SAS Certified Statistical Business Analyst Using SAS 9

This course is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis.

Duration 5 Days
Certificate SAS Global
Language English

Fees 60000 + taxes


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Course Description

Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables; as well as modelers and analysts who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries

About Program

This course is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis. It covers analysis of variance, linear and logistic regression, preparing inputs for predictive models, and measuring model performance.

Format of Training

All the benefits of the classroom without the travel:

Led by an expert instructor who can virtually look over your shoulder. Discuss, share, exchange ideas with students from different countries

Classroom training options include courses offered in our regional training centers or via our Live Web classroom.

Taught by certified instructors at high-tech facilities across the country

  • A SAS expert at your side.
  • Focused learning away from the office
  • Networking opportunities
  • State-of-the-art facilities
  • Electronic course notes downloadable to your device and permission to print
  • Business Knowledge Series: in-depth courses on the latest business topics
  • We offer Connected Classes! Watch for courses in Cary, New York, Arlington, Dallas and San Francisco that connect remote students via our Live Web classroom.

Prerequisite

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.

Training Features

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

    Predictive Modeling Using Logistic Regression

    • 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.

Course Curriculum

Course Overview and Review of Concepts

  • descriptive statistics
  • inferential statistics
  • examining data distributions
  • obtaining and interpreting sample statistics using the UNIVARIATE procedure
  • examining data distributions graphically in the UNIVARIATE and FREQ procedures
  • constructing confidence intervals
  • performing simple tests of hypothesis
  • performing tests of differences between two group means using PROC TTEST

ANOVA and Regression

  • performing one-way ANOVA with the GLM procedure
  • performing post-hoc multiple comparisons tests in PROC GLM
  • producing correlations with the CORR procedure
  • fitting a simple linear regression model with the REG procedure

More Complex Linear Models

  • performing two-way ANOVA with and without interactions
  • understanding the concepts of multiple regression

Model Building and Effect Selection

  • automated model selection techniques in PROC GLMSELECT to choose from among several candidate models
  • interpreting and comparison of selected models

Model Post-Fitting for Inference

  • examining residuals
  • investigating influential observations
  • assessing collinearity

Model Building and Scoring for Prediction

  • understanding the concepts of predictive modeling
  • understanding the importance of data partitioning
  • understanding the concepts of scoring
  • obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM

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
  • using automated model selection techniques in PROC LOGISTIC including interaction terms
  • obtaining predictions (scoring) for new data using PROC PLM

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

Course Fees

Live Web

  

  

60000

+ Applicable Taxes
Enroll Now

Classroom

  

  

60000

+ Applicable Taxes
Enroll Now