최신 A00-485 무료덤프 - SASInstitute Modeling Using SAS Visual Statistics

A. To determine the level of significance for each variable

B. To determine the order in which variables are entered into the model

C. To indicate which variables are predictors and which are responses

D. To specify which variables are categorical and which are continuous

A. The p-values associated with the predictor variables

B. The distribution of predictor variables in the dataset

C. The linear relationship between predictors and the response

D. The presence of any outliers or patterns in prediction errors

A. To predict categorical outcomes

B. To capture non-linear relationships in data

C. To classify data into distinct groups

D. To establish a linear relationship between variables

A. Running a SQL query to generate predictions

B. Converting the code to a macro for automation

C. Opening the code file and manually executing it

D. Dragging and dropping the score code file into the project

A. The p-values associated with the predictor variables

B. The relationship between two predictor variables

C. The trade-off between false positives and false negatives at different thresholds

D. The distribution of predictor variables in the dataset

A. By calculating the distance between data points and leaf nodes

B. By concatenating the decision rules leading to a leaf node

C. By applying a clustering algorithm to leaf node values

D. By extracting the node IDs of all leaf nodes

A. An interaction can be created for a squared category variable.

B. The maximum number of interactions allowed in a model is 3.

C. Measures and categories can be combined to create an interaction.

D. Each interaction is limited to 3 effects or fewer.

A. The distribution of the response variable

B. The relative performance of the model compared to a random model

C. The correlation between predictor variables

D. The interaction effects between predictor variables

A. Use the toolbar options to filter the visualization

B. Use the filter pane to apply filters to the visualization

C. Apply filters using the interactive filter dialog in the visualization

D. Define filters directly in the data source properties

A. The learning rate of the model

B. The AUC (Area Under the Curve)

C. The R-squared value

D. The p-value of the intercept term