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- Real Life Scenario Based Exposure to following tools and concepts
- Scatter Diagrams, Correlation, Co-correlation & Multicollinearity
- Multiple Linear Regression – Line of Best Fit, Least Sq Method, Best Sub-set Metho
- Logistic Regression using Logit Function
- Residual Analysis
- Terms such as: Pearson’s Correlation, Spearman’s Rho, VIF, R-sq, R-sq (adj), R-sq (pred), S Value, Mallow’s Cp
- Confidence Band and Prediction Band
If you are a Six Sigma Black Belt Aspirant or simply a Six Sigma Aspirant, you will find this course of real help. Here’s why: Regression Analysis is a topic of importance in ASQ and IASSC Certification Tests. With this course, you will be able to answer quite a few questions and easily add few marks. That’s guaranteed!
If you a machine learning enthusiast, then you already know that one of the foundation pillars of Machine Learning & Predictive Modeling is Statistical Modeling (& Regression Analysis). If you don’t have a formal education in statistics or modeling, but have a strong programming background, this course will serve as a primer, explaining the concepts, (without coding).
Of course, in Machine Learning there are other models & algorithms that is not in the scope of this course.
What are you going to get:
- Correlation & Scatter Diagram
- Single Linear Regression using Line of Best Fit
- Multiple Linear Regression with Best sub-set method
- Residual Analysis
- Various Statistics : R-sq, R-sq(Adj), R-sq(Pred), S Value, Mallow’s Cp, VIF
- Multi-collinearity
- Spearman’s Coefficient
- Logistic Regression using Logit function
- Predictive Analytics
- Six Sigma Black Belt Aspirants
- Six Sigma Aspirants, in general
- Machine Learning & Statistical Modeling Enthusiasts
