TRAINING / Statistical business analyst

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling

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

Duration: 3 Days

This course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, linear regression, and logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.

Learn how to

  • Generate descriptive statistics and explore data with graphs
  • Perform analysis of variance and apply multiple comparison techniques
  • Perform linear regression and assess the assumptions
  • Use regression model selection techniques to aid in the choice of predictor variables in multiple regression
  • Use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
  • Use chi-square statistics to detect associations among categorical variables
  • Fit a multiple logistic regression model.

Who should attend:

Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables


Before attending this course, you should

  • Have completed the equivalent of an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression
  • Be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS Programming 1: Essentials course.

Course Conents

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

Duration: 2 Days

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables, assessing models, treating missing values and using efficiency techniques for massive data sets.

Learn how to

  • Use logistic regression to model an individual's behavior as a function of known inputs
  • Create effect plots and odds ratio plots using ODS Statistical Graphics
  • Handle missing data values
  • Tackle multicollinearity in your predictors
  • Assess model performance and compare models.

Who should attend

Modelers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries


Before attending this course, you should

  • Have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programming 1: Essentials course
  • Have experience building statistical models using SAS software
  • Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

Course Contents:

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

SAS Certification

One Attempt of free SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Exam (Worth Rs. 11,000/- approx)

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Certification Package Fees

Rs. 72000/- plus 12.36% service tax discounted fees 62999/- Plus 12.36% service tax

Batch Detail






29 July -2 August

5 Days

INR 80899.0

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