Free PDF 2026 A00-240: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Updated Test Free

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Measure Model Performance25%- Decision Optimization
  • 1. Establish effective decision cutoff values
  • 2. Evaluate scoring strategies
- Model Assessment
  • 1. Apply principles of honest assessment
  • 2. Assess classifier performance using confusion matrices
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data
- Performance Visualization
  • 1. Create and interpret ROC charts
  • 2. Create and interpret gains charts
  • 3. Create and interpret lift charts
Topic 2: ANOVA10%- ANOVA Analysis
  • 1. Detect and analyze factor interactions
  • 2. Perform post hoc tests
  • 3. Evaluate treatment effects
  • 4. Analyze differences between population means using GLM and TTEST procedures
- ANOVA Concepts and Assumptions
  • 1. Identify appropriate ANOVA methods
  • 2. Verify ANOVA assumptions
Topic 3: Logistic Regression25%- Interpret and Deploy Models
  • 1. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
- Develop Logistic Models
  • 1. Optimize model performance through input selection
  • 2. Perform logistic regression using the LOGISTIC procedure
Topic 4: Linear Regression20%- Build Regression Models
  • 1. Use REG and GLM procedures
  • 2. Fit multiple linear regression models
- Model Selection and Diagnostics
  • 1. Assess model validity
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Conduct residual and diagnostic analysis
- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
Topic 5: Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Use empirical logit plots for nonlinearity detection
  • 2. Identify nonlinear associations using CORR procedure
  • 3. Improve predictive power of categorical variables
  • 4. Screen variables for irrelevance
- Data Preparation
  • 1. Manipulate data using DATA step programming
  • 2. Use loops, arrays, functions and conditional statements
  • 3. Identify challenges in preparing model input data

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q82-Q87):

NEW QUESTION # 82
Which statistic, calculated from a validation sample, can help decide which model to use for prediction of a binary target variable?

Answer: C

Explanation:
Explanation/Reference:


NEW QUESTION # 83
Refer to the confusion matrix:

Calculate the sensitivity. (0 - negative outcome, 1 - positive outcome)
Click the calculator button to display a calculator if needed.

Answer: D


NEW QUESTION # 84
Which statistic, calculated from a validation sample, can help decide which model to use for prediction of a binary target variable?

Answer: C


NEW QUESTION # 85
When mean imputation is performed on data after the data is partitioned for honest assessment,
what is the most appropriate method for handling the mean imputation?

Answer: A


NEW QUESTION # 86
A financial services manager wants to assess the probability that certain clients will default on their Home Equity Line of Credit (HELOC). A former employee left the code listed below.

The training data set is named HELOC, while a similar data set of more recent clients is named RECENT_HELOC. Which SAS data steps will calculate the predicted probability of default on recent clients? (Choose two.)

Answer: A,B


NEW QUESTION # 87
......

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