This page is a one-stop solution for any information you may require for SAS Certified Statistical Business Analyst Using SAS 9 (A00-240) Certification exam. The SAS A00-240 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Certified Statistical Business Analyst Using SAS 9 - Regression and Modeling exam preparation, we have designed these resources to help you get ready to take your dream exam.
The SAS Certified Statistical Business Analyst Using SAS 9 credential is globally recognized for validating SAS Statistical Business Analyst knowledge. With the SAS Certified Statistical Business Analyst Using SAS 9 - Regression and Modeling Certification credential, you stand out in a crowd and prove that you have the SAS Statistical Business Analyst knowledge to make a difference within your organization. The SAS Certified Statistical Business Analyst Using SAS 9 Certification (A00-240) exam will test the candidate's knowledge on following areas.
SAS A00-240 Exam Summary:
| Exam Name | SAS Certified Statistical Business Analyst Using SAS 9 |
| Exam Code | A00-240 |
| Exam Duration | 120 minutes |
| Exam Questions | 60 |
| Passing Score | 68% |
| Exam Price | $180 (USD) |
| Training |
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression Predictive Modeling Using Logistic Regression |
| Book | SAS® Certification Prep Guide: Statistical Business Analysis Using SAS®9 |
| Exam Registration | Pearson VUE |
| Sample Questions | SAS Statistical Business Analyst Certification Sample Question |
| Practice Exam | SAS Statistical Business Analyst Certification Practice Exam |
SAS A00-240 Exam 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, Q-Q plots) - Describe the effect of skewness on the normal distribution - Define H0, H1, Type I/II error, statistical power, p-value - Describe the effect of sample size on p-value and power - Interpret the results of hypothesis testing - Interpret histograms and normal probability charts - Draw conclusions about your data from histogram, box-whisker, and Q-Q 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 T-Tests, 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: - p-value - 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 non-linearities and the problems they create in predictive models - Discuss outliers and the problems they create in predictive models - Describe quasi-complete 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 non-linear association using the CORR procedure |
- Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear 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 non-linear associations |
| Screen variables for non-linearity 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 cut-off 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 the knowledge and understanding of a candidate in the area as above via the certification exam. The SAS Statistical Business Analyst (A00-240) Certification exam contains a high value in the market being the brand value of the SAS attached with it. It is highly recommended to a candidate to do a thorough study and also get a hand full of the practice to clear SAS Certified Statistical Business Analyst Using SAS 9 exam without any hiccups.