SAS Statistical Business Analyst

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

  • Introduction to Statistics
    • Examining data distributions
    • Obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
    • Render graphically in the UNIVARIATE and SGPLOT procedures
    • Constructing confidence intervals
    • Performing simple tests of hypothesis
  • Tests and Analysis of Variance
    • Tests of differences between two group means using PROC TTEST
    • One-way ANOVA with the GLM procedure
    • Multiple comparisons tests in PROC GLM
    • Two-way ANOVA with and without interactions
  • Linear Regression
    • Working with correlations
    • fitting a simple linear regression model
    • Understanding the concepts of multiple regression
    • Working with multiple models
    • Interpreting models
  • Linear Regression Diagnostics
    • Examining residuals
    • Investigating influential observations
    • Assessing collinearity
  • Categorical Data Analysis
    • Producing frequency tables
    • Examining tests for general and linear association
    • Understanding logistic regression
    • Fitting univariate and multivariate logistic regression models
Statisticians, researchers, and business analysts who use SAS programming to generate analyses
  • Should have completed the equivalent of an undergraduate course in statistics
  • Should be able to execute SAS programs and create SAS data sets
Learn to
  • Understand describe data using graphical techniques
  • Use Analysis of Varience (ANOVA)
  • 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.
Delivery Method : Classroom Training
Duration : 21 hours
Level : Fundamental
Languages : English

Predictive Modeling Using Logistic Regression

Discover Knowledge on Technology This course helps participants to get a deep understanding of how logistics regression is used for predictive analytics.
  • Introduction to Predictive Modeling
    • Business applications
    • Analytical challenges
  • Fitting the Model
    • Parameter estimation
    • Adjustments for oversampling
  • Input Data Preparation
    • Missing values
    • Categorical inputs
    • Variable clustering
    • Variable screening
    • Subset selection
  • Classifier Performance
    • ROC curves and Lift charts
    • K-S statistic
    • c statistic
    • Evaluating a series of models
Modelers, analysts and statisticians who need to build predictive models
  • Must have completed statistics course on regression
  • Experience in executing SAS programs and creating SAS data sets
  • Experience building statistical models using SAS software
Learn how to
  • Use logistic regression to model as a function of known inputs
  • Create visualizations
  • Handle missing data values
  • Tackle multicollinearity
  • Assess model performance and compare models.
Delivery Method : Classroom Training
Duration : 14 hours
Level : Intermediate
Languages : English
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