functions

Reduce the number of categorical levels in a predictive model

Screen variables for irrelevance using the CORR procedure

Screen variables for non-linearity using empirical logit plots

Measure Model Performance

·         Apply the principles of honest assessment to model performance measurement

·         Assess classifier performance using the confusion matrix

·         Model selection and validation using training and validation data

·         Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection

·         Establish effective decision cut-off values for scoring

·         analysis of variance

·         linear and logistic regression

·         preparing inputs for predictive models

·         measuring model performance.

Experience is a critical component to becoming a SAS Certified Professional. While no exam questions will be drawn verbatim from the courses or course exercises, these courses will provide candidates with a foundation from which to apply the knowledge and skills necessary for the exam.