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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

Preparation of certification exams could be covered with two resource types . The first one are the study guides, reference books and study forums that are elaborated and appropriate for building information from ground up. Apart from them video tutorials and lectures are a good option to ease the pain of through study and are relatively make the study process more interesting nonetheless these demand time and concentration from the learner. Smart candidates who wish to create a solid foundation altogether examination topics and connected technologies typically mix video lectures with study guides to reap the advantages of each but practice exams or practice exam engines is one important study tool which goes typically unnoted by most candidates. Practice exams are designed with our experts to make exam prospects test their knowledge on skills attained in course, as well as prospects become comfortable and familiar with the real exam environment. Statistics have indicated exam anxiety plays much bigger role of students failure in exam than the fear of the unknown. iPassleader expert team recommends preparing some notes on these topics along with it don't forget to practice A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam exam dumps which had been written by our expert team, each of these can assist you loads to clear this exam with excellent marks.

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

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: 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 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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