SASInstitute Modeling Using SAS Visual Statistics - A00-485 Free Exam Questions

A. To calculate summary statistics for a measure

B. To combine multiple categories into fewer levels

C. To transform a categorical variable into a numerical one

D. To replace dirty data with missing values

A. To exclude all predictor variables

B. To specify the distribution of the response variable

C. To assign roles to variables

D. To identify and choose relevant predictor variables

A. The resulting model is an ordinal logistic regression model with the lowest level of the response as the reference.

B. The resulting model is a multinomial logistic regression model with a generalized logit link.

C. The resulting model is a binary logistic regression model with a selected level as the event.

D. You cannot assign a response variable with 3 or more levels to the logistic regression visualization.

A. SAS Data Integration Studio

B. SAS Enterprise Guide

C. SAS Enterprise Miner

D. SAS Studio

A. Using a decision tree for data classification

B. Creating a scatter plot of data points

C. Assigning predefined labels to data points

D. Grouping similar data points together without predefined labels

A. The p-value associated with the intercept term

B. The impact of outliers on the model

C. The degree of multicollinearity among predictors

D. The goodness of fit of the model to the data

A. To determine the order of predictor variables in the model

B. To calculate the p-values of predictor variables

C. To ensure that predictor variables are not highly correlated

D. To verify if the chosen model assumptions are met and make adjustments if necessary

A. Observations with a value of 1 will be used as Training data.

B. No partition will be used because there are more than two levels.

C. Observations with a value of 0 will be used as Training data.

D. Observations with a value of 2 will be used as Training data.

A. Generalized linear model with a Normal distribution and Log Link.

B. Logistic regression model.

C. Linear regression model with Interaction effects.

D. Generalized linear model with a Poisson distribution and Identity link.

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

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

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

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

A. Exponential

B. Normal

C. Poisson

D. Logistic

A. Output

B. Input

C. Predictor

D. Target

A. Percentile changed from 5th percentile to 40th percentile

B. Misclassification was used instead of Cumulative Lift

C. Prediction Cutoff reduced from .5 to .19

D. Analyst clicked on Model 2 to highlight it

A. By changing the K-value

B. By adjusting the learning rate

C. By modifying the random seed

D. By increasing the number of iterations

A. To calculate the p-values of the predictors

B. To identify outliers in the data

C. To assess multicollinearity among predictors

D. To capture the relationship between predictors and the response variable