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

• analytical challenges

Fitting the Model

• parameter estimation

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

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