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Topics of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Dumps will include the following topics:

1. ANOVA - 10%

  • Verify the assumptions of ANOVA
  • Perform ANOVA post hoc test to evaluate treatment effect
  • Analyze differences between population means using the GLM and TTEST procedures
  • Detect and analyze interactions between factors

2. Linear Regression - 20%

  • Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
  • Assess the validity of a given regression model through the use of diagnostic and residual analysis
  • Fit a multiple linear regression model using the REG and GLM procedures
  • Use the REG or GLMSELECT procedure to perform model selection

3. Logistic Regression - 25%

  • Score new data sets using the LOGISTIC and PLM procedures
  • Perform logistic regression with the LOGISTIC procedure
  • Interpret the output of the LOGISTIC procedure
  • Optimize model performance through input selection

4. Prepare Inputs for Predictive Model Performance - 20%

  • Screen variables for irrelevance and non-linear association using the CORR procedure
  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
  • Identify the potential challenges when preparing input data for a model
  • Improve the predictive power of categorical inputs
  • Screen variables for non-linearity using empirical logit plots

5. Measure Model Performance - 25%

  • Assess classifier performance using the confusion matrix
  • Apply the principles of honest assessment to model performance measurement
  • Model selection and validation using training and validation data
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Establish effective decision cut-off values for scoring

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How to study the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

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Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html

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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

It is universally acknowledged that SASInstitute certification can help present you as a good master of some knowledge in certain areas, and it also serves as an embodiment in showcasing one's personal skills. However, it is easier to say so than to actually get the SASInstitute certification. We have to understand that not everyone is good at self-learning and self-discipline, and thus many people need outside help to cultivate good study habits, especially those who have trouble in following a timetable. To handle this, our A00-240 test training will provide you with a well-rounded service so that you will not lag behind and finish your daily task step by step. At the same time, our A00-240 study torrent will also save your time and energy in well-targeted learning as we are going to make everything done in order that you can stay focused in learning our A00-240 study materials without worries behind. We are so honored and pleased to be able to read our detailed introduction and we will try our best to enable you a better understanding of our A00-240 test training better.

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
Topic 2: Prepare Inputs for Predictive Model Performance20%- 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
- Variable Engineering and Screening
  • 1. Improve predictive power of categorical variables
  • 2. Identify nonlinear associations using CORR procedure
  • 3. Use empirical logit plots for nonlinearity detection
  • 4. Screen variables for irrelevance
Topic 3: Linear Regression20%- 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
- Model Selection and Diagnostics
  • 1. Assess model validity
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Conduct residual and diagnostic analysis
Topic 4: ANOVA10%- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
- ANOVA Analysis
  • 1. Detect and analyze factor interactions
  • 2. Perform post hoc tests
  • 3. Evaluate treatment effects
  • 4. Analyze differences between population means using GLM and TTEST procedures
Topic 5: Measure Model Performance25%- 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 lift 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

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