MATH 251 Lecture 28

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Linear Regression

Given , , and you think , but know nothing (this is where statistics comes in).

Observe a finite collection of data .

Data is assumed to be "noisy" (not 100% true). We want to reconstruct

In general, we need two things:

  1. A class of functions
  2. A measure to be minimized

Linear Regression

Assume that our class is linear (i.e. ). We want the best choice for and : Least-Squares linear regression

Measure the deviation from , so we need a function that depends on and the data. Our goal is to minimize this function.

Calculate its gradient, set it to 0, and solve for and .

"this is on Wikipedia"

Quadratic Regression

Least-Squares Quadratic Regression let , so

Penalty Coefficient

Sometimes the data looks like a certain form, but could be something else. Introduce a penalty coefficient to check "fitness"


Line Integrals

Confused about Notation?


Given a curve and a vector field , the line integral represents how much of the vector field is in the direction of the curve , like Work in physics.

Written: Calculate (vector field notation) or (differential form notation)

Example: represents the unit circle going counter-clockwise, and

  1. Break into separate line regions (if necessary)
  2. Parameterize each line : , ,
  3. Determine meaning of differential:
  4. Plug in vectors into dot product:
  5. Set up integral with internal components (use chain rule):
  6. Evaluate:

Example

represents the triangle formed by (0,0), (1,0), and (1,1). Set up an integral for each line. (generally move counter-clockwise)

Parameterization for each :

  1. , ,
  2. , ,
  3. , ,


Example

Let represent a force field (like gravity). If the force is constant, then . If the force changes for each point, use the integral to get a continuous sum of the Work at each point.