Lecture_05_Polynomial_Regression - EGR 102 Introduction to...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
1 EGR 102 Lecture 5 1 EGR 102 Introduction to Engineering Modeling Curve Fitting Polynomial Regression Chapter 14.1 Figures from: “Applied Numerical Methods with MATLAB,” Steven Chapra, McGraw Hill EGR 102 Lecture 5 2 Curve Fitting ± Describes techniques to fit curves to discrete data to obtain intermediate estimates. ± There are two general approaches to curve fitting: ± Data exhibit a significant degree of scatter ± Derive a single curve that represents the general trend of the data. ± Data is very precise ± Pass a curve or a series of curves through each of the points. ± In engineering, two types of applications are encountered: ± Trend analysis ± Predicting values of dependent variable may include extrapolation beyond data points or interpolation between data points. ± Hypothesis testing ± Comparing existing mathematical model with measured data.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 EGR 102 Lecture 5 3 Variable Data Example (a) Data with significant error (b) Polynomial fit oscillating beyond the range of the data (c) Linear fit EGR 102 Lecture 5 4 ± Arithmetic mean: The sum of the individual data points ( y i ) divided by the number of points ( n ). ± Standard deviation: The most common measure of a spread for a sample.
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 12/26/2010 for the course EGR 102 taught by Professor Hinds during the Spring '09 term at Michigan State University.

Page1 / 12

Lecture_05_Polynomial_Regression - EGR 102 Introduction to...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online