Please enable JavaScript.
Coggle requires JavaScript to display documents.
Correlation and Regression - Coggle Diagram
Correlation and Regression
Introduction to Correlation and Regression
Understanding relationships between two factors in a process
Importance of not inferring causation from correlation
Statistical concepts aiding Six Sigma teams
Regression Analysis
Transition from correlation to regression
Coefficient of determination (r2 value)
Understanding the linear relationship between variables
Predictive modeling with regression equations
Application in Six Sigma
Determining optimum input values for process control
Verification of relationships between inputs
Role in DMAIC phases: Measure, Analyze, Improve
Correlation
Visualizing with scatter diagrams
Types: Positive and negative correlation
Interpretation of correlation strength
Definition: Linear association between two variables
Analyzing Regression in Excel
Interpreting output: Multiple R and R Square
Creating regression worksheets
Visualizing regression lines and equations
Utilizing Excel's Data Analysis ToolPak
Purpose of Correlation and Regression
Analyzing elements of a process
Making predictions about process outcomes
Understanding processes and relationships
Correlation Coefficient
Interpretation of values
Calculation methods: Pearson’s formula and sum of squares
Definition and range (-1 to 1)
Data Requirements
Suitable data types for correlation and regression
Quantitative nature of data