## Multiple Regression with Minitab

### What you’ll learn

Mastering Muliple Regression – Including Linear and Polynominal Regression

Perform and interpret the results of Regression Analysis using Minitab

A practical view of the Regression modeling

### Requirements

Some basic understanding of statistical concepts

You can download 30 days trial version of Minitab for practice from their website

### Description

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 RegressionMultiple RegressionNonlinear Regression (Polynomial)Bias Variance Trade-offSelecting features using Best Subsets and Stepwise selection approachesIdentifying OutliersTraining and Test Data – Validation set approach, Leave one out cross-validation and K-Fold Validation.Predicting ResponseProject Work – Medical Insurance Charges

### Overview

Section 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

Section 2: Multiple Regression

Lecture 16 Multiple Regression Introduction

Lecture 17 [Minitab] Multiple Regression Demonstration – Part 1

Lecture 18 Analyzing Multiple Regression Results – Part 1

Lecture 19 Analyzing Multiple Regression Results – Part 2

Lecture 20 [Minitab] Multiple Regression Demonstration Part 2

Lecture 21 Analyzing Multiple Regression Results – Part 3

Section 3: Nonlinear Regression

Lecture 22 Underfitting vs Overfitting

Lecture 23 Bias Variance Trade-off

Lecture 24 Polynomial Model

Lecture 25 [Minitab] Demonstration of Polynomial Models

Lecture 26 [Minitab] Comparing Models

Lecture 27 Comparing Three Models – Linear, Quadratic and Cubic

Lecture 28 Stepwise Selection and Conclusion

Section 4: Feature Selection

Lecture 29 Model Reduction – Introduction

Lecture 30 Cement Heat Evolved Dataset

Lecture 31 Features Selection Rules

Lecture 32 [Minitab] Best Subsets Regression Demonstration

Lecture 33 Features Selection – Stepwise

Lecture 34 [Minitab] Features Selection – Stepwise

Section 5: Outliers (Identifying and Adressing)

Lecture 35 Outliers in the Model

Lecture 36 Unusual X Values

Lecture 37 [Minitab] Outliers and it’s Masurements – Hi(Leverage), Cooks Distance and DFITS

Section 6: Testing the Model

Lecture 38 Training and Testing Model – Introduction

Lecture 39 Train Test Splitting

Lecture 40 K-Fold and Leave One Out Cross Validation

Lecture 41 [Minitab] Training and Testing Demonstration

Section 7: Making Predictions

Lecture 42 Estimating the response based on predictors

Section 8: Project Work – To Review the Course Learnings

Lecture 43 About the project – Medical Insurance Charge

Lecture 44 Exploring the Dataset

Lecture 45 Regression Model – The First Attempt

Lecture 46 The Final Regression Model and the Course Conclusion

Section 9: Bonus Section

Lecture 47 BONUS LECTURE

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

#### Course Information:

Udemy | English | 4h 48m | 1.64 GB

Created by: Sandeep Kumar, Quality Gurus Inc.

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