^{b}that fits best into series of (x, y) points.

# Beta version#

BETA TEST VERSION OF THIS ITEM

This online calculator is currently under heavy development. It may or it may NOT work correctly.

You CAN try to use it. You CAN even get the proper results.

However, please VERIFY all results on your own, as the level of completion of this item is NOT CONFIRMED.

Feel free to send any ideas and comments !

This online calculator is currently under heavy development. It may or it may NOT work correctly.

You CAN try to use it. You CAN even get the proper results.

However, please VERIFY all results on your own, as the level of completion of this item is NOT CONFIRMED.

Feel free to send any ideas and comments !

# Calculation data - measurement points#

Format of input data | ||

x-values | ||

y-values |

# Results - approximation of your dataset#

Regression type | Approximation formula | Coefficient of determination R^{2} |

Power regression | Show source$y=x^{2}$ | 1 |

# Summary - function best fitting to your data#

Measurement points | ||

Number of points | 4 | |

Points you entered | (2, 4), (3, 9), (4, 16), (5, 25) | |

Approximation | ||

Regression type | Power regression | |

Function formula | Show source$y=x^{2}$ | |

Coefficient of determination R^{2} | 1 |

# Some facts#

- ⓘ Hint: If you're not sure what type of regression this is, let us do the hard work for you and visit another calculator: Regression types.
- Approximation of a function consists in finding a
**function formula**that**best matches**to a**set of points**e.g. obtained as measurement data. - The
**least squares method**is one of the methods for**finding such a function**. - The least squares method is the
**optimization method**. As a result we get function that the**sum of squares of deviations from the measured data is the smallest**. Mathematically, we can write it as follows:

$\sum_{i=1}^{n} \left[y_i - f(x_i)\right]^2 = min.$where:

**$(x_i, y_i)$**- coordinations of the i-th measurement point, these are**points that we know**,

**$f(x)$**- the function we are**searching for**, we want this function to best match to the measurement points,

**$n$**- number of measurement points.

- If we limit the search to
**power function**only, then we say about**power regression**or**power approximation**.

$f(x) = a \times x^{b}$**f(x)**- function that best approximates the input data in the best way,

**a,b**- unknown function parameters, which we**want to find**.

- Power approximation is an example of
**non-linear regression**i.e. estimation with function other than linear function. - Using the method of least squares we can find
**a and b parameters**of the above function, at which the sum of squares of deviations from the measured data is the smallest, so the final function**fits best to the the input data**. - If the parameter in the exponent (denoted by the symbol b in the formula above) is a natural number, then power regression becomes a special case polynomial regression.

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