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SASInstitute A00-240 Exam Syllabus Topics:

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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q45-Q50):

NEW QUESTION # 45
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 dummy coded zip_code levels as inputs.
B. Clustering the levels using the number of cases in each zip_code as input.
C. Clustering the levels using the zip_code values as input.
D. Clustering the levels using the target proportion for each zip_code as input.

Answer: D

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

NEW QUESTION # 46
Customers were surveyed to assess their intent to purchase a product. An analyst divided the customers into groups defined by the company's pre-assigned market segments and tested for difference in the customers' average intent to purchase. The following is the output from the GLM procedure:

What percentage of customers' intent to purchase is explained by market segment?
Click the calculator button to display a calculator if needed.

A. 35%
B. 76%
C. 65%
D. <0.01%

Answer: B

NEW QUESTION # 47
Refer to the exhibit.

Given alpha=0.02, which conclusion is justified regarding percentage of body fat, comparing small (S), medium (M), and large (L) wrist sizes?

A. Medium wrist size is significantly different than small wrist size.
B. Large wrist size is significantly different than small wrist size.
C. There is no significant difference due to wrist size.
D. Large wrist size is significantly different than medium wrist size.

Answer: B

NEW QUESTION # 48
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 D
B. Option B
C. Option A
D. Option C

Answer: C

NEW QUESTION # 49
Refer to the exhibit:

The box plot was used to analyze daily sales data following three different ad campaigns.
The business analyst concludes that one of the assumptions of ANOVA was violated.
Which assumption has been violated and why?

A. Constant variance, because the interquartile ranges are different in different ad campaigns.
B. Normality, because the interquartile ranges are different in different ad campaigns.
C. Normality, because Prob > F < .0001.
D. Constant variance, because Prob > F < .0001.

Answer: A

NEW QUESTION # 50
......

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