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SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: ANOVA	10%	- ANOVA Concepts and Assumptions 1. Verify ANOVA assumptions 2. Identify appropriate ANOVA methods - ANOVA Analysis 1. Analyze differences between population means using GLM and TTEST procedures 2. Perform post hoc tests 3. Detect and analyze factor interactions 4. Evaluate treatment effects
Topic 2: Linear Regression	20%	- Interpret Regression Results 1. Evaluate parameter estimates 2. Interpret REG, PLM and GLM output - Model Selection and Diagnostics 1. Assess model validity 2. Perform model selection using REG or GLMSELECT 3. Conduct residual and diagnostic analysis - Build Regression Models 1. Fit multiple linear regression models 2. Use REG and GLM procedures
Topic 3: Logistic Regression	25%	- Interpret and Deploy Models 1. Interpret LOGISTIC procedure output 2. Score new data sets using LOGISTIC and PLM procedures - Develop Logistic Models 1. Optimize model performance through input selection 2. Perform logistic regression using the LOGISTIC procedure
Topic 4: Measure Model Performance	25%	- Model Assessment 1. Apply principles of honest assessment 2. Assess classifier performance using confusion matrices - Performance Visualization 1. Create and interpret gains charts 2. Create and interpret lift charts 3. Create and interpret ROC charts - Validation and Selection 1. Compare and select predictive models 2. Perform model validation using training and validation data - Decision Optimization 1. Evaluate scoring strategies 2. Establish effective decision cutoff values
Topic 5: Prepare Inputs for Predictive Model Performance	20%	- Data Preparation 1. Identify challenges in preparing model input data 2. Use loops, arrays, functions and conditional statements 3. Manipulate data using DATA step programming - Variable Engineering and Screening 1. Improve predictive power of categorical variables 2. Identify nonlinear associations using CORR procedure 3. Screen variables for irrelevance 4. Use empirical logit plots for nonlinearity detection

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q20-Q25):

NEW QUESTION # 20
The selection criterion used in the forward selection method in the REG procedure is:

A. Mallows' Cp
B. SLE
C. Adjusted R-Square
D. AIC

Answer: B

NEW QUESTION # 21
A financial services manager wants to assess the probability that certain clients will default on their Home
Equity Line of Credit (HELOC). A former employee left the code listed below.

The training data set is named HELOC, while a similar data set of more recent clients is named
RECENT_HELOC. Which SAS data steps will calculate the predicted probability of default on recent
clients? (Choose two.)

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

Answer: B,D

NEW QUESTION # 22
Assume a $10 cost for soliciting a non-responder and a $200 profit for soliciting a responder. The logistic regression model gives a probability score named P_R on a SAS data set called VALID. The VALID data set contains the responder variable Pinch, a 1/0 variable coded as 1 for responder. Customers will be solicited when their probability score is more than 0.05.
Which SAS program computes the profit for each customer in the data set VALID?

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

Answer: D

NEW QUESTION # 23
Which SAS program will divide the original data set into 60% training and 40% validation data sets,
stratified by county?

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

Answer: B

NEW QUESTION # 24
Refer to the exhibit.

Based on the control plot, which conclusion is justified regarding the means of the response?

A. All groups are significantly different from each other.
B. No groups are significantly different from each other.
C. 2XL is significantly different from all other groups.
D. Only XL and 2XL are not significantly different from each other.

Answer: D

NEW QUESTION # 25
......

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