# 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.

## 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