Correlation and Regression: Six Sigma Thinking, #8

By Sumeet Savant

Understanding the concept of Correlation and Regression for Six Sigma, knowing how to perform Correlation and Regression, understanding the various types of Correlation and Regression and the differences between them has never been easier!

Have you ever wanted to perform association and causation analysis on your company's processes using Correlation and Regression?
Have you ever wanted to perform association and causation analysis on your customer's processes using Correlation and Regression?
Have you ever wanted perform predictions on your processes based on the most reliable statistical tools and models?

If the answer to any of these questions is yes, you need look no further.

Lean Six Sigma Master Black Belt Mentor and Coach, Sumeet Savant, with more than a decade of experience in executing, leading, and mentoring hundreds of Lean Six Sigma improvement projects brings to you a simple, straight forward, no nonsense book which will enable you understand the concept of Correlation and Regression to successfully utilize them to achieve the process improvements you have always dreamt of, quickly, effectively, and efficiently.

He shares his belief: "For your process improvements to be considered successful, you need statistically proven tools and models to ensure that you are addressing the right causes and are prioritizing your efforts on the most vital root causes and there is no other better means for achieving this other than the Correlation and Regression."

Do Act Now and have a LOOK INSIDE the short book to have a brief experience of the immensely valuable information it has in store for you.

With this book, you will get to,
- Know, understand, and appreciate what are Correlation, and Regression.
- Know and understand the different types of Correlation like the Positive, Negative, Perfectly Positive, Perfectly Negative, Zero, Simple, Partial, Multiple, Linear and Non Linear.
- Know and understand the Scatter Plot, and its interpretation.
- Know and understand the Correlation Coefficient and its interpretation.
- Know and understand the different types of Regression like Simple, Multiple, Linear, and Non Linear.
- Know and understand how to develop the Simple Linear Regression model.
- Know and understand how to develop the Multiple Linear Regression model.
- Know and understand how to develop the Multiple Linear Regression model using Matrices.
- Know and understand the different terms used to determine the effectiveness of the developed model like SSE, SST, RSME, RSE, and R2.
- Know and understand the other major regression models like the Polynomial, Logarithmic, Power, Exponential, and the Moving Average.
- Study more that ten case studies most relevant for the mentioned topics.
- And know all these and much more in the least number of words possible – to help you improve your processes and reduce process variations faster.

This may very well be the last book you will ever need to learn Correlation and Regression for Six Sigma and achieve overall improvements in your processes, services, and products.

We are sure about it! And, so will you be!
 

• Language: English
• Publisher: Sumeet Savant
• Release date: Nov 29, 2019
• ISBN: 9781393586555

Sumeet Savant

Sumeet Savant is a Lean Six Sigma Master Black Belt Mentor and coach, with more than a decade of experience in executing, leading and mentoring Lean Six Sigma process improvement projects. He is a BTech, MBA, and Prince certified Practitioner. He has facilitated hundreds of process improvement projects, and coached hundreds of professionals, White, Yellow, Green, and Black Belts over the years. He lives in Mumbai, India with his family.

Book preview

Table of Contents

Start

About the Author

Section I: Correlation

Correlation

Types of Correlation

Scatter Plot

Case Study 1: Correlation using Scatter Plot

Case Study 2: Correlation using Scatter Plot

Correlation Coefficient

Karl Pearson's Correlation Coefficient Formula

Case Study 3: Correlation Coefficient using Pearson's Formula

Section II: Regression

Regression

Types of Regression

Variables, Coefficients, and Residuals

Simple Linear Regression

Slope

Intercept

Line of Best Fit

Case Study 4: Line of Best Fit

Residual

Model based on the mean

Case Study 5: Base model

Sum of Squared Errors SSE

Case Study 6: Calculation of SSE

Total Sum of Squared Values SST

Case Study 7: Calculation of SST

Coefficient of Determination R Square

Case Study 8: Calculation of R Square

Simple Linear Regression Steps

Multiple Linear Regression with two independent variables

Coefficients and Intercept for Multiple Linear Regression with two independent variables

Case Study 9: Multiple Linear Regression

Multi Collinearity

Case Study 10: Multi Collinearity

Case Study 12: RSquare

RMSE, MSE, and RSquared Adjusted

Linear Regression using Matrices

Case Study 13: Linear Regression using Matrices

Case Study 14: R2 calculation using Matrices

Polynomial Regression

Logarithmic Regression

Power Regression

Exponential Regression

Moving Average Regression

Authors Note

Correlation and Regression

Six Sigma Thinking Series

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Sumeet Savant

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(c)2019, Sumeet Savant

All rights reserved

ABOUT THE AUTHOR

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Sumeet Savant is a Lean Six Sigma Master Black Belt Mentor and coach, with more than a decade of experience in executing, leading and mentoring Lean Six Sigma process improvement projects. He is a BTech, MBA, and Prince certified Practitioner. He has facilitated hundreds of process improvement projects, and coached hundreds of professionals, Yellow, Green, and Black Belts over the years. He lives in Mumbai, India with his family.

Correlation

Correlation

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Correlation is a statistical tool that helps in understanding the,

• Degree of relationship between two variables.

• Nature of relationship between two variables.

For example to check relation between price and demand.

In Lean Six Sigma Correlation