Welcome to your onestop solution for all the information you need to excel in the SAS Statistical Business Analysis Using SAS 9  Regression and Modeling (A00240) Certification exam. This page provides an indepth overview of the SAS A00240 Exam Summary, Syllabus Topics, and Sample Questions, designed to lay the foundation for your exam preparation. We aim to help you achieve your SAS Certified Statistical Business Analyst Using SAS 9  Regression and Modeling certification goals seamlessly. Our detailed syllabus outlines each topic covered in the exam, ensuring you focus on the areas that matter most. With our sample questions and practice exams, you can gauge your readiness and boost your confidence to take on the SAS Statistical Business Analysis exam.
Why SAS Statistical Business Analysis Certification Matters
The SAS A00240 exam is globally recognized for validating your knowledge and skills. With the SAS Certified Statistical Business Analyst Using SAS 9  Regression and Modeling credential, you stand out in a competitive job market and demonstrate your expertise to make significant contributions within your organization. The SAS Statistical Business Analysis Using SAS 9  Regression and Modeling Certification exam will test your proficiency in the various syllabus topics.
SAS A00240 Exam Summary:
Exam Name  SAS Statistical Business Analysis Using SAS 9  Regression and Modeling 
Exam Code  A00240 
Exam Duration  120 minutes 
Exam Questions  60 
Passing Score  68% 
Exam Price  $180 (USD) 
Books / Training 
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression Predictive Modeling Using Logistic Regression 
Exam Registration  Pearson VUE 
Sample Questions  SAS Statistical Business Analysis Regression and Modeling Certification Sample Question 
Practice Exam  SAS Statistical Business Analysis Regression and Modeling Certification Practice Exam 
SAS A00240 Exam Syllabus Topics:
Objective  Details 

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, box whisker, 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
 Evaluate the null hypothesis using the output of the GLM procedure 
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 
Linear Regression  20% 

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 differentmodel 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) 
Logistic Regression  25% 

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 LOGISTIC 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 
Prepare Inputs for Predictive Model Performance  20% 

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
 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 SGPLOT procedure  Interpret empirical logit plots 
Measure Model Performance  25% 

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 
The SAS has created this credential to assess your knowledge and understanding in the specified areas through the A00240 certification exam. The SAS Certified Statistical Business Analyst Using SAS 9  Regression and Modeling exam holds significant value in the market due to the brand reputation of SAS. We highly recommend thorough study and extensive practice to ensure you pass the SAS Statistical Business Analysis Using SAS 9  Regression and Modeling exam with confidence.