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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What is the primary benefit of exporting score code for model deployment?

A) It allows for model performance assessment on a test dataset.
B) It enables scoring of new data without the need for SAS Visual Statistics.
C) It provides insights into the distribution of predictor variables.
D) It automatically generates new predictor variables.

2. When assessing the quality of a linear regression model, what does a low p-value associated with the F-statistic suggest?

A) The model is a poor fit for the data.
B) The model has a significant overall explanatory power.
C) The model has a weak linear relationship with the response.
D) The model has a high level of multicollinearity.

3. 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.

4. What does a Lift chart evaluate in terms of model performance?

A) The correlation between predictor variables
B) The distribution of the response variable
C) The relative performance of the model compared to a random model
D) The interaction effects between predictor variables

5. After a logistic regression has been created in SAS Visual Statistics, you discover that not all observations were used to create the model.
How would you run the model on all of the data?

A) Include the correct offset term.
B) Include the correct interaction term.
C) Select Informative Missingness on the properties tab.
D) Select Use Variable Selection on the properties tab.

Solutions: