What you'll 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.
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
