Multiple Regression with Minitab

Multiple Regression with Minitab, available at $84.99, has an average rating of 4.8, with 47 lectures, 7 quizzes, based on 117 reviews, and has 714 subscribers.

You will learn about Master the fundamentals of Regression Analysis, including both linear and polynomial regression techniques. Perform and interpret the results of multiple regression analysis using Minitab with confidence. Start with basics, understanding scatter plots and simple regression with one predictor, and progressively move to more complex scenarios. Gain a practical view of regression modeling, analyzing real-world examples like predicting insurance costs based on various factors. Add additional predictors to your regression models and understand the significance of R-squared and adjusted R-squared values. Select the best features using Best Subsets and Stepwise selection approaches to optimize your models. Learn about training and test data, including validation set approach, leave-one-out cross-validation, and K-Fold validation. Develop a solid foundation in multiple regression to boost your career in data analysis and Six Sigma projects. This course is ideal for individuals who are Six Sigma professionals who want to take their understanding of Regression Analysis to the next level or Anyone who wants to get a more in-depth insight into interpreting the Regression results It is particularly useful for Six Sigma professionals who want to take their understanding of Regression Analysis to the next level or Anyone who wants to get a more in-depth insight into interpreting the Regression results.

Enroll now: Multiple Regression with Minitab

Summary

Title: Multiple Regression with Minitab

Price: $84.99

Average Rating: 4.8

Number of Lectures: 47

Number of Quizzes: 7

Number of Published Lectures: 47

Number of Published Quizzes: 7

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Master the fundamentals of Regression Analysis, including both linear and polynomial regression techniques.
Perform and interpret the results of multiple regression analysis using Minitab with confidence.
Start with basics, understanding scatter plots and simple regression with one predictor, and progressively move to more complex scenarios.
Gain a practical view of regression modeling, analyzing real-world examples like predicting insurance costs based on various factors.
Add additional predictors to your regression models and understand the significance of R-squared and adjusted R-squared values.
Select the best features using Best Subsets and Stepwise selection approaches to optimize your models.
Learn about training and test data, including validation set approach, leave-one-out cross-validation, and K-Fold validation.
Develop a solid foundation in multiple regression to boost your career in data analysis and Six Sigma projects.

Who Should Attend

Six Sigma professionals who want to take their understanding of Regression Analysis to the next level
Anyone who wants to get a more in-depth insight into interpreting the Regression results

Target Audiences

Six Sigma professionals who want to take their understanding of Regression Analysis to the next level
Anyone who wants to get a more in-depth insight into interpreting the Regression results

In this course, I will teach you one of the most commonly used analytical techniques: Regression Analysis.

This course covers the top of multiple regression analysis at the Six Sigma Master Black Belt level.

I will use Minitab 19 to perform the analysis. The focus of my teaching will be on explaining the concepts and on analyzing and interpreting the results of the analysis.

The course starts from the basics, covering the scatter plot and learning the simple regression with just one predictor. The analysis is conducted in Minitab 19, and the results of the output are explained in detail. To understand the concept, a simple example of hours of studies and marks obtained in the exam is taken. As you move through the course the example becomes more complex. In the end, we analyzed and modelled the insurance cost based on various factors.

This course also covers hypothesis testing, understanding the p-value to interpret the result.

Later, additional predictors are added to the regression model. The performance of the model is understood by interpreting the value of R-squared and adjusted R-squared.

The following concepts are covered in this course:

Simple Linear Regression
Multiple Regression
Nonlinear Regression (Polynomial)
Bias Variance Trade-off
Selecting features using Best Subsets and Stepwise selection approaches
Identifying Outliers
Training and Test Data – Validation set approach, Leave one out cross-validation and K-Fold Validation.
Predicting Response
Project Work – Medical Insurance Charges

Course Curriculum

Chapter 1: Simple Linear Regression

Lecture 1: Introduction to Simple Linear Regression

Lecture 2: Understanding Scatter Plot

Lecture 3: [Minitab] Plotting Scatter and Matrix Plots

Lecture 4: Correlation Coefficient

Lecture 5: [Minitab] Regression – Two Approaches in Minitab

Lecture 6: The R Value

Lecture 7: The R-Squared Value (Coefficient of Determination)

Lecture 8: Hypothesis Testing – Introduction

Lecture 9: Type I and Type II Errors

Lecture 10: The p-Value

Lecture 11: Regression Line

Lecture 12: Residuals

Lecture 13: The p-Value and VIF

Lecture 14: The S-Value, Confidence and Prediction Intervals

Lecture 15: R-Squared

Chapter 2: Multiple Regression

Lecture 1: Multiple Regression Introduction

Lecture 2: [Minitab] Multiple Regression Demonstration – Part 1

Lecture 3: Analyzing Multiple Regression Results – Part 1

Lecture 4: Analyzing Multiple Regression Results – Part 2

Lecture 5: [Minitab] Multiple Regression Demonstration Part 2

Lecture 6: Analyzing Multiple Regression Results – Part 3

Chapter 3: Nonlinear Regression

Lecture 1: Underfitting vs Overfitting

Lecture 2: Bias Variance Trade-off

Lecture 3: Polynomial Model

Lecture 4: [Minitab] Demonstration of Polynomial Models

Lecture 5: [Minitab] Comparing Models

Lecture 6: Comparing Three Models – Linear, Quadratic and Cubic

Lecture 7: Stepwise Selection and Conclusion

Chapter 4: Feature Selection

Lecture 1: Model Reduction – Introduction

Lecture 2: Cement Heat Evolved Dataset

Lecture 3: Features Selection Rules

Lecture 4: [Minitab] Best Subsets Regression Demonstration

Lecture 5: Features Selection – Stepwise

Lecture 6: [Minitab] Features Selection – Stepwise

Chapter 5: Outliers (Identifying and Adressing)

Lecture 1: Outliers in the Model

Lecture 2: Unusual X Values

Lecture 3: [Minitab] Outliers and it's Masurements – Hi(Leverage), Cooks Distance and DFITS

Chapter 6: Testing the Model

Lecture 1: Training and Testing Model – Introduction

Lecture 2: Train Test Splitting

Lecture 3: K-Fold and Leave One Out Cross Validation

Lecture 4: [Minitab] Training and Testing Demonstration

Chapter 7: Making Predictions

Lecture 1: Estimating the response based on predictors

Chapter 8: Project Work – To Review the Course Learnings

Lecture 1: About the project – Medical Insurance Charge

Lecture 2: Exploring the Dataset

Lecture 3: Regression Model – The First Attempt

Lecture 4: The Final Regression Model and the Course Conclusion

Chapter 9: Bonus Section

Lecture 1: BONUS LECTURE

Instructors

Sandeep Kumar, Quality Gurus Inc.
Experienced Quality Director • Six Sigma Coach • Consultant

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 54 votes
5 stars: 58 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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