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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Measure Model Performance25%- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret gains charts
  • 3. Create and interpret ROC charts
- 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
- Validation and Selection
  • 1. Perform model validation using training and validation data
  • 2. Compare and select predictive models
Linear Regression20%- Model Selection and Diagnostics
  • 1. Conduct residual and diagnostic analysis
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Assess model validity
- Build Regression Models
  • 1. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
- Interpret Regression Results
  • 1. Evaluate parameter estimates
  • 2. Interpret REG, PLM and GLM output
ANOVA10%- ANOVA Analysis
  • 1. Detect and analyze factor interactions
  • 2. Perform post hoc tests
  • 3. Analyze differences between population means using GLM and TTEST procedures
  • 4. Evaluate treatment effects
- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
Logistic Regression25%- Develop Logistic Models
  • 1. Perform logistic regression using the LOGISTIC procedure
  • 2. Optimize model performance through input selection
- Interpret and Deploy Models
  • 1. Interpret LOGISTIC procedure output
  • 2. Score new data sets using LOGISTIC and PLM procedures
Prepare Inputs for Predictive Model Performance20%- 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
- Variable Engineering and Screening
  • 1. Use empirical logit plots for nonlinearity detection
  • 2. Screen variables for irrelevance
  • 3. Identify nonlinear associations using CORR procedure
  • 4. Improve predictive power of categorical variables

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q83-Q88):

NEW QUESTION # 83
Refer to the lift chart:

What does the reference line at lift = 1 corresponds to?

Answer: D


NEW QUESTION # 84
An analyst generates a model using the LOGISTIC procedure. They are now interested in getting the sensitivity and specificity statistics on a validation data set for a variety of cutoff values.
Which statement and option combination will generate these statistics?

Answer: B


NEW QUESTION # 85
A marketing analyst assessed the effect of web page design (A, B, or C) on customers' intent to purchase an expensive product. The focus group was divided randomly into three sub-groups, each of which was asked to view one of the web pages and then give their intent to purchase on a scale from 0 to 100. The analyst also asked the customers to give their income, which was coded as: I (lowest), II (medium), or III (highest). After analyzing the data, the analyst claimed that there was significant interaction and the webpage design mainly influenced high income people.
Which graph supports the analyst's conclusion?
A)

B)

C)

D)

Answer: C


NEW QUESTION # 86
Which statistic is based on the maximum vertical distance between the primary event EDF and the secondary event EDF?

Answer: B


NEW QUESTION # 87
The standard form of a linear regression model is:

Which statement best summarizes the assumptions placed on the errors?

Answer: C


NEW QUESTION # 88
......

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