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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. Refer to the exhibit:

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

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

2. What is the purpose of applying a prediction cut-off in model assessment?

A) To determine the optimal threshold for classifying outcomes
B) To divide the dataset into training and testing sets
C) To calculate the p-values of predictor variables
D) To identify influential data points in the dataset

3. Refer to the exhibit from SAS Visual Statistics:

What can be concluded from the lift plot shown in the exhibit?

A) The response rate for the best 20% is predicted to be 2.75 times greater than the sample fitted in the model.
B) The 20% of cases with the highest predicted probability of the modeled event level is expected to capture 2.75 times as many events as a random sample of the same size.
C) The 20% of cases with the lowest predicted probability of the modeled event level is predicted to capture 2.75 times as many events as a random sample of 20%.
D) The response rate for the best 20% is predicted to increase 5 times under the fitted model.

4. When interpreting comparison results using the Assessment panel, what information can be obtained?

A) The relationship between two predictor variables
B) Model performance metrics, such as AUC and KS Statistic
C) The distribution of predictor variables
D) The number of iterations performed by each model

5. Which statement is true about performing regressions in SAS Visual Statistics?

A) You can build both a linear regression or a logistic regression with a generalized linear model.
B) You can build neither a linear regression nor a logistic regression with a generalized linear model.
C) You can specify a multinomial classifier as the response variable with linear regression.
D) You can specify an ordinal classifier as the response variable with linear regression.

Solutions: