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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What are some properties that can be defined in a generalized linear regression model?

A) Distribution and link function
B) Coefficients and p-values
C) Residuals and intercept
D) Number of predictors

2. What does adding an interaction effect in linear regression modeling allow you to capture?

A) The joint impact of two or more predictors on the response variable
B) The multicollinearity among predictors
C) The presence of outliers in the data
D) The effect of missing data on the model

3. When interpreting a Residual Plot, what does it reveal about the model's performance?

A) The linear relationship between predictors and the response
B) The distribution of predictor variables in the dataset
C) The p-values associated with the predictor variables
D) The presence of any outliers or patterns in prediction errors

4. When combining multiple categories into fewer levels in SAS Visual Analytics, what technique is often used?

A) Principal Component Analysis (PCA)
B) Binning or grouping
C) Data imputation
D) Variable transformation

5. Prior to fitting a regression model, data binning of a continuous effect should be used for what purpose?

A) Reduce the impact of minor observation errors.
B) Improve the accuracy of your predictions.
C) Gain information due to discretization.
D) Reduce the cardinality of a continuous effect.

Solutions: