Six Sigma Black Belt Level Regression Analysis
Requirements

Real Life Scenario Based Exposure to following tools and concepts

Scatter Diagrams, Correlation, Cocorrelation & Multicollinearity

Multiple Linear Regression  Line of Best Fit, Least Sq Method, Best Subset Metho

Logistic Regression using Logit Function

Residual Analysis

Terms such as: Pearson’s Correlation, Spearman’s Rho, VIF, Rsq, Rsq (adj), Rsq (pred), S Value, Mallow’s Cp

Confidence Band and Prediction Band
Description
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 enthusia st , 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 subset method
 Residual Analysis
 Various Statistics : Rsq, Rsq(Adj), Rsq(Pred), S Value, Mallow’s Cp, VIF
 Multicollinearity
 Spearman’s Coefficient
 Logistic Regression using Logit function
 Predictive Analytics
Who this course is for:
 Six Sigma Black Belt Aspirants
 Six Sigma Aspirants, in general
 Machine Learning & Statistical Modeling Enthusiasts
https://www.udemy.com/course/sixsigmablackbeltlevelregressionanalysis/