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SASInstitute A00-240 Exam Syllabus Topics:

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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q37-Q42):

NEW QUESTION # 37
A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model.
What will be the result?

A. A decrease in Mean Square Error
B. A decrease in R-Square
C. An increase in R-Square
D. No change in R-Square

Answer: C

NEW QUESTION # 38
Consider scoring new observations in the SCORE procedure versus the SCORE statement in the LOGISTIC procedure.
Which statement is true?

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

Answer: D

NEW QUESTION # 39
Refer to the exhibit:

SAS output from the RSOUARE selection method, within the REG procedure, is shown. The top two
models in each subset are given.
Based on the AIC statistic, which model is the champion model?

A. Age Weight RunTime RunPulse RestPulse MaxPulse
B. Age Weight RunTime RunPulse MaxPulse
C. RunTime
D. RestPulse

Answer: B

NEW QUESTION # 40
An analyst fits a logistic regression model to predict whether or not a client will default on a loan. One of the predictors in the model is agent, and each agent serves 15-20 clients each. The model fails to converge. The analyst prints the summarized data, showing the number of defaulted loans per agent. See the partial output below:

What is the most likely reason that the model fails to converge?

A. There is collinearity among the predictors.
B. There are missing values in the data.
C. There is quasi-complete separation in the data.
D. There are too many observations in the data.

Answer: C

NEW QUESTION # 41
A company has branch offices in eight regions. Customers within each region are classified as either "High Value" or "Medium Value" and are coded using the variable name VALUE. In the last year, the total amount of purchases per customer is used as the response variable.
Suppose there is a significant interaction between REGION and VALUE. What can you conclude?

A. Regions with higher average purchases have more high value customers.
B. The difference between average purchases for medium and high value customers depends on the region.
C. Regions with higher average purchases have more medium value customers.
D. More high value customers are found in some regions than others.

Answer: B

Explanation:
Explanation/Reference:

NEW QUESTION # 42
......

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