SAS Business Analytics Certification

Exam topics include

 

ANOVA

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

Linear Regression

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

Logistic Regression

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

Prepare Inputs for Predictive Model Performance

  • Identify potential problems with input data.
  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions.
  • Reduce the number of categorical levels in a predictive model.
  • Screen variables for irrelevance using the CORR procedure.
  • Screen variables for non-linearity using empirical logic plots.

Measure Model Performance

  • Apply the principles of honest assessment to model performance measurement.
  • Assess classifier performance using the confusion matrix.
  • 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.

Prerequisite Basic Concepts 

  • descriptive statistics
  • inferential statistics
  • steps for conducting a hypothesis test
  • basics of using your SAS software

Introduction to Statistics 

  • examining data distributions
  • obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
  • examining data distributions graphically in the UNIVARIATE and SGPLOT procedures
  • constructing confidence intervals
  • performing simple tests of hypothesis

t Tests and Analysis of Variance 

  • performing tests of differences between two group means using PROC TEST.
  • performing one-way ANOVA with the GLM procedure.
  • performing post-hoc multiple comparisons tests in PROC GLM.
  • performing two-way ANOVA with and without interactions.

Linear Regression 

  • producing correlations with the CORR procedure.
  • fitting a simple linear regression model with the REG procedure.
  • understanding the concepts of multiple regression.
  • using automated model selection techniques in PROC REG to choose from among several candidate models.
  • interpreting models.

Linear Regression Diagnostics 

  • examining residuals
  • investigating influential observations
  • assessing collinearity

Categorical Data Analysis 

  • producing frequency tables with the FREQ procedure
  • examining tests for general and linear association using the FREQ procedure
  • understanding exact tests
  • understanding the concepts of logistic regression
  • fitting univariate and multivariate logistic regression models using the LOGISTIC procedure

Predictive Modeling 

  • business applications
  • analytical challenges

Fitting the Model 

  • parameter estimation
  • adjustments for oversampling

Preparing the Input Variables 

  • missing values
  • categorical inputs
  • variable clustering
  • variable screening
  • subset selection

Classifier Performance 

  • ROC curves and Lift charts
  • optimal cutoffs
  • K-S statistic
  • c statistic
  • profit
  • evaluating a series of models

Link: http://support.sas.com/certify/creds/sba.html#t2

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