Transcription of CHAPTER 5 - CURVE FITTING
{{id}} {{{paragraph}}}
Example: stock market
CHAPTER 5 - CURVE FITTING
CHAPTER 5 CURVE FITTINGCURVE FITTINGP resenter: Dr. Zalilah Sharer 2018 School of Chemical and Energy EngineeringUniversiti Teknologi Malaysia23 September 2018 TOPICSL inear regression (exponential model, power equation and saturation growth rate equation)Polynomial Regression Polynomial Interpolation (Linear interpolation, Quadratic Interpolation, Newton DD)Lagrange InterpolationCurve FITTING CURVE fittingdescribes techniques to fit curves at points between the discrete values to obtain intermediate estimates. Two general approaches for CURVE FITTING :a)Least Squares Regression -to fits the shape or a)Least Squares Regression -to fits the shape or general trend by sketch a best line of the data without necessarily matching the individual points (figure , pg 426).- 2 types of FITTING :i) Linear Regressionii) Polynomial RegressionFigure shows sketches developed from same set of data by 3 ) least-squares regression -did not attempt to connect the point, but characterized the general upward trend of the data with a straight lineb) Linear interpolation -Used straight-line segments or linear interpolation to connect the points.
Curve Fitting Linear Regression is fitting a ‘best’ straight line through the points. The mathematical expression for the straight line is: y = a 0+a 1x+e Eq17.1 where, a1-slope a0 -intercept e - error, or residual, between the model and the observations Rearranging the eq. above as: e = y -a0 -a1x
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: