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Topics of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Dumps will include the following topics:
1. ANOVA - 10%
- 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
2. Linear Regression - 20%
- Use the REG or GLMSELECT procedure to perform model selection
- Assess the validity of a given regression model through the use of diagnostic and residual analysis
- Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
- Fit a multiple linear regression model using the REG and GLM procedures
3. Logistic Regression - 25%
- Score new data sets using the LOGISTIC and PLM procedures
- Interpret the output of the LOGISTIC procedure
- Perform logistic regression with the LOGISTIC procedure
- Optimize model performance through input selection
4. Prepare Inputs for Predictive Model Performance - 20%
- Identify the potential challenges when preparing input data for a model
- Screen variables for non-linearity using empirical logit plots
- Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
- Screen variables for irrelevance and non-linear association using the CORR procedure
- Improve the predictive power of categorical inputs
5. Measure Model Performance - 25%
- Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
- Model selection and validation using training and validation data
- Assess classifier performance using the confusion matrix
- Apply the principles of honest assessment to model performance measurement
- Establish effective decision cut-off values for scoring
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Logistic Regression | 25% | - Score new datasets with LOGISTIC and PLM - Select and optimize input variables - Interpret odds ratios and model coefficients - Build models using PROC LOGISTIC |
| Topic 2: Prepare Inputs for Predictive Modeling | 20% | - Manipulate data using DATA step - Screen variables with CORR procedure and empirical logit plots - Identify data preparation challenges - Transform categorical variables |
| Topic 3: Analysis of Variance (ANOVA) | 10% | - Assess ANOVA assumptions - Analyze factor interactions - Use PROC GLM for ANOVA - Perform post-hoc tests |
| Topic 4: Linear Regression | 20% | - Conduct residual and diagnostic analysis - Perform model selection with GLMSELECT - Fit multiple linear regression models - Interpret REG, GLM, and PLM procedure output |
| Topic 5: Measure and Evaluate Model Performance | 25% | - Use confusion matrices and classification metrics - Validate models with training/validation data - Create and interpret ROC, lift, and gains charts - Determine optimal decision cut-off values - Apply honest assessment principles |
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