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SASInstitute A00-240 Exam Syllabus Topics:

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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q17-Q22):

NEW QUESTION # 17
Given the following LOGISTIC procedure:

What is the difference between the datasets OUTFILEJ and OUTFILE_2?

A. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored
logits.
B. OUTFILEJ contains the final parameter estimates and Wald Chi-Square values while OUTFILE_2
contains the newly scored probabilities.
C. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored
probabilities
D. OUTFILE_1 contains the final parameter estimates while OUTFILE_2 contains the newly scored
probabilities.

Answer: D

NEW QUESTION # 18
Which of the following describes a concordant pair of observations in the LOGISTIC procedure?

A. An observation with the event has an equal probability as another observation with the event.
B. An observation with the event has a higher predicted probability than the observation without the event
C. An observation with the event has an equal predicted probability as the observation without the event.
D. An observation with the event has a lower predicted probability than the observation without the event.

Answer: B

NEW QUESTION # 19
Refer to the exhibit:

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

A. Candidate 2, because the area under the curve is greater
B. Candidate 1, because the area outside 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: A

NEW QUESTION # 20
Including redundant input variables in a regression model can:

A. Destabilize parameter estimates and increase the risk of overfitting.
B. Stabilize parameter estimates and increase the risk of overfitting.
C. Stabilize parameter estimates and decrease the risk of overfitting.
D. Destabilize parameter estimates and decrease the risk of overfitting.

Answer: A

NEW QUESTION # 21
Which SAS program will divide the original data set into 60% training and 40% validation data sets, stratified by county?

A. Option B
B. Option A
C. Option C
D. Option D

Answer: C

NEW QUESTION # 22
......

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