Mô tả

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





Bạn sẽ học được gì

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

Yêu cầu

  • Some basic understanding of statistical concepts
  • You can download 30 days trial version of Minitab for practice from their website

Nội dung khoá học

9 sections

Simple Linear Regression

16 lectures
Introduction to Simple Linear Regression
06:57
Understanding Scatter Plot
07:38
[Minitab] Plotting Scatter and Matrix Plots
03:44
Correlation Coefficient
03:47
[Minitab] Regression - Two Approaches in Minitab
06:03
The R Value
04:58
The R-Squared Value (Coefficient of Determination)
04:43
Hypothesis Testing - Introduction
09:51
Type I and Type II Errors
06:36
The p-Value
06:28
Regression Line
10:12
Residuals
04:16
The p-Value and VIF
04:31
The S-Value, Confidence and Prediction Intervals
04:39
R-Squared
06:08
Quiz: Simple Linear Regression
8 questions

Multiple Regression

7 lectures
Multiple Regression Introduction
03:53
[Minitab] Multiple Regression Demonstration - Part 1
06:11
Analyzing Multiple Regression Results - Part 1
08:45
Analyzing Multiple Regression Results - Part 2
11:50
[Minitab] Multiple Regression Demonstration Part 2
08:55
Analyzing Multiple Regression Results - Part 3
05:16
Quiz: Multiple Regression
2 questions

Nonlinear Regression

8 lectures
Underfitting vs Overfitting
05:25
Bias Variance Trade-off
04:08
Polynomial Model
04:46
[Minitab] Demonstration of Polynomial Models
04:50
[Minitab] Comparing Models
04:40
Comparing Three Models - Linear, Quadratic and Cubic
06:08
Stepwise Selection and Conclusion
03:56
Quiz: Nonlinear Regression
2 questions

Feature Selection

7 lectures
Model Reduction - Introduction
03:48
Cement Heat Evolved Dataset
09:48
Features Selection Rules
03:55
[Minitab] Best Subsets Regression Demonstration
06:42
Features Selection - Stepwise
04:29
[Minitab] Features Selection - Stepwise
07:33
Quiz: Feature Selection
1 question

Outliers (Identifying and Adressing)

4 lectures
Outliers in the Model
06:26
Unusual X Values
07:14
[Minitab] Outliers and it's Masurements - Hi(Leverage), Cooks Distance and DFITS
04:31
Quiz Outliers
1 question

Testing the Model

5 lectures
Training and Testing Model - Introduction
07:57
Train Test Splitting
05:04
K-Fold and Leave One Out Cross Validation
04:36
[Minitab] Training and Testing Demonstration
07:30
Quiz: Testing the Model
1 question

Making Predictions

1 lectures
Estimating the response based on predictors
07:23

Project Work - To Review the Course Learnings

5 lectures
About the project - Medical Insurance Charge
03:10
Exploring the Dataset
07:29
Regression Model - The First Attempt
12:08
The Final Regression Model and the Course Conclusion
08:46
Quiz: Project Work
1 question

Bonus Section

1 lectures
BONUS LECTURE
00:33

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