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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When building a cluster analysis in SAS Visual Statistics, which of the following algorithms is commonly used for partitioning data into clusters?

A) Decision Tree
B) Principal Component Analysis (PCA)
C) K-means
D) Linear Regression

2. Given an Influence Plot generated in a Linear Regression within SAS Visual Statistics, which diagnostic can be used to quantify leverage?

A) Likelihood Displacement
B) AIC
C) Adjusted R-Squared
D) Concordance

3. Which statement is true regarding the number of polylines created in a Parallel Coordinates plot during Cluster Analysis?

A) It cannot exceed 3,000.
B) It is calculated using the number of variables visible in the plot.
C) It is calculated using the number of variables used in the cluster analysis.
D) It is calculated using the variable with the greatest number of bins.

4. How are predictions formulated for a decision tree when a new data point is presented to the model?

A) By using a weighted sum of all decision rules in the tree
B) By averaging the values of all leaf nodes
C) By following a path through the tree to a leaf node and returning the majority class or mean value of that node
D) By calculating the Euclidean distance to all tree nodes and selecting the closest one

5. What is the primary purpose of modeling effects in linear regression?

A) To calculate the p-values of the predictors
B) To identify outliers in the data
C) To assess multicollinearity among predictors
D) To capture the relationship between predictors and the response variable

Solutions: