Curve Fitting

Question 1

Describes techniques to fit curves to discrete data to obtain intermediate estimates

Answer 1

Curve fitting

Question 2

Two general approaches

Answer 2

1) Data with noise (deviant values)
2) Data known to be very precise (no / very few noise)

Question 3

Data with noise (deviant values) strategy

Derive a single curve that represents the general trend of data

Answer 3

Regression

Question 4

Data known to be very precise (no / very few noise) strategy

Fit a curve or series of curves passing through each of the points

Answer 4

Interpolation

Question 5

A process of using pattern of the data to make predictions

Answer 5

Trend analysis

Question 6

Compare a known mathematical model with measured data

Answer 6

Hypothesis testing

Question 7

Derive simpler function

Answer 7

Function simplification

Question 8

The shape with which the data is spread around the mean

Answer 8

Data distribution

Question 9

Derive an approximating function that fits the shape or generated trend of the data without necessarily matching individual points

Answer 9

Regression

Question 10

Fitting a straight line to a set of paired observations

Answer 10

Linear Regression

Question 11

A method of constructing new data points from a discrete set of known points

Answer 11

Interpolation

Question 12

For n + 1 data points, there is

Answer 12

exactly one unique polynomial of order n that passes through all points

Question 13

First order polynomial connecting two points

Answer 13

Linear

Question 14

Second-order polynomial connecting three points

Answer 14

Quadratic or Parabolic

Question 15

Various mathematical formats (Polynomial functions)

Answer 15

Fitting polynomial to data points
Newton’s Divided Difference (NDD)
Lagrange Interpolating Polynomials
Neville’s Method
Spline Curves