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SASInstitute A00-485 Exam Syllabus Topics:

Section	Objectives
Data Preparation and Exploration in SAS Viya	- Loading and preparing data for Visual Statistics - Exploratory data analysis and visualization
Predictive Modeling Fundamentals	- Regression modeling concepts 1. Linear regression 2. Logistic regression - Model selection and comparison techniques
Machine Learning Methods in Visual Statistics	- Clustering techniques - Decision trees and random forests concepts
Model Evaluation and Interpretation	- Assessing model performance - Interpreting model outputs and reports

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What are the roles that can be assigned in a generalized linear model (GLM)?

A) Target
B) Intercept
C) Predictor
D) Offset

2. Given a scenario, what condition makes models comparable in SAS Visual Statistics?

A) If they use the same dataset and response variable
B) If they have different sample sizes
C) If they use different link functions
D) If they use different response variables

3. In the context of linear regression, what does the term "linearity" refer to?

A) The use of polynomial functions for modeling
B) The use of a straight line to model relationships
C) The absence of any relationship between variables
D) The requirement for variables to be categorical

4. In linear regression, what does the coefficient of determination (R-squared) measure?

A) The impact of outliers on the model
B) The p-value associated with the intercept term
C) The goodness of fit of the model to the data
D) The degree of multicollinearity among predictors

5. Refer to the exhibit:

Which option was not specified in creating the linear regression model using SAS Visual Statistics?