Exam topics include
- 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.
- 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.
- 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.
- 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
- 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
- ROC curves and Lift charts
- optimal cutoffs
- K-S statistic
- c statistic
- evaluating a series of models
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