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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and Assessing Regression-type Models | 41% | - Perform linear regression modeling - Explain concepts of linear models - Perform nonparametric logistic regression modeling - Perform generalized additive modeling - Assess model fit and diagnostics - Perform generalized linear regression modeling |
| Model Comparison and Scoring | 9% | - Apply models to score new data - Compare multiple models using fit statistics |
| SAS Visual Statistics Cross-functional Tasks | 22% | - Prepare data using SAS Visual Analytics - Filter data used for a model - Perform model validation - Use interactive group-by functionality |
| Building and Assessing Segmentation Models | 28% | - Perform supervised segmentation using decision trees - Perform unsupervised segmentation using cluster analysis - Analyze and interpret cluster results - Assess and interpret decision tree performance |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. When interpreting an Influence plot, what does it typically show?
A) The relationship between the response variable and the intercept term
B) The effect of each predictor variable on the response
C) The interaction between two predictor variables
D) The distribution of residuals in the model
2. In linear regression, what does a high p-value for a predictor variable suggest?
A) The predictor is a categorical variable.
B) The predictor is highly significant for the model.
C) The predictor has a strong linear relationship with the response.
D) The predictor is not relevant for the model.
3. How is the KS Statistic typically used when interpreting an ROC chart?
A) To evaluate the overall performance of a classification model
B) To measure the area under the ROC curve
C) To assess the balance between sensitivity and specificity
D) To identify the optimal prediction cut-off threshold
4. Why are decision trees a useful technique for segmentation?
A) Trees split data into smaller cells which are decreasingly "pure".
B) Trees treat each split cell dependently based on earlier split decisions.
C) Trees split the response into heterogeneous groups, while minimizing the difference in the response of the groups.
D) Trees split the response into homogeneous groups, while maximizing the difference in the response of the groups.
5. When examining the summary table for group-by processing, what information can you find?
A) The standard deviation of the target variable for each group
B) The mean value of the target variable for each group
C) The distribution of the group-by variable
D) The number of observations in each group
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A,B,D |
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