Quadratic Regression Calculator
Statistical Analysis
R-squared (R²): 0
Standard Error: 0
Understanding Quadratic Regression
Quadratic regression finds the best-fitting parabola for a set of data points. It's useful for analyzing relationships that show curved patterns or have a single maximum or minimum.
Key Components
Equation Form
- y = ax² + bx + c
- a: determines opening direction and width
- b: affects axis of symmetry
- c: y-intercept
Applications
- Physics (projectile motion)
- Economics (profit optimization)
- Biology (population growth)
- Engineering (stress analysis)
- Market Analysis (price optimization)
Interpreting Results
- Parabola Shape Analysis
- Maximum/Minimum Points
- Goodness of Fit
- Prediction Accuracy
Statistical Measures
- R-squared Value
- Standard Error
- Residual Analysis
- Confidence Intervals
Frequently Asked Questions
What is R-squared in quadratic regression?
R-squared measures how well the quadratic equation fits the data, with values closer to 1 indicating better fit.
When should I use quadratic regression?
Use quadratic regression when data shows a curved pattern with one bend, or when relationships aren't linear but have a clear parabolic shape.
How accurate are the predictions?
Prediction accuracy depends on R-squared value, data quality, and how well the quadratic model fits the underlying relationship.
Analysis Disclaimer
This calculator provides estimates based on quadratic regression. For professional applications, verify results with specialized statistical software.