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SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Identify nonlinear associations using CORR procedure 2. Screen variables for irrelevance 3. Use empirical logit plots for nonlinearity detection 4. Improve predictive power of categorical variables
Topic 2: Measure Model Performance	25%	- 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 - Validation and Selection 1. Compare and select predictive models 2. Perform model validation using training and validation data - Performance Visualization 1. Create and interpret gains charts 2. Create and interpret ROC charts 3. Create and interpret lift charts
Topic 3: ANOVA	10%	- ANOVA Concepts and Assumptions 1. Verify ANOVA assumptions 2. Identify appropriate ANOVA methods - ANOVA Analysis 1. Perform post hoc tests 2. Analyze differences between population means using GLM and TTEST procedures 3. Detect and analyze factor interactions 4. Evaluate treatment effects
Topic 4: 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 5: Linear Regression	20%	- Model Selection and Diagnostics 1. Assess model validity 2. Perform model selection using REG or GLMSELECT 3. Conduct residual and diagnostic analysis - Interpret Regression Results 1. Interpret REG, PLM and GLM output 2. Evaluate parameter estimates - Build Regression Models 1. Fit multiple linear regression models 2. Use REG and GLM procedures

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential 認定 A00-240 試験問題 (Q28-Q33):

質問 # 28
Refer to the exhibit:

The plots represent two models, A and B, being fit to the same two data sets, training and validation.
Model A is 90.5% accurate at distinguishing blue from red on the training data and 75.5% accurate at doing the same on validation data. Model B is 83% accurate at distinguishing blue from red on the training data and
78.3% accurate at doing the same on the validation data.
Which of the two models should be selected and why?

A. It is more complex with a higher accuracy than model B on training data.
B. It is simpler with a higher accuracy than model A on validation data.
C. It performs better on the boundary for the training data.
D. It is more complex with a higher accuracy than model A on validation data.

正解：B

質問 # 29
What is the default method in the LOGISTIC procedure to handle observations with missing data?

A. Parameters are estimated accounting for the missing values.
B. Parameter estimates are made on all available data.
C. Missing values are imputed.
D. Only cases with variables that are fully populated are used.

正解：D

解説：
Explanation/Reference:

質問 # 30
In partitioning data for model assessment, which sampling methods are acceptable? (Choose two.)

A. Stratified random sampling without replacement
B. Simple random sampling with replacement
C. Sequential random sampling with replacement
D. Simple random sampling without replacement

正解：A、D

質問 # 31
Refer to the lift chart:

At a depth of 0.1, Lift = 3.14. What does this mean?

A. Selecting the top 10% of the population scored by the model should result in 3.14 times greater accuracy than a random draw of 10%.
B. Selecting the observations with a response probability of at least 10% should result in 3.14 times greater accuracy than a random draw of 10%.
C. Selecting the observations with a response probability of at least 10% should result in 3.14 times more events than a random draw of 10%.
D. Selecting the top 10% of the population scored by the model should result in 3.14 times more events than a random draw of 10%.

正解：D

質問 # 32
PROC GLMSELECT was used for building a model predicting the natural log of a baseball player's salary from certain performance and longevity statistics. The model used backward elimination using SBC as its selection criterion. The sequence of steps is summarized in the graphic shown below:

At Step 9 number of at bats (nAtBat) was removed from the model.
Why was it removed?

A. Removing nAtBat yielded the largest improvement to SBC.
B. Removing nAtBat had the largest effect on the parameter estimate of nHits.
C. The p-Value for nAtBat was smallest.
D. The p-Value for nAtBat was largest.

正解：D

質問 # 33
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