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SASInstitute A00-240 Exam Syllabus Topics:

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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 quasi-complete separation in the data.
B. There are too many observations in the data.
C. There is collinearity among the predictors.
D. There are missing values in the data.

Answer: A

NEW QUESTION # 13
Given the following SAS data set TEST:

Which SAS program is NOT a correct way to create dummy variables?

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

Answer: C

NEW QUESTION # 14
This question will ask you to provide a missing option.
Given the following SAS program:

What option must be added to the program to obtain a data set containing Spearman statistics?

A. OUTCORR=estimates
B. OUTS=estimates
C. OUTPUT=estimates
D. OUT=estimates

Answer: C

NEW QUESTION # 15
An analyst knows that the categorical predictor, storeId, is an important predictor of the target.
However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.
What are the two most effective ways to address the problem? (Choose two.)

A. Use subject matter expertise to combine stores that are similar.
B. Randomly combine the stores into five groups to keep the stochastic variation among the observations intact.
C. Eliminate store_id as a predictor in the model because it has too many levels to be feasible.
D. Cluster by using Greenacre's method to combine stores that are similar.

Answer: A,D

NEW QUESTION # 16
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. More high value customers are found in some regions than others.
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. Regions with higher average purchases have more high value customers.

Answer: B

Explanation:
Explanation/Reference:

NEW QUESTION # 17
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