Statistical Learning
This course is an advanced journey into the creation of mathematical models. Specifically, the topics of ANOVA and especially regression are extended to much more advanced techniques. The scope is the creation of models able to explain one or more responses on the basis of a set of descriptors. The course is really border line between statistical learning and machine learning, as regression methods belong to both worlds. Definitely a distinctive and professional growth in your regression skills!
Topics include
- What is statistical learning
- Random and fixed factors using ANOVA
- Crossed and nested data structures
- Modeling the effect of a covariate on a response
- Using MANOVA to simultaneously analyse multiple correlated responses
- Using transformations to manage violations of basic assumptions
- The many ways for regression modelling
- Tree based regressions
- PLS vs OLS regression
- Ridge and Lasso
- MARS (Multivariate Adaptive Regression Spline)
- Non linear regression
- Logistic regression
What you will be able to do
- Create non linear models
- Use logistic regression
- Create regression models for complex systems
- Study and manage covariates
- Understand the real assumptions behind statistical modelling
Duration
1 day.
Pre requisites
Basic Statistics.
Available based on
- TIBCO Statistica
- Minitab
- R
- Python
Audience
This course is designed for statisticians, data scientists and data analysts, or for those who need to create more sophisticated models to understand and explain complex systems.
