 # Six Sigma Black Belt Level Regression Analysis

### Requirements

• 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

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 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

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/six-sigma-black-belt-level-regression-analysis/