Six Sigma Black Belt Level Regression Analysis – Free Udemy Courses
Get ready for ASQ & IASSC Six Sigma Black Belt Certification BoK Topic | Get a Primer on Predictive Modeling
What you’ll learn
Six Sigma Black Belt Level Regression Analysis – Free Udemy Courses
- Build Predictive Models based on Multiple Linear and Logistic Regression
- Analyze and Interpret results of Regression Models
Requirements
-
Real Life Scenario Based Exposure to the 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 a few marks. That’s guaranteed!
If you are 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 some other models & algorithms are 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 the Logit function
- Predictive Analytics
Who this course is for:
- Six Sigma Black Belt Aspirants
- Six Sigma Aspirants, in general
- Machine Learning & Statistical Modeling Enthusiasts
Add Comment