A. Job Type is not a significant predictor in this model.
B. Debt to income ratio is a significant predictor when Job Type = Mgr.
C. Job Type = Office has no important variables associated with it.
D. Debt to income ratio is a significant predictor when Job Type = Sales.
A. To specify the relationship between the predictor and response variables
B. To determine the distribution of the response variable
C. To calculate the p-values of predictor variables
D. To adjust the model for multicollinearity
A. Using the logit link function to model the probability of an event
B. Assuming a linear relationship between predictors and response
C. Estimating probabilities of categorical outcomes
D. Modeling binary or ordinal outcomes
A. The distribution of residuals in the models
B. The stability of model predictions
C. The trade-off between true positives and false positives at different thresholds
D. The correlation between predictor variables
A. SAS Studio
B. SAS Enterprise Guide
C. SAS Visual Data Builder
D. SAS Enterprise Miner
B. SAS Data Integration Studio
C. SAS Enterprise Guide
A. It automatically generates new predictor variables.
B. It allows for model performance assessment on a test dataset.
C. It provides insights into the distribution of predictor variables.
D. It enables scoring of new data without the need for SAS Visual Statistics.
A. When the p-value is less than 0.05
B. When the p-value is less than 0.01
C. When the p-value is greater than 0.05
D. When the p-value is exactly 0.05
A. Analyst clicked on Model 2 to highlight it
B. Misclassification was used instead of Cumulative Lift
C. Prediction Cutoff reduced from .5 to .19
D. Percentile changed from 5th percentile to 40th percentile
A. To determine the optimal threshold for classifying outcomes
B. To calculate the p-values of predictor variables
C. To identify influential data points in the dataset
D. To divide the dataset into training and testing sets
