### E-learning

###### This self-paced learning is designed for individuals who need a solid foundation in using SAS software to conduct and interpret complex statistical data analysis and who can learn autonomously. The included content helps you prepare for the SAS Certified Statistical Business Analyst Using SAS(R)9: Regression and Modeling credential. 4.9 / 5.0 4.9 / 5.0

## Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

###### 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
###### Tests and Analysis of Variance
• performing tests of differences between two group means using PROC TTEST
• 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 Using Logistic Regression

###### 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

#### Course Batch Details  ### Sample Certificate ## Get In Touch

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