SASInstitute A00-240 SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Exam Questions Get Excellent Scores

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SASInstitute A00-240 Exam Syllabus Topics:

Section	Objectives
Topic 1: Model Diagnostics and Validation	- Goodness-of-fit measures - Influence and leverage
Topic 2: ANOVA and General Linear Models	- GLM procedures in SAS - One-way and multi-way ANOVA
Topic 3: Multiple Regression	- Interaction effects - Multicollinearity diagnostics - Model building and selection
Topic 4: Logistic Regression	- Binary logistic regression - Model fit and diagnostics
Topic 5: Simple Linear Regression	- Estimation and inference - Residual analysis - Model assumptions and interpretation

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q90-Q95):

NEW QUESTION # 90
Consider scoring new observations in the SCORE procedure versus the SCORE statement in the LOGISTIC procedure.
Which statement is true?

A. The SCORE statement in the LOGISTIC procedure returns only predicted probabilities, whereas the SCORE procedure returns only predicted logits.
B. The SCORE procedure and the SCORE statement in the LOGISTIC procedure produce the same output.
C. Unlike the SCORE procedure, the SCORE statement in the LOGISTIC procedure produces both predicted probabilities and predicted logits.
D. The SCORE statement in the LOGISTIC procedure returns only predicted logits, whereas the SCORE procedure returns only predicted probabilities.

Answer: A

NEW QUESTION # 91
There are missing values in the input variables for a regression application.
Which SAS procedure provides a viable solution?

A. CLUSTER
B. GLM
C. STDI2E
D. VARCLUS

Answer: C

NEW QUESTION # 92
Refer to the exhibit:

Based upon the comparative ROC plot for two competing models, which is the champion model and why?

A. Candidate 1, because the area outside the curve is greater
B. Candidate 2, because the area under the curve is greater
C. Candidate 2, because it shows less over fit than Candidate 1
D. Candidate 1, because it is closer to the diagonal reference curve

Answer: B

NEW QUESTION # 93
What is a drawback to performing data cleansing (imputation, transformations, etc.) on raw data prior to
partitioning the data for honest assessment as opposed to performing the data cleansing after partitioning
the data?

A. It omits the training (and test) data sets from the benefits of the cleansing methods.
B. It violates assumptions of the model.
C. There is no ability to compare the effectiveness of different cleansing methods.
D. It requires extra computational effort and time.

Answer: C

NEW QUESTION # 94
When mean imputation is performed on data after the data is partitioned for honest assessment, what is
the most appropriate method for handling the mean imputation?

A. The sample means from the validation data set are applied to the training and test data sets.
B. The sample means from each partition of the data are applied to their own partition.
C. The sample means from the training data set are applied to the validation and test data sets.
D. The sample means from the test data set are applied to the training and validation data sets.

Answer: C

NEW QUESTION # 95
......

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