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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q76-Q81):

NEW QUESTION # 76
There are missing values in the input variables for a regression application.
Which SAS procedure provides a viable solution?

A. GLM
B. STDI2E
C. VARCLUS
D. CLUSTER

Answer: B

NEW QUESTION # 77
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 too many observations in the data.
B. There is quasi-complete separation in the data.
C. There are missing values in the data.
D. There is collinearity among the predictors.

Answer: B

NEW QUESTION # 78
Which statistic is based on the maximum vertical distance between the primary event EDF and the secondary event EDF?

A. Max EDF
B. KS
C. SBC
D. Brier Score

Answer: C

NEW QUESTION # 79
Which SAS program will detect collinearity in a multiple regression application?

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

Answer: D

NEW QUESTION # 80
Given the following GLM procedure output:

Which statement is correct at an alpha level of 0.05?

A. Gender should not be removed due to its involvement in the significant interaction.
B. Gender should be removed because it is non-significant.
C. School*Gender should be removed because it is non-significant.
D. School should be removed because it is significant.

Answer: A

NEW QUESTION # 81
......

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