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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| SAS Visual Statistics Cross-functional Tasks | 22% | - Use interactive group-by functionality - Perform model validation - Filter data used for a model - Prepare data using SAS Visual Analytics |
| Building and Assessing Segmentation Models | 28% | - Perform supervised segmentation using decision trees - Assess and interpret decision tree performance - Analyze and interpret cluster results - Perform unsupervised segmentation using cluster analysis |
| Building and Assessing Regression-type Models | 41% | - Assess model fit and diagnostics - Perform generalized additive modeling - Explain concepts of linear models - Perform generalized linear regression modeling - Perform nonparametric logistic regression modeling - Perform linear regression modeling |
| Model Comparison and Scoring | 9% | - Apply models to score new data - Compare multiple models using fit statistics |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
What are some properties that can be defined in a generalized linear regression model?
- A. Distribution and link function
- B. Coefficients and p-values
- C. Residuals and intercept
- D. Number of predictors
Correct Answer: A 🗳️
What does adding an interaction effect in linear regression modeling allow you to capture?
- A. The joint impact of two or more predictors on the response variable
- B. The multicollinearity among predictors
- C. The presence of outliers in the data
- D. The effect of missing data on the model
Correct Answer: A 🗳️
When interpreting a Residual Plot, what does it reveal about the model's performance?
- A. The linear relationship between predictors and the response
- B. The distribution of predictor variables in the dataset
- C. The p-values associated with the predictor variables
- D. The presence of any outliers or patterns in prediction errors
Correct Answer: D 🗳️
When combining multiple categories into fewer levels in SAS Visual Analytics, what technique is often used?
- A. Principal Component Analysis (PCA)
- B. Binning or grouping
- C. Data imputation
- D. Variable transformation
Correct Answer: B 🗳️
Prior to fitting a regression model, data binning of a continuous effect should be used for what purpose?
- A. Reduce the impact of minor observation errors.
- B. Improve the accuracy of your predictions.
- C. Gain information due to discretization.
- D. Reduce the cardinality of a continuous effect.
Correct Answer: A 🗳️
