Free SAS Statistical Business Analysis SAS9: Regression and Model Exam A00-240 Exam Practice Test

Within PROC GLM, the interaction between the two categorical predictors, Income and Gender, was shown to be significant. An item store was saved from the GLM analysis.Which statement from PROC PLM would test the significance of Gender within each level of Income and adjust for multiple tests?

• sliceby Gender / adjust=tukey;
• slice Income*Gender / sliceby=Gender adjust=tukey;
• slice Income*Gender / sliceby=Income adjust=tukey;
• sliceby Income / adjust=tukey;

An analyst knows that the categorical predictor, zip_code, is an important predictor of a binary target. However, zip_code has too many levels to be a feasible predictor in a model. The analyst uses PROC CLUSTER to implement Greenacre's method to reduce the number of categorical levels.What is the correct application of Greenacre's method in this situation?

• Clustering the levels using the target proportion for each zip_code as input.
• Clustering the levels using the zip_code values as input.
• Clustering the levels using the number of cases in each zip_code as input.
• Clustering the levels using dummy coded zip_code levels as inputs.

A confusion matrix is created for data that were oversampled due to a rare target. What values are not affected by this oversampling?

• Sensitivity and PV+
• Specificity and PV-
• PV+ and PV-
• Sensitivity and Specificity

After performing an ANOVA test, an analyst has determined that a significant effect exists due to income. The analyst wants to compare each Income to all others and wants to control for experimentwise error.Which GLM procedure statement would provide the most appropriate output?

• lsmeans Income / pdiff=control adjust=dunnett;
• lsmeans Income / pdiff=control adjust=t;
• lsmeans Income / pdiff=all adjust=tukey;
• lsmeans Income / pdiff=all adjust=t;

In order to perform honest assessment on a predictive model, what is an acceptable division between training, validation, and testing data?

• Training: 50% Validation: 0% Testing: 50%
• Training: 100% Validation: 0% Testing: 0%
• Training: 0% Validation: 100% Testing: 0%
• Training: 50% Validation: 50% Testing: 0%

What is a benefit to performing data cleansing (imputation, transformations, etc.) on data after partitioning the data for honest assessment as opposed to performing the data cleansing prior to partitioning the data?

• It makes inference on the model possible.
• It is computationally easier and requires less time.
• It omits the training (and test) data sets from the benefits of the cleansing methods.
• It allows for the determination of the effectiveness of the cleansing method.

Refer to the exhibit.Which conclusion is justified concerning Sales, comparing stores A, B, and C?Store B is significantly different from store

• Store C is significantly different from Store A.
• Store B is significantly different from store C.
• There is no significant difference between stores.

Which characteristic of Studentized residuals indicate potential outliers?

• Only studentized residuals greater than negative two
• Only studentized residuals less than negative two and greater than two
• Only studentized residuals greater than two
• Only studentized residuals less than two and greater than negative two

Which SAS program will best identify influential observations in a multiple regression application?

• Option A
• Option B
• Option C
• Option D

A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model.What will be the result?

• An increase in R-Square
• A decrease in R-Square
• A decrease in Mean Square Error
• No change in R-Square