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| Section | Weight | Objectives |
|---|
| Prepare Inputs for Predictive Model Performance | 20% | - Variable Engineering and Screening
- 1. Identify nonlinear associations using CORR procedure
- 2. Use empirical logit plots for nonlinearity detection
- 3. Improve predictive power of categorical variables
- 4. Screen variables for irrelevance
- Data Preparation
- 1. Identify challenges in preparing model input data
- 2. Manipulate data using DATA step programming
- 3. Use loops, arrays, functions and conditional statements
|
| ANOVA | 10% | - ANOVA Analysis
- 1. Perform post hoc tests
- 2. Evaluate treatment effects
- 3. Detect and analyze factor interactions
- 4. Analyze differences between population means using GLM and TTEST procedures
- ANOVA Concepts and Assumptions
- 1. Verify ANOVA assumptions
- 2. Identify appropriate ANOVA methods
|
| 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
|
| Linear Regression | 20% | - Model Selection and Diagnostics
- 1. Perform model selection using REG or GLMSELECT
- 2. Assess model validity
- 3. Conduct residual and diagnostic analysis
- 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
|
| Measure Model Performance | 25% | - Performance Visualization
- 1. Create and interpret ROC charts
- 2. Create and interpret gains charts
- 3. Create and interpret lift 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
|
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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q51-Q56):
NEW QUESTION # 51
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 Pearson statistics?
- A. OUTCORR=estimates
- B. OUTP=estimates
- C. OUTPUT=estimates
- D. OUTSTAT=estimates
Answer: B
NEW QUESTION # 52
Refer to the following odds ratio table:

What is a correct interpretation of the estimate?
- A. The probability of the event is 1.142 greater for each one thousand dollar increase in salary.
- B. The probability of the event is 1.142 greater for each one dollar increase in salary.
- C. The odds of the event are 1.142 greater for each one thousand dollar increase in salary.
- D. The odds of the event are 1.142 greater for each one dollar increase in salary.
Answer: C
NEW QUESTION # 53
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. Eliminate store_id as a predictor in the model because it has too many levels to be feasible.
- B. Cluster by using Greenacre's method to combine stores that are similar.
- C. Use subject matter expertise to combine stores that are similar.
- D. Randomly combine the stores into five groups to keep the stochastic variation among the observations
intact.
Answer: B,C
NEW QUESTION # 54
Refer to the exhibit:

Based upon the comparative ROC plot for two competing models, which is the champion model and why?
- A. Candidate 1, because the area outside the curve is greater
- B. Candidate 2, because the area under the curve is greater
- C. Candidate 2, because it shows less over fit than Candidate 1
- D. Candidate 1, because it is closer to the diagonal reference curve
Answer: B
Explanation:
Explanation
NEW QUESTION # 55
This question will ask you to provide a missing option.
A business analyst is investigating the differences in sales figures across 8 sales regions. The analyst is interested in viewing the regression equation parameter estimates for each of the design variables.
Which option completes the program to produce the regression equation parameter estimates?

Solve
- A. Est
- B. Solution
- C.
- D. Estimate
Answer: A
Explanation:
Explanation/Reference: https://documentation.sas.com/?
docsetId=statug&docsetTarget=statug_ods_examples06.htm&docsetVersion=14.3&locale=en
NEW QUESTION # 56
......
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