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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

Question 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 Population as an offset.
D. Add the log of Population as an effect.

Question 2

You have a Partition column in your data that takes on values of 0, 1, and 2.
What happens, by default, if you define a Partition data item from this variable?

A. No partition will be used because there are more than two levels.
B. Observations with a value of 2 will be used as Training data.
C. Observations with a value of 0 will be used as Training data.
D. Observations with a value of 1 will be used as Training data.

Question 3

When examining the summary table for group-by processing, what information can you find?

A. The mean value of the target variable for each group
B. The distribution of the group-by variable
C. The number of observations in each group
D. The standard deviation of the target variable for each group

Question 4

What properties can be assigned when comparing models in SAS Visual Statistics?

A. The learning rate for gradient boosting models
B. The color scheme for visualization
C. The variables to include in the model comparison
D. The number of observations in the dataset

Question 5

In generalized linear models, what is the purpose of the link function?

A. To adjust the model for multicollinearity
B. To calculate the p-values of predictor variables
C. To determine the distribution of the response variable
D. To specify the relationship between the predictor and response variables

Solutions: