최신SASInstitute Modeling Using SAS Visual Statistics - A00-485무료샘플문제

A. The predicted events decrease and the predicted probabilities remain unchanged.

B. The predicted events remain unchanged and the predicted probabilities decrease.

C. The predicted events remain unchanged and the predicted probabilities increase.

D. The predicted events increase and the predicted probabilities remain unchanged.

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

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

C. To determine the level of significance for each variable

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

A. The distribution of the response variable

B. The performance of models in terms of AUC

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

D. The relationship between two predictor variables

A. Group-by variable with the highest p-value

B. Fit statistics such as AIC and BIC

C. Visualization of model residuals

D. Variable Importance

A. Filter containers

B. Filtering using the toolbar options

C. Interactive filters in the visualization

D. Exclude option in filter dialogs

A. A measure of data point centrality

B. Characteristics shared by data points within a cluster

C. The number of clusters in the analysis

D. Descriptive statistics of individual data points

A. By following a path through the tree to a leaf node and returning the majority class or mean value of that node

B. By averaging the values of all leaf nodes

C. By using a weighted sum of all decision rules in the tree

D. By calculating the Euclidean distance to all tree nodes and selecting the closest one

A. To measure the balance between sensitivity and specificity

B. To assess the multicollinearity among predictors

C. To evaluate the relationship between two predictor variables

D. To calculate the p-values of predictor variables

A. To convert the decision tree into a random forest model

B. To remove all leaf nodes and keep only the root node

C. To trim branches of the tree to reduce complexity and overfitting

D. To increase the depth of the tree to improve accuracy

A. Average Sales is a significant predictor when Customer Value Level = E.

B. Customer Value Level C has no important variables associated with it.

C. Customer Value Level is not a significant predictor in this model.

D. Average Sales is an important predictor when Customer Value Level = C.

A. You can build neither a linear regression nor a logistic regression with a generalized linear model.

B. You can specify an ordinal classifier as the response variable with linear regression.

C. You can build both a linear regression or a logistic regression with a generalized linear model.

D. You can specify a multinomial classifier as the response variable with linear regression.

A. The joint impact of two or more predictors on the response variable

B. The effect of missing data on the model

C. The presence of outliers in the data

D. The multicollinearity among predictors

A. Imputing a variable

B. Aggregating a measure

C. Creating a dummy variable

D. Transforming a variable