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Multivariate Statistics for Understanding Complex Data
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Multivariate Statistics for Understanding Complex Data
MULT : 
This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. The course emphasizes understanding the results of the analysis and presenting your conclusions with graphs.
Learn How To
  • make sense of the math behind many multivariate statistical analyses
  • reduce dimensionality with principal components analysis
  • identify latent variables with exploratory factor analysis and factor rotation
  • understand individual preferences with qualitative preference analysis
  • explain associations among many categories with correspondence analysis
  • finds patterns of association among different sets of continuous variables with canonical correlation analysis
  • explain differences among groups in terms of many predictor variables through canonical discriminant analyses
  • classify observations into groups with linear and quadratic discriminant analyses
  • fit complex multivariate predictive models with partial least squares regression analysis.
    • Who Should Attend
    Business analysts, social science researchers, marketers, and statisticians who want to use SAS to make sense of highly dimensional multivariate data
    Prerequisites
    Before attending this course, you should be familiar with statistical concepts such as hypothesis testing, linear models, and collinearity concepts in regression. You should have an understanding of the topics taught in Statistics 2: ANOVA and Regression or equivalent.
    SAS Products Covered
    SAS/STAT
    Course Outline
    Overview of Multivariate Methods
  • examples of multivariate analyses
  • matrix algebra concepts
    • Principal Components Analysis Using the PRINCOMP procedure
  • principal component analysis for dimension reduction
    • Exploratory Factor Analysis Using the FACTOR Procedure
  • factor analysis for latent variable measurement
  • factor rotation
    • Multidimensional Preference Analysis Using the PRINQUAL and TRANSREG procedures
  • plotting high-dimensional preference data
  • mapping preferences to other characteristics
    • Correspondence Analysis Using the CORRESP Procedure
  • understanding complex associations among categorical variables
    • Canonical Variate Analysis Using the CANCORR and CANDISC Procedures
  • multivariate dimensions reduction for two sets of variables
    • Discriminant Function Analysis Using the DISCRIM Procedure
  • classification into groups
  • linear discriminant analysis
  • quadratic discriminant analysis
  • empirical validation
    • Partial Least Squares Regression Using the PLS Procedure
  • PLS for one target variable
  • PLS for many targets
  • PLS for predictive modeling
    • Live Class Schedule
    Duration: 40.0 hours
    Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
    Live Class Schedule SOLD SEPARATELY
    DATES ▼ LOCATION ▲
    ▼         		TIME ▲
    ▼         		LANGUAGE EVENT FEE
    15-17,20-22 OCT 2025 Live Web, US 1:00 PM-4:30 PM EDT English 3,000 USD
    Private Training
    Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
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    Mentoring Services
    Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.
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