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SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Analysis of Variance (ANOVA)	10%	- Perform post-hoc tests - Use PROC GLM for ANOVA - Assess ANOVA assumptions - Analyze factor interactions
Topic 2: Logistic Regression	25%	- Interpret odds ratios and model coefficients - Select and optimize input variables - Build models using PROC LOGISTIC - Score new datasets with LOGISTIC and PLM
Topic 3: Measure and Evaluate Model Performance	25%	- Use confusion matrices and classification metrics - Apply honest assessment principles - Determine optimal decision cut-off values - Validate models with training/validation data - Create and interpret ROC, lift, and gains charts
Topic 4: Prepare Inputs for Predictive Modeling	20%	- Transform categorical variables - Identify data preparation challenges - Manipulate data using DATA step - Screen variables with CORR procedure and empirical logit plots
Topic 5: Linear Regression	20%	- Conduct residual and diagnostic analysis - Perform model selection with GLMSELECT - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q64-Q69):

NEW QUESTION # 64
In partitioning data for model assessment, which sampling methods are acceptable? (Choose two.)

A. Simple random sampling with replacement
B. Simple random sampling without replacement
C. Stratified random sampling without replacement
D. Sequential random sampling with replacement

Answer: B,C

NEW QUESTION # 65
A researcher has several variables that could be possible predictors for the final model. There is interest in checking all 2-way interactions for possible entry to the model. The researcher has decided to use forward selection within PROC LOGISTIC. Fill in the missing code option that will ensure that all 2-way interactions will be considered for entry.

A. include = 5
B. start = 4
C. start = 5
D. include = 4

Answer: A

NEW QUESTION # 66
When working with smaller data sets (N<200), which method is preferred to perform honest assessment?

A. Cross validation using 4th quartile observations
B. Training: 40% Validation: 30% Testing: 30%
C. K-fold cross validation
D. Use the AIC goodness of fit statistic

Answer: A

NEW QUESTION # 67
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 logits, whereas the SCORE procedure returns only predicted probabilities.
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 probabilities, whereas the SCORE procedure returns only predicted logits.

Answer: D

Explanation:
Explanation/Reference:

NEW QUESTION # 68
An analyst generates a model using the LOGISTIC procedure. They are now interested in getting the
sensitivity and specificity statistics on a validation data set for a variety of cutoff values.
Which statement and option combination will generate these statistics?

A. Score data=valid1 outroc=roc;
B. mode1 resp(event= '1') = gender region/outroc=roc;
C. Score data=valid1 out=roc;
D. mode1 resp(event"1") = gender region/ out=roc;

Answer: A

NEW QUESTION # 69
......

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