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In statistics, the relation between the different variables is first represented on a graph paper. If the points after joining form a rough line, that is called linear equation.And in case that doesn’t happen, the least square fit procedure is used to find out the results for the non-linear relation.
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The square of the errors of the single equationsis summed up and minimized by the total result. This gives the least squares fit data.Once you register for the least squares fit assignment help, you will get an in-depth analysis of the subject.
Assumptions about an error:
- Existence in the response data only.
- Random errors following general distribution of 0 (zero mean), s2(constant variance).
The equation is: error ~ N (0, s2).
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Four types of least square fit methods:
- Non-linear least squares:
This method is used to fit a non-linear formation, the equation whose coefficients are not in a linear position, to the data.
- Linear least squares:
In this equation, the coefficients are linear.
- Weighted linear least squares:
Here, an addition weight (scale factor) is used in the fitting method to lessen the error estimation.
- Robust least squares:
This method is used to reduce the influence extreme factors or outliers have on the data. There are further two methods of robust least square process- Bisquare weights and Least Absolute Residuals.
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