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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

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

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

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

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

3. Which statement is true about Decision Trees in SAS Visual Statistics

A) Decision trees can be used for outlier detection.
B) Decision trees cannot handle missing values.
C) Decision trees cannot handle continuous dependent variables.
D) Decision trees can be used for classification and estimation.

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

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

5. Refer to the exhibit:

Which is the modeling approach that should be used when fitting the Target Gift Amount variable?

A) Logistic regression model.
B) Generalized linear model with a Poisson distribution and Identity link.
C) Linear regression model with Interaction effects.
D) Generalized linear model with a Normal distribution and Log Link.

Solutions: