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SASInstitute A00-240 Exam Syllabus Topics:

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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q88-Q93):

NEW QUESTION # 88
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 are missing values in the data.
B. There is quasi-complete separation in the data.
C. There is collinearity among the predictors.
D. There are too many observations in the data.

Answer: B

NEW QUESTION # 89
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?

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

Answer: A

Explanation:
Explanation/Reference: https://support.sas.com/resources/papers/proceedings/proceedings/sugi31/079-31.pdf

NEW QUESTION # 90
The question will ask you to provide a missing statement. Given the following SAS program:

Which SAS statement will complete the program to correctly score the data set NEW_DATA?

A. Scoredata=MYDIR,NEW DATA out=scores;
B. Scoredata data=MYDIR.NEW_DATA output=scores;
C. Scoredata data=MYDIR.NEW_DATA out=scores;
D. Scoredata=HYDIR.NEU_DATA output=scores;

Answer: A

NEW QUESTION # 91
A marketing campaign will send brochures describing an expensive product to a set of customers. The
cost for mailing and production per customer is $50. The company makes $500 revenue for each sale.
What is the profit matrix for a typical person in the population?

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

Answer: B

NEW QUESTION # 92
Refer to the REG procedure output:

The Intercept estimate is interpreted as:

A. The predicted value of the response when all predictors are at their minimum values.
B. The predicted value of the response when all predictors = 0.
C. The predicted value of the response when all the predictors are at their current values.
D. The predicted value of the response when all predictors are at their means.

Answer: B

NEW QUESTION # 93
......

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