Statistical Thinking Background

Statistical Thinking for Industrial Problem Solving

A free online statistics course

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.

Gray gradation

Specific topics covered in this module include:


  • 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

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