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SAS Certified Statistical Business Analyst Using SAS 9 Training in Hyderabad India

SAS Certified Statistical Business Analyst Using SAS 9 Training in Hyderabad India

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. It covers analysis of variance, linear and logistic regression, preparing inputs for predictive models, and measuring model performance.

Duration: 40-45hrs

 Course Content:

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

 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 collinearit.
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 Using Logistic Regression
    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