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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| SAS Visual Statistics Cross-functional Tasks | 22% | - Filter data used for a model - Use interactive group-by functionality - Perform model validation - Prepare data using SAS Visual Analytics |
| Model Comparison and Scoring | 9% | - Apply models to score new data - Compare multiple models using fit statistics |
| Building and Assessing Segmentation Models | 28% | - Assess and interpret decision tree performance - Perform unsupervised segmentation using cluster analysis - Analyze and interpret cluster results - Perform supervised segmentation using decision trees |
| Building and Assessing Regression-type Models | 41% | - Perform nonparametric logistic regression modeling - Perform generalized linear regression modeling - Perform generalized additive modeling - Assess model fit and diagnostics - Explain concepts of linear models - Perform linear regression modeling |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. Your company has a dataset that represents global sales. You are a part of a team of analysts that each have responsibility for a certain region of the world. You decide to create a data source filter to suppress every region but yours.
What effect will this have on any new explorations that your teammates create?
A) It will suppress all observations that do not match your region.
B) It will delete all observations that do not match your region.
C) It will suppress all observations that do not match their corresponding region.
D) It will have no effect on any observations in the dataset.
2. Which equation does NOT represent a linear model? Note: bi are parameters and Xi are variables.
A) y = b0 + b1X1 + b2X2
B) y = b0 + b1X1 + b2X1 3
C) y = b0 + b1X1 + (b2/b1)X2
D) y = b0 + b1X1 + b2X2 + b3(X1X2)
3. Interactive group-by analysis can be performed for each of the following in Visual Statistics except:
A) Logistic regression
B) Generalized linear model
C) Decision tree
D) Linear regression
4. What are some properties that can be defined in a generalized linear regression model?
A) Coefficients and p-values
B) Distribution and link function
C) Number of predictors
D) Residuals and intercept
5. When examining the Fit Summary for group-by models, what can you analyze to evaluate the model fit?
A) BIC (Bayesian Information Criterion)
B) R-squared value
C) AIC (Akaike Information Criterion)
D) p-value for the group-by variable
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A,B,C |





