Correlation and Regression

The process of solving problems and finding new opportunities often begins with asking basic questions about how one variable relates to another. For example:

What is the relationship between chemical reaction time and impurity percentage?
Which settings of temperature and catalyst concentration will result in higher yield?
Which variables are significant predictors of defective parts?

Correlation and regression analysis can be used to explore variable relationships and ultimately explain, predict and optimize outcomes.

Estimated time to complete this module: 5 to 6 hours

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https://share.vidyard.com/watch/Jozv9eQurNa2tU3W1YQhyh

Specific topics covered in this module include:

Correlation

What is Correlation?
Interpreting Correlation

Simple Linear Regression

Introduction to Regression Analysis
The Simple Linear Regression Model
The Method of Least Squares
Visualizing the Method of Least Squares
Regression Model Assumptions
Interpreting Regression Results
Fitting a Model with Curvature

Multiple Linear Regression

What is Multiple Linear Regression?
Fitting the Multiple Linear Regression Model
Interpreting Results in Explanatory Modeling
Residual Analysis and Outliers
Multiple Linear Regression with Categorical Predictors
Multiple Linear Regression with Interactions
Variable Selection
Multicollinearity

Introduction to Logistic Regression

What Is Logistic Regression?
The Simple Logistic Model
Simple Logistic Regression Example
Interpreting Logistic Regression Results
Multiple Logistic Regression
Logistic Regression with Interactions
Common Issues