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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Prepare Inputs for Predictive Model Performance20%- Data Preparation
  • 1. Manipulate data using DATA step programming
  • 2. Identify challenges in preparing model input data
  • 3. Use loops, arrays, functions and conditional statements
- Variable Engineering and Screening
  • 1. Identify nonlinear associations using CORR procedure
  • 2. Improve predictive power of categorical variables
  • 3. Screen variables for irrelevance
  • 4. Use empirical logit plots for nonlinearity detection
Measure Model Performance25%- Performance Visualization
  • 1. Create and interpret gains charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret lift 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
- Model Assessment
  • 1. Assess classifier performance using confusion matrices
  • 2. Apply principles of honest assessment
Linear Regression20%- 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. Use REG and GLM procedures
  • 2. Fit multiple linear regression models
- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
Logistic Regression25%- 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
ANOVA10%- ANOVA Concepts and Assumptions
  • 1. Identify appropriate ANOVA methods
  • 2. Verify ANOVA assumptions
- 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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q39-Q44):

NEW QUESTION # 39
The SAS data set RESULT contains the following variables:
* Region (GrpA or GrpB)
* Sales (dollars per year)
Which SAS programs can be used to find the p-value for comparing GrpA sales with GrpB sales? (Choose two.)

Answer: B,C

Explanation:
Explanation


NEW QUESTION # 40
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?

Answer: D


NEW QUESTION # 41
A non-contributing predictor variable (Pr > |t| = 0.658) is removed from an existing multiple linear regression model.
What will be the result?

Answer: D


NEW QUESTION # 42
This question will ask you to provide missing code segments.
A logistic regression model was fit on a data set where 40% of the outcomes were events (TARGET=1) and
60% were non-events (TARGET=0). The analyst knows that the population where the model will be deployed has 5% events and 95% non-events. The analyst also knows that the company's profit margin for correctly targeted events is nine times higher than the company's loss for incorrectly targeted non-event.
Given the following SAS program:

What X and Y values should be added to the program to correctly score the data?

Answer: C


NEW QUESTION # 43
This question will ask you to provide missing code segments.
A logistic regression model was fit on a data set where 40% of the outcomes were events (TARGET=1) and
60% were non-events (TARGET=0). The analyst knows that the population where the model will be deployed has 5% events and 95% non-events. The analyst also knows that the company's profit margin for correctly targeted events is nine times higher than the company's loss for incorrectly targeted non-event.
Given the following SAS program:

What X and Y values should be added to the program to correctly score the data?

Answer: C


NEW QUESTION # 44
......

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