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SASInstitute A00-485 valid - in .pdf
- Exam Code: A00-485
- Exam Name: Modeling Using SAS Visual Statistics
- Q & A: 190 Questions and Answers
- PDF Price: $59.99
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- Exam Code: A00-485
- Exam Name: Modeling Using SAS Visual Statistics
- Q & A: 190 Questions and Answers
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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation and Exploration in SAS Viya | - Exploratory data analysis and visualization - Loading and preparing data for Visual Statistics |
| Topic 2: Model Evaluation and Interpretation | - Assessing model performance - Interpreting model outputs and reports |
| Topic 3: Machine Learning Methods in Visual Statistics | - Clustering techniques - Decision trees and random forests concepts |
| Topic 4: Predictive Modeling Fundamentals | - Model selection and comparison techniques - Regression modeling concepts
|
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
Question #1
When interpreting an Influence plot, what does it typically show?
- A. The effect of each predictor variable on the response
- B. The interaction between two predictor variables
- C. The distribution of residuals in the model
- D. The relationship between the response variable and the intercept term
Correct Answer: A 🗳️
Question #2
What can be interpreted from the Summary Table for model comparison, including statistics and variable importance?
- A. The number of iterations performed by each model
- B. The distribution of predictor variables
- C. Model performance metrics and their relative importance
- D. The correlation matrix of predictor variables
Correct Answer: C 🗳️
Question #3
In the below nonparametric logistic regression results display, where would you click to get a plot of significant continuous effects?
Correct Answer:
Question #4
In the context of assessing model results, what information can be interpreted from the Fit Summary window?
- A. A list of all predictor variables used in the model
- B. A summary of the model's goodness of fit and performance metrics
- C. A detailed description of the data preprocessing steps
- D. The distribution of the response variable in the dataset
Correct Answer: B 🗳️
Question #5
In linear regression, what does a high p-value for a predictor variable suggest?
- A. The predictor is highly significant for the model.
- B. The predictor is not relevant for the model.
- C. The predictor has a strong linear relationship with the response.
- D. The predictor is a categorical variable.
Correct Answer: B 🗳️
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