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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. You want to fit a Poisson regression and use the log link function to predict skin cancer from weather, pollution, and demographic variables. Each row in the data is a geographic region.
The response, Melanoma, is stored as the number of melanoma skin cancer cases in each region.
Because some regions have many people and some have few, your data also includes a variable with the region population.
What is the best way to include population in this model?

A) Add Population as an effect.
B) Add the log of Population as an offset.
C) Add the log of Population as an effect.
D) Add Population as an offset.

2. When should you replace dirty data with missing values in SAS Visual Analytics?

A) When the data contains outliers
B) When you want to remove missing values
C) When the data is incorrect or invalid
D) When you want to create a new variable

3. Which statement is true for the Informative Missingness property in SAS Visual Statistics?

A) A table of missingness patterns for the predictor variables is displayed in the model output.
B) The user is prompted to fill in a value to impute for missing predictor variables.
C) Missing values are imputed and indicator variables are created for missing predictors.
D) Missing values are flagged on diagnostic plots.

4. When assigning variable roles for a decision tree, which role typically represents the target variable you want to predict?

A) Splitter
B) Output
C) Predictor
D) Input

5. What does it mean to create dummy variables in SAS Visual Data Builder?

A) Aggregating multiple measures into one variable
B) Converting categorical variables into binary variables
C) Generating artificial data points
D) Replacing missing values with zeros

Solutions: