Fundamentals of Correlation and Regression

What you'll learn
Understand the fundamentals of correlation and regression and explain how statistical relationships are identified and interpreted in real-world data.
Create and interpret scatter plots, correlation coefficients, regression equations, and regression models using Microsoft Excel.
Evaluate the strength, direction, and statistical significance of relationships between variables using tools such as p-values, hypothesis tests, and R-squared.
Build and interpret simple and multiple linear regression models for prediction, forecasting, and process analysis applications.
Identify common regression issues such as outliers, extrapolation, residual patterns, and misleading correlations.
Apply correlation and regression tools to practical engineering, manufacturing, quality, business, and operational decision-making problems.
Use Microsoft Excel functions and tools such as CORREL, LINEST, trendlines, and the Data Analysis ToolPak to perform statistical analysis.
Develop a foundation for more advanced analytical topics such as ANOVA, predictive analytics, machine learning, and logistic regression.
Requirements
A basic understanding of introductory statistics is helpful, but advanced statistical knowledge is not required.
Students should be comfortable using Microsoft Excel at a basic level, including entering data, creating charts, and using simple formulas.
No prior experience with correlation, regression, data analytics, or statistical modeling is required.
A copy of Microsoft Excel is recommended so students can follow along with the hands-on examples and downloadable templates.
Students should be willing to work through practical examples and apply statistical thinking to real-world problems and decision making.
Description
Learn how to identify relationships in data, build predictive models, and make better decisions using practical statistical tools and Microsoft Excel.
Correlation and regression are among the most important analytical tools used in engineering, manufacturing, quality, operations, business analytics, supply chain management, finance, and data-driven decision making. This course is designed to make these concepts practical, approachable, and immediately useful.
In thisFundamentals of Correlation and Regression, you will learn how to
- Interpret correlation and regression results
- Build and analyze regression models
- Evaluate statistical significance using p-values and hypothesis tests
- Understand R-squared, residuals, outliers, and prediction error
- Create simple and multiple regression models
- Use Microsoft Excel to perform real-world statistical analysis
- Apply regression tools to forecasting, prediction, process analysis, and operational improvement
The course combines
- Voice-over PowerPoint instruction
- Step-by-step Microsoft Excel demonstrations
- Practical examples from engineering, manufacturing, quality, and business applications
- able Excel templates and example files
This course is designed for professionals and students who want practical analytical skills without unnecessary theory and mathematical complexity.
Included in This Course
- able Excel templates and example files
- Glossary of statistical and regression terminology
- Lifetime access to all course materials
- Future course updates
- Practical, career-focused instruction
Who this course is for
Quality Engineers, Manufacturing Engineers, Process Engineers, Reliability Engineers, Industrial Engineers
Continuous Improvement Professionals, Lean Six Sigma Green Belts, Lean Six Sigma Black Belts, Operational Excellence Managers, Quality Managers
Production Supervisors, Manufacturing Managers, Operations Managers, Plant Managers, Engineering Managers
Supply Chain Analysts, Business Analysts, Data Analysts, Operations Analysts, Financial Analysts
Test Engineers, Validation Engineers, Product Engineers, Design Engineers, Mechanical Engineers
Calibration Technicians, Quality Technicians, Laboratory Technicians, Metrology Technicians, Engineering Technicians
Procurement Professionals, Supplier Quality Engineers, Supplier Development Engineers, Inventory Analysts, Logistics Analysts
MBA Students, Engineering Students, Operations Professionals, Project Managers, Technical Managers