SAS Global Certification program
SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling CredentialDesigned for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis Successful candidates should have experience in

Required Exam
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 multiplechoice and shortanswer 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 A00240; required when registering with Pearson VUE.
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ANOVA  10%
Linear Regression  20%
Logistic Regression  25%
Prepare Inputs for Predictive Model Performance  20%
Measure Model Performance  25%
Note: All 22 main objectives will be tested on every exam. The 126 expanded objectives are provided for additional explanation and define the entire domain that could be tested.
Exam topics include:
ANOVA  10%
Verify the assumptions of ANOVA
 Explain the central limit theorem and when it must be applied
 Examine the distribution of continuous variables (histogram, boxwhisker, QQ plots)
 Describe the effect of skewness on the normal distribution
 Define H0, H1, Type I/II error, statistical power, pvalue
 Describe the effect of sample size on pvalue and power
 Interpret the results of hypothesis testing
 Interpret histograms and normal probability charts
 Draw conclusions about your data from histogram, boxwhisker, and QQ plots
 Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
 For a given experiment, verify that the observations are independent
 For a given experiment, verify the errors are normally distributed
 Use the UNIVARIATE procedure to examine residuals
 For a given experiment, verify all groups have equal response variance
 Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance
Analyze differences between population means using the GLM and TTEST procedures
 Use the GLM Procedure to perform ANOVA
 CLASS statement
 MODEL statement
 MEANS statement
 OUTPUT statement
 Evaluate the null hypothesis using the output of the GLM procedure
 Interpret the statistical output of the GLM procedure (variance derived from MSE, F value, pvalue R**2, Levene's test)
 Interpret the graphical output of the GLM procedure
 Use the TTEST Procedure to compare means
Perform ANOVA post hoc test to evaluate treatment effect
 Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
 Use PDIFF option of LSMEANS statement
 Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
 Interpret diffograms to evaluate pairwise comparisons
 Interpret control plots to evaluate pairwise comparisons
 Compare/Contrast use of pairwise TTests, Tukey and Dunnett comparison methods
Detect and analyze interactions between factors
 Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
 LSMEANS with SLICE=option (Also using PROC PLM)
 ODS SELECT
 Interpret the output of the GLM procedure to identify interaction between factors: pvalue
 F Value
 R Squared
 TYPE I SS
 TYPE III SS
Fit a multiple linear regression model using the REG and GLM procedures
 Use the REG procedure to fit a multiple linear regression model
 Use the GLM procedure to fit a multiple linear regression model
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
 Interpret REG or GLM procedure output for a multiple linear regression model: convert models to algebraic expressions
 Convert models to algebraic expressions
 Identify missing degrees of freedom
 Identify variance due to model/error, and total variance
 Calculate a missing F value
 Identify variable with largest impact to model
 For output from two models, identify which model is better
 Identify how much of the variation in the dependent variable is explained by the model
 Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)
Use the REG or GLMSELECT procedure to perform model selection
 Use the SELECTION option of the model statement in the GLMSELECT procedure
 Compare the different model selection methods (STEPWISE, FORWARD, BACKWARD)
 Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
 Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
 Assign names to models in the REG procedure (multiple model statements)
Assess the validity of a given regression model through the use of diagnostic and residual analysis
 Explain the assumptions for linear regression
 From a set of residuals plots, asses which assumption about the error terms has been violated
 Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
 Explain options for handling influential observations
 Identify collinearity problems by examining REG procedure output
 Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)
Perform logistic regression with the LOGISTIC procedure
 Identify experiments that require analysis via logistic regression
 Identify logistic regression assumptions
 logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
 Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)
Optimize model performance through input selection
 Use the LOGISTIC procedure to fit a multiple logistic regression model
 LOGISTIC procedure SELECTION=SCORE option
 Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure
Interpret the output of the LOGISTIC procedure
 Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
 Testing Global Null Hypothesis table
 Type 3 Analysis of Effects table
 Analysis of Maximum Likelihood Estimates table
 Association of Predicted Probabilities and Observed Responses
Score new data sets using the LOGISTIC and PLM procedures
 Use the SCORE statement in the PLM procedure to score new cases
 Use the CODE statement in PROC LOGISITIC to score new data
 Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
 Use the INMODEL/OUTMODEL options in PROC LOGISTIC
 Explain how to score new data when you have developed a model from a biased sample
Identify the potential challenges when preparing input data for a model
 Identify problems that missing values can cause in creating predictive models and scoring new data sets
 Identify limitations of Complete Case Analysis
 Explain problems caused by categorical variables with numerous levels
 Discuss the problem of redundant variables
 Discuss the problem of irrelevant and redundant variables
 Discuss the nonlinearities and the problems they create in predictive models
 Discuss outliers and the problems they create in predictive models
 Describe quasicomplete separation
 Discuss the effect of interactions
 Determine when it is necessary to oversample data
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
 Use ARRAYs to create missing indicators
 Use ARRAYS, LOOP, IF, and explicit OUTPUT statements
Improve the predictive power of categorical inputs
 Reduce the number of levels of a categorical variable
 Explain thresholding
 Explain Greenacre's method
 Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
 METHOD=WARD option
 FREQ, VAR, ID statement
 Use of ODS output to create an output data set
 Convert categorical variables to continuous using smooth weight of evidence
Screen variables for irrelevance and nonlinear association using the CORR procedure
 Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and nonlinear associations
 Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
 Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and nonlinear associations
Screen variables for nonlinearity using empirical logit plots
 Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
 Interpret RANK procedure output
 Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
 Create empirical logit plots with the GPLOT procedure
 Interpret empirical logit plots
Apply the principles of honest assessment to model performance measurement
 Explain techniques to honestly assess classifier performance
 Explain overfitting
 Explain differences between validation and test data
 Identify the impact of performing data preparation before data is split
Assess classifier performance using the confusion matrix
 Explain the confusion matrix
 Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV
 Explain the effect of oversampling on the confusion matrix
 Adjust the confusion matrix for oversampling
Model selection and validation using training and validation data
 Divide data into training and validation data sets using the SURVEYSELECT procedure
 Discuss the subset selection methods available in PROC LOGISTIC
 Discuss methods to determine interactions (forward selection, with bar and @ notation)
 Create interaction plot with the results from PROC LOGISTIC
 Select the model with fit statistics (BIC, AIC, KS, Brier score)
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
 Explain and interpret charts (ROC, Lift, Gains)
 Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
 Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
 Explain the concept of depth as it relates to the gains chart
Establish effective decision cutoff values for scoring
 Illustrate a decision rule that maximizes the expected profit
 Explain the profit matrix and how to use it to estimate the profit per scored customer
 Calculate decision cutoffs using Bayes rule, given a profit matrix
 Determine optimum cutoff values from profit plots
 Given a profit matrix, and model results, determine the model with the highest average profit
Note: All 22 main objectives will be tested on every exam. The 126 expanded objectives are provided for additional explanation and define the entire domain that could be tested.
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Practice:
 View the sample questions for this exam.
 Access SAS software through the SAS University Edition.
More information:
Contact the SAS Global Certification Program at certification@sas.com or 8007270025.Registration Options:
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Testing Locations
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Exam Pricing
Within North America and India, the fees associated with an exam offered through Pearson VUE is $180 USD.Certification exam prices are subject to change. In some countries, different pricing and additional taxes may apply. Please visit www.pearsonvue.com/sas for exam pricing in your country.
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Retake Policy
Candidates may attempt each certification exam up to five times in a 12month period, waiting a minimum of 14 days between attempts. Exam charges are incurred for each exam attempt. Exams that do not comply with this retake policy will be considered invalid and will not be eligible for refund and/or a certification credential. Once a passing score is achieved on a specific exam, no further attempts are allowed on that exam.Candidate Agreement
Candidates are encouraged to review the SAS Institute Inc. Global Certification Program Candidate Agreement prior to their exam day.
What to bring: Certification candidates are required to provide two forms of identification to the testing center, including one photo identification such as a driver's license or a valid
passport. Both forms must display the candidate's signature. If you have questions regarding acceptable forms of id, please contact www.pearsonvue.com/contact.
Arriving at the test center: Candidates should plan to arrive 15 minutes before their scheduled exam time. Candidates arriving more than 15 minutes late are not guaranteed exam availability or a refund.
Reference materials: To maintain the security of the test environment, candidates are not permitted to bring reference materials of any kind into the testing center.
Personal items: The only items allowed in the testing area are your identification. Please leave any backpacks, laptops, briefcases and other personal items at home. If you have personal items that cannot be left behind (such as purses), the testing center may have lockers available for use. No cameras, cell phones, audio players, or other electronic devices are allowed during exam sessions. Please refer to Pearson VUE Candidate Rules Agreement for more information.
All notes will be collected at the end of testing and no material may be removed from the testing event.
Arriving at the test center: Candidates should plan to arrive 15 minutes before their scheduled exam time. Candidates arriving more than 15 minutes late are not guaranteed exam availability or a refund.
Reference materials: To maintain the security of the test environment, candidates are not permitted to bring reference materials of any kind into the testing center.
Personal items: The only items allowed in the testing area are your identification. Please leave any backpacks, laptops, briefcases and other personal items at home. If you have personal items that cannot be left behind (such as purses), the testing center may have lockers available for use. No cameras, cell phones, audio players, or other electronic devices are allowed during exam sessions. Please refer to Pearson VUE Candidate Rules Agreement for more information.
All notes will be collected at the end of testing and no material may be removed from the testing event.
Score Report
You will receive an immediate pass/fail score upon completion of your exam attempt at your testing facility. The score report will display the percentage of items in each section that you answered correctly for your exam. Please note: These section scores are calculated on a per section basis and cannot be used in determining your total score. They are provided to you for descriptive purposes only.Welcome Email and Certificate
If you pass your exam and meet all requirements for this credential, you will receive an email from SAS with instructions providing access to your certificate and logo through the Certification Records Management System. This email will be sent to the email address you provided to Pearson VUE at exam registration. Some individual firewalls may send this email to your junk folder. Please allow at least one week from your exam date to receive your email.Within Certification Records Management system, your certificate can be accessed on the left navigation bar under "Printable Documents." To print your certificate, your popup blocker should be disabled before clicking the "Print Now" button. Click on "Print Now" and your certificate will open in a new window where you can download and/or print.
Certain credentials require more than one exam to earn the credential. We encourage you to visit credentials and exams for more information.
Public Registry of Certified Professionals
A Public Registry of SAS Certified Professionals is maintained within the SAS Certification Records Management system. If you do not wish for your name to appear in the Public Registry of SAS Certified Professionals, you can choose to be excluded by updating your personal information in the SAS Certification Records Management system.Once you earn your credential, you'll enjoy these perks:
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