The module Cubic Spline is used to fit any functional regression curve through data with one independent variable x and one dependent random variable y. Number of points ( xi, yi) is n. The regression model y = f(x) + ε is composed of p cubic curves defined on p adjacent segments
(-∞,xu,1)∪<xu,1,xu,2)∪..∪<xu,p-1,+∞) of the x-axis separated by p–1 knots. The values xu,i are knots and can be defined by the user. Analytical properties of the curve (like smoothness, curvature, continuity) and statistical properties (residual variance, prediction variance) are subject to modeling.
Cubic smoothing spline - Pdf manual
Output
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Protocol:
- Basic characteristics
- Model parameters
- Table of predicted values
- Residual squares sum
- Residual std. deviation
- Residual variance
- Mean std. deviation
- Eff. degrees of freedom
- Tables of extremes and inflexes
- No of extremes
- Table of inflexes
- Table of data and residuals
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Graphics:
- Plot of regression spline function
- Plot of regression spline function with visible knot values
- First derivative of the spline function
- Second derivative of the spline function
- Integral of the spline function
- Plot of residuals
- Q-Q plot of the residuals
- Plot of Y-prediction
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Dialog window for Cubic spline module
Example output
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Last Updated ( 19.03.2013 )
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QC-Expert 
