SAS Predictive Modeling

SAS Predictive Modeling

Learn To Discover Latest Technology This course is for participants will learn to apply different statistical models to the available data and build data science capability using SAS Analytics Pro, SAS Enterprise Guide.
  • Introduction to Statistics
    • Fundamental statistical concepts
    • Examining distributions
    • Describing categorical data
    • Constructing confidence intervals
    • Performing simple tests of hypothesis
  • Analysis of Variance (ANOVA)
    • Performing one-way ANOVA
    • Performing multiple comparisons
    • Performing two-way ANOVA with and without interactions
  • Regression
    • Producing correlations
    • Fitting a simple linear regression model
    • Understanding multiple regression
    • Building and interpreting models
    • Understanding regression techniques
    • Exploring stepwise selection techniques
  • Regression Diagnostics
    • Examining residuals
    • Investigating influential observations and collinearity
  • Categorical Data Analysis
    • Describing categorical data
    • Tests for general and linear association
    • Understanding logistic regression and multiple logistic regression
    • Performing backward elimination with logistic regression
Statisticians and business analysts who want to use a point-and-click interface to SAS
  • Familiarity with both SAS Enterprise Guide and basic statistical concepts
  • Should have completed an undergraduate course in statistics
  • Able to perform analysis and create data sets with SAS Enterprise Guide software
  • Generate descriptive statistics and explore data with graphs
  • Perform analysis of variance (ANOVA)
  • Perform linear regression
  • Learn how to identify potential outliers in multiple regression
  • Use chi-square statistics to detect associations among categorical variables
  • fit a multiple logistic regression model.
This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 7.1 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course. An e-course is also available for SAS Enterprise Guide 5.1 and SAS Enterprise Guide 4.3.
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 Days
Level : Advanced
Languages : English

Applied Analytics Using SAS Enterprise Miner

Learn To Discover Latest Technology This course is for participants will learn to build powerful visualization for the performed analytics using SAS Enterprise Miner 14.1
  • Introduction to SAS Enterprise Miner
  • Introduction to Predictive Modeling
    • Predictive Modeling Fundamentals and Decision Trees
    • Cultivating decision trees
    • Optimizing the complexity of decision trees
  • Regression Analysis
    • Working with regression data
    • Optimizing regression complexity
    • Interpreting regression models
    • Transforming inputs
    • Working with categorical data inputs
  • Neural Networks and Other Modeling Tools
    • Introduction to neural network models
    • Input selection
    • Training boundaries
  • Model Assessment
    • Model fitting
    • Statistical graphics
    • Adjusting for separate sampling
    • Working with profit matrices
  • Model Implementation
    • Internally scored data sets
    • Score code modules
  • Introduction to Pattern Discovery - Cluster Analysis
Data analysts, qualitative experts, and business analysts who wish to use SAS Enterprise Miner
  • Should be acquainted with Microsoft Windows and Windows software
  • Should have at least an introductory-level familiarity with basic statistics and regression modelling
  • Previous SAS software experience is helpful but not required.
Learn how to
  • Build and understand predictive models such as decision trees and regression models
  • Compare and explain complex models
  • Modify data for better analysis results
  • Apply association and sequence discovery to transaction data
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 Days
Level : Advanced
Languages : English
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