SASInstitute A00-240 Free Download & A00-240 Latest Exam Guide

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
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. Identify nonlinear associations using CORR procedure
  • 2. Improve predictive power of categorical variables
  • 3. Use empirical logit plots for nonlinearity detection
  • 4. Screen variables for irrelevance
Logistic Regression25%- Develop Logistic Models
  • 1. Optimize model performance through input selection
  • 2. Perform logistic regression using the LOGISTIC procedure
- Interpret and Deploy Models
  • 1. Interpret LOGISTIC procedure output
  • 2. Score new data sets using LOGISTIC and PLM procedures
ANOVA10%- ANOVA Analysis
  • 1. Analyze differences between population means using GLM and TTEST procedures
  • 2. Evaluate treatment effects
  • 3. Perform post hoc tests
  • 4. Detect and analyze factor interactions
- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
Measure Model Performance25%- Decision Optimization
  • 1. Establish effective decision cutoff values
  • 2. Evaluate scoring strategies
- Performance Visualization
  • 1. Create and interpret ROC charts
  • 2. Create and interpret gains charts
  • 3. Create and interpret lift charts
- Model Assessment
  • 1. Assess classifier performance using confusion matrices
  • 2. Apply principles of honest assessment
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data
Linear Regression20%- Interpret Regression Results
  • 1. Evaluate parameter estimates
  • 2. Interpret REG, PLM and GLM output
- Model Selection and Diagnostics
  • 1. Perform model selection using REG or GLMSELECT
  • 2. Assess model validity
  • 3. Conduct residual and diagnostic analysis
- 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 Sample Questions (Q96-Q101):

NEW QUESTION # 96
A researcher is planning a logistic regression to model the probability of disease occurrence. The researcher determines the rate of disease occurrence in the population is 1%.
For which of the following would this study be a candidate?

Answer: C


NEW QUESTION # 97
When mean imputation is performed on data after the data is partitioned for honest assessment, what is
the most appropriate method for handling the mean imputation?

Answer: B


NEW QUESTION # 98
Assume a $10 cost for soliciting a non-responder and a $200 profit for soliciting a responder. The logistic
regression model gives a probability score named P_R on a SAS data set called VALID. The VALID data
set contains the responder variable Pinch, a 1/0 variable coded as 1 for responder. Customers will be
solicited when their probability score is more than 0.05.
Which SAS program computes the profit for each customer in the data set VALID?

Answer: A


NEW QUESTION # 99
An analyst compares the mean salaries of men and women working at a company.
The SAS data set SALARY contains variables:
Gender (M or F)

Pay (dollars per year)

Which SAS programs can be used to find the p-value for comparing men's salaries with women's salaries?
(Choose two.)

Answer: A,B


NEW QUESTION # 100
The selection criterion used in the forward selection method in the REG procedure is:

Answer: C


NEW QUESTION # 101
......

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